What are the main differences between Fini AI and Progress Agentic RAG?

Fini AI excels in Industry-leading 97-98% accuracy claim backed by customer testimonials and True action-taking capabilities - executes refunds, KYC, account updates beyond Q&A, scoring 91/100 in user ratings with 97% accuracy. Progress Agentic RAG leads in Proprietary REMi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors and Open-source NucliaDB transparency (710+ GitHub stars) with managed infrastructure removes operational burden while maintaining technical visibility with 82/100 overall rating and N/A% accuracy. Key technical difference: Fini AI focuses on Sophie AI Agent with 5-layer supervised execution while Progress Agentic RAG emphasizes REMi evaluation model v2 (Llama 3.2-3B) for continuous quality scoring: answer relevance, context relevance, groundedness, correctness (0-5 scale). Pricing differs by $700/month.

Which is better for enterprises: Fini AI or Progress Agentic RAG?

Fini AI offers SOC 2 compliance for enterprise security. HIPAA compliance varies - contact vendors for details. For scalability, both offer cloud-based deployment options. Fini AI leads with 91/100 enterprise readiness score (89/100 enterprise features).

How much does Fini AI cost vs Progress Agentic RAG?

Fini AI starts at custom pricing/monthly (usage-based model), while Progress Agentic RAG begins at $700/monthly (paid model). Fini AI offers a free tier. This represents a significant price difference of $700/month. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Progress Agentic RAG to Fini AI?

Migration from Progress Agentic RAG to Fini AI typically requires API-based data export/import, which Fini AI provides. Fini AI provides comprehensive API access fair documentation for the migration process. Estimated migration time: 1-2 days for cloud setup. Contact Fini AI support for migration assistance and best practices.

What integrations do Fini AI and Progress Agentic RAG support?

Fini AI integrates with Zendesk (Native marketplace app), Intercom (Native with Fin compatibility), Salesforce Service Cloud, Front, Gorgias (19 integrations total). Progress Agentic RAG supports Python SDK: pip install nuclia (Python 3.8+, ~21K weekly downloads), JavaScript/TypeScript SDK: @nuclia/core (React, Next.js, Angular, Vue.js, Svelte), CMS plugins: WordPress, Strapi, Workflow: Pipedream official app, Zapier API-compatible, Chrome extension for web page indexing (7 integrations available). Both offer REST APIs for custom integrations. Fini AI offers 171% more integrations, with webhook support for real-time data sync.

What are the main differences between Fini AI and Progress Agentic RAG?

Fini AI offers strong capabilities, scoring 0/100 in user ratings. Progress Agentic RAG provides unique strengths with 0/100 overall rating. Key technical difference: Fini AI focuses on advanced features while Progress Agentic RAG emphasizes specialized capabilities.

Which is better for enterprises: Fini AI or Progress Agentic RAG?

Both platforms provide enterprise-grade security. HIPAA compliance varies - contact vendors for details. For scalability, both offer cloud-based deployment options. Progress Agentic RAG leads with strong/100 enterprise readiness score.

How much does Fini AI cost vs Progress Agentic RAG?

Fini AI starts at custom pricing/month (subscription model), while Progress Agentic RAG begins at contact sales/month (subscription model). Both require paid plans. Pricing is relatively comparable. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Fini AI to Progress Agentic RAG?

Migration from Fini AI to Progress Agentic RAG typically requires manual data transfer since API access may be limited. Progress Agentic RAG offers standard documentation for the migration process. Estimated migration time: 1-2 days for cloud setup. Contact Progress Agentic RAG support for migration assistance and best practices.

What integrations do Fini AI and Progress Agentic RAG support?

Fini AI integrates with (multiple platforms). Progress Agentic RAG supports (various tools). API availability varies by plan. Integration coverage is comparable.

Fini AI vs Progress Agentic RAG

Make an informed decision with our comprehensive comparison. Discover which RAG solution perfectly fits your needs.

More about Progress Agentic RAG More about Fini AI Get Started Today

Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

In this comprehensive guide, we compare Fini AI and Progress Agentic RAG across various parameters including features, pricing, performance, and customer support to help you make the best decision for your business needs.

Overview

When choosing between Fini AI and Progress Agentic RAG, understanding their unique strengths and architectural differences is crucial for making an informed decision. Both platforms serve the RAG (Retrieval-Augmented Generation) space but cater to different use cases and organizational needs.

Quick Decision Guide

Choose Fini AI if: you value industry-leading 97-98% accuracy claim backed by customer testimonials
Choose Progress Agentic RAG if: you value proprietary remi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors

About Fini AI

Fini AI is ragless ai agent for customer support automation. Fini AI is a next-generation customer support platform built on proprietary RAGless architecture, claiming 97-98% accuracy. Founded by ex-Uber engineers and backed by Y Combinator, Fini specializes in action-taking AI agents that execute refunds, update accounts, and verify identities—going beyond traditional RAG document retrieval. Founded in 2022, headquartered in Amsterdam, Netherlands, the platform has established itself as a reliable solution in the RAG space.

Overall Rating

91/100

Starting Price

Custom

About Progress Agentic RAG

Progress Agentic RAG Landing Page Screenshot

Progress Agentic RAG is enterprise application development and deployment platform. Enterprise RAG-as-a-Service platform launched Sept 2025 following Progress Software's acquisition of Barcelona-based Nuclia. Combines SOC2/ISO 27001 security with proprietary REMi evaluation model for continuous answer quality monitoring. Built on open-source NucliaDB (710+ GitHub stars) with Python/JavaScript SDKs. Starting at $700/month. Founded in 2019 (Nuclia), acquired 2025, headquartered in Barcelona, Spain (Nuclia) / Bedford, MA, USA (Progress), the platform has established itself as a reliable solution in the RAG space.

Overall Rating

82/100

Starting Price

$700/mo

Key Differences at a Glance

In terms of user ratings, Fini AI in overall satisfaction. From a cost perspective, Fini AI starts at a lower price point. The platforms also differ in their primary focus: AI Agent versus Enterprise Software. These differences make each platform better suited for specific use cases and organizational requirements.

⚠️ What This Comparison Covers

We'll analyze features, pricing, performance benchmarks, security compliance, integration capabilities, and real-world use cases to help you determine which platform best fits your organization's needs. All data is independently verified from official documentation and third-party review platforms.

Detailed Feature Comparison

Fini AI	Progress Agentic RAG	CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Supports PDF, Word/Docs, plain text, JSON, YAML, and CSV files Full website crawling for web links Note: YouTube transcript ingestion NOT supported - LLMs "not great at interpreting images or videos directly" Cloud integrations: Native connections to Google Drive, Notion, Confluence, and Guru Zendesk and Intercom serve as both knowledge sources (historical tickets) and deployment channels Note: Dropbox integration not available Chat2KB feature (Growth/Enterprise): Auto-extracts Q&A pairs from conversations, emails, tickets Real-time knowledge refresh - updated content used immediately Intelligent conflict resolution automatically removes contradictory information Scaling: Starter 50 docs → Growth 1,000 docs → Enterprise unlimited	60+ Document Formats: PDF, Word (.docx), Excel, PowerPoint, plain text, email formats with automatic parsing Multimedia Processing: Automatic speech-to-text (MP3, WAV, AIFF), video transcript extraction (MP4, etc.), OCR for scanned documents/images Cloud Connectors: SharePoint, Confluence, OneDrive, Google Drive, Amazon S3 with direct integration Sync Agent Desktop App: 60-minute automatic sync with content hashing to prevent redundant reindexing Manual Upload Interface: Files, folders, web links, sitemaps, Q&A pairs via dashboard Fast Deployment: 2-hour initial ingestion, 48-hour full deployment timeline CRITICAL GAPS: NO Dropbox integration, NO Notion integration, NO explicit YouTube transcript extraction documented Architecture Focus: Comprehensive knowledge retrieval vs lead conversion focus (unlike Drift)	Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API. Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs. Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text. View Transcription Guide Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier. See Zapier Connectors Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
Integrations & Channels
20+ native helpdesk integrations (no Zapier dependency) Zendesk: Native marketplace app with full ticket management, auto-tagging, email/chat/social Intercom: Native with Fin compatibility, works within ticketing backend Salesforce Service Cloud: CRM sync, case management Front: AI auto-replies, trains on conversation history Gorgias: Email-to-chat automation, internal note generation HubSpot: CRM integration, customer context sync Also: LiveChat, Freshdesk, Help Scout, Kustomer, Gladly, Re:amaze Omnichannel: Slack, Discord, Microsoft Teams for internal/community support WhatsApp, Messenger, Instagram via Zendesk/Intercom routes (not native) Note: Telegram not explicitly supported Website embedding: Fini Widget (chat bubble), Fini Search Bar, Fini Standalone (full-page) Chrome Extension: "Answer with Fini" for agent productivity across Gmail, Intercom, Zendesk Note: Zapier integration absent - focuses on native integrations Webhooks marked "Coming Soon" (Zendesk-specific available now)	Python SDK: pip install nuclia (Python 3.8+, ~21,000 weekly downloads) JavaScript/TypeScript SDK: @nuclia/core on NPM (React, Next.js, Angular, Vue.js, Svelte) CMS Plugins: WordPress, Strapi integrations Workflow Automation: Pipedream official app, Zapier API-compatible Chrome Extension: Web page indexing capability Progress Ecosystem: OpenEdge database connector, Sitefinity CMS integration ('first Generative CMS') CRITICAL LIMITATION: NO native Slack, WhatsApp, Telegram, or Microsoft Teams integrations Platform Design: RAG backend + embeddable widget, NOT omnichannel conversational AI platform Custom Development Required: Messaging platform integrations need API-based custom builds	Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app. Offers ready-made hooks for Slack, Zendesk, Confluence, YouTube, Sharepoint, 100+ more. Explore API Integrations Connects with 5,000+ apps via Zapier and webhooks to automate your workflows. Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases. Hosted CustomGPT.ai offers hosted MCP Server with support for Claude Web, Claude Desktop, Cursor, ChatGPT, Windsurf, Trae, etc. Read more here. Supports OpenAI API Endpoint compatibility. Read more here.
Core Chatbot Features
Sophie AI Agent: 5-layer supervised execution framework Layer 1 - Safety Guardrails: 40+ filters, PII masking (SSN, credit cards, passports), brand tone compliance Layer 2 - LLM Supervisor: Core orchestration brain that determines resolution paths Layer 3 - Skill Modules: Deterministic modules for Search, Write, Follow Process, Take Action Layer 4 - Live Feedback: Auto-validates outputs, detects errors, learns from corrections Layer 5 - Traceability: Full audit trail of decisions and reasoning 100+ language support with locale-based routing and real-time translation Human handoff preserves full conversation context Configurable escalation triggers: keywords, sentiment analysis, topic-based rules, confidence thresholds Conversation history with sentiment tracking and export (CSV, JSON) AI Categorization auto-tags conversations by topic with intent classification	AI Search & Generative Answers: Semantic search and Q&A across knowledge bases with trusted, source-linked answers Multi-Turn Conversations: Context-aware dialogue with conversation history maintained for follow-up questions Source Citations: Every answer includes citations linking to source documents for verification and transparency Auto-Summarization: Automatic summarization of long documents for quick understanding Entity Recognition: AI classification and entity extraction enriching corpus for better Q&A Answer-Only Mode: Widget configuration for concise answers vs detailed responses based on use case Multilingual Support: Nuclia multilingual embedding model handles multiple languages out-of-box MISSING FEATURES: NO lead capture, NO human handoff/escalation workflows, NO chat history export for users	Reduces hallucinations by grounding replies in your data and adding source citations for transparency. Benchmark Details Handles multi-turn, context-aware chats with persistent history and solid conversation management. Speaks 90+ languages, making global rollouts straightforward. Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
Customization & Branding
GUI-based chat widget editor (full CSS access not documented) Options: Logo upload, brand color selection, title/description customization Welcome messages, pre-defined FAQ questions, reference link visibility toggles Streaming response toggles White-labeling: Custom domain via CNAME, full logo replacement, agent identity renaming 100+ tone options: Friendly, Professional, TaxAssistant, Finance advisor, Casual, Super polite Domain restrictions: Specific domain lock, wildcard (.domain.com), or unrestricted Flows (Mini Specialized Agents):* No-code specialized workflows for specific tasks User context capture from backend systems Dynamic routing based on user category (VIP, first-time, veteran) Metadata-driven personalization: plan type, churn risk, subscription tier, purchase history	Prompt Lab: Test LLMs side-by-side using actual customer data with real-time comparison 30+ RAG Parameters: Custom chunking strategies, context size configuration, hybrid search weighting Retrieval Strategy Customization: Agents autonomously select optimal approaches per query Widget Customization: Visual editor for suggestions, filters, metadata, thumbnails, answer modes Advanced CSS Styling: Shadow DOM with cssPath attribute for deep customization White-Labeling Support: Full OEM deployments via API-first architecture Role-Based Access Control: Account-level (Owners, Members), Knowledge Box-level (Manager, Writer, Reader) with cascading permissions SSO Integration: Enterprise identity provider connectivity	Fully white-labels the widget—colors, logos, icons, CSS, everything can match your brand. White-label Options Provides a no-code dashboard to set welcome messages, bot names, and visual themes. Lets you shape the AI’s persona and tone using pre-prompts and system instructions. Uses domain allowlisting to ensure the chatbot appears only on approved sites.
L L M Model Options
Starter (Free): GPT-4o mini only Growth: GPT-4o mini + Claude (version unspecified) Enterprise: GPT-4o + Multi-layer models Multi-layer model architecture (Enterprise): Automatic routing to best-suited LLM per query part Complex queries decomposed into sub-queries with specialized agents per part Maximizes accuracy while controlling costs through intelligent routing Note: No user-controlled runtime model switching - plan-based selection only RAGless architecture: Query-writing AI, not traditional vector search "No embeddings, no hallucinations" - precise source attribution Bypasses retrieval at inference time for deterministic results	Anthropic: Claude 3.7, Claude 3.5 Sonnet v2 OpenAI: ChatGPT 4o, 4o mini Google: Gemini Flash 2.5, Palm2 Meta: Llama 3.2 Microsoft/Azure: Mistral Large 2 Cohere: Command-R suite Nuclia Private GenAI: 100% data isolation for maximum security Model Switching: Change providers without architectural changes via Prompt Lab Embedding Flexibility: Configurable per Knowledge Box (Nuclia multilingual default + OpenAI embeddings) Side-by-Side Testing: Compare responses across models using actual data in Prompt Lab	Taps into top models—OpenAI’s GPT-5.1 series, GPT-4 series, and even Anthropic’s Claude for enterprise needs (4.5 opus and sonnet, etc ). Automatically balances cost and performance by picking the right model for each request. Model Selection Details Uses proprietary prompt engineering and retrieval tweaks to return high-quality, citation-backed answers. Handles all model management behind the scenes—no extra API keys or fine-tuning steps for you.
Developer Experience ( A P I & S D Ks)
Base URL: https://api-prod.usefini.com Authentication: Bearer Token via API key (generated per bot in Dashboard) Current Version: v2 (no documented versioning policy) Core Endpoints: /v2/bots/ask-question (Q&A), /v2/bots/links/* (knowledge management) Store Feedback, Get Chat History, Knowledge Items CRUD Supports: messageHistory, instruction, stream, temperature, user_attributes, functions (JSON Schema) Note: NO official SDKs for Python, JavaScript, or any language Documentation provides basic Python (requests) and Node.js examples only Documentation quality: - Completeness: 3/5 (covers main endpoints, lacks depth) - Code examples: 4/5 (good Python/Node.js examples) - Error handling: 2/5 (no error codes documented) - Rate limits: 1/5 (not documented) Paramount: Open-source tool (github.com/ask-fini/paramount) for agent accuracy measurement	Open-Source Foundation: NucliaDB (710+ GitHub stars, AGPLv3 license, Python/Rust) provides transparency into core retrieval mechanisms Python SDK: pip install nuclia (Python 3.8+, ~21,000 weekly downloads) - full API coverage JavaScript/TypeScript SDK: @nuclia/core (React, Next.js, Angular, Vue.js, Svelte support) REST API: Regional endpoints https://{region}.rag.progress.cloud/api/v1/ with comprehensive documentation Key Endpoints: /ask (generative answers), /find (semantic search), /upload (ingestion), /remi (quality evaluation) Dual Documentation: docs.rag.progress.cloud (primary) + legacy docs.nuclia.dev (fragmentation concern) RAG Cookbook: Downloadable comprehensive guide for developers Code Example Simplicity: Upload and search in just a few Python lines with intuitive SDK design API-First Design: Complete programmatic control over all platform capabilities	Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat. API Documentation Offers open-source SDKs—like the Python `customgpt-client`—plus Postman collections to speed integration. Open-Source SDK Backs you up with cookbooks, code samples, and step-by-step guides for every skill level.
Performance & Accuracy
97-98% accuracy claim across marketing materials and customer testimonials Customer results: - Column Tax: "Sophie's accurate in over 97% of cases, solves 85%+ of queries" - Qogita: "Maggie's accurate in over 90% of cases" - Qogita case study: 88% ticket resolution, 121% SLA improvement - Column Tax: 94% accuracy, 98% queries resolved Hallucination prevention via 6 mechanisms: 1. RAGless architecture eliminates "black box retrieval" 2. LLM filtering removes irrelevant/outdated knowledge pre-response 3. Confidence-based gating escalates to humans when uncertain 4. Every answer "LLM-reviewed—not just LLM-generated" 5. Guardrails layer provides proactive safety checks 6. Deterministic Skill Modules ensure business logic consistency Accuracy measurement tools: - Sophia AI Evaluator (Growth/Enterprise): Auto-evaluates correctness, tone, completeness - Paramount: Open-source Python tool for tracking accuracy improvements - CXACT Benchmarking Suite: Proprietary framework (whitepaper) General claim: 80% of support tickets resolved end-to-end without human intervention	Benchmark Leader: Nuclia with OpenAI embeddings achieved highest scores vs Vectara on Docmatix 1.4k dataset across answer relevance, context relevance, correctness 100M Vectors: Fully ingested and optimized in ~20 minutes with sufficient worker allocation REMi v2 Speed: 30x faster inference than original Mistral-based implementation (Llama 3.2-3B based) Four-Index Hybrid Search: Document Index (property filtering), Full Text (keyword/fuzzy), Vector/Chunk (semantic), Knowledge Graph (entity relationships) Dynamic Sharding: Automatic shard creation as vector counts grow with index node replication for fault tolerance Fast Deployment: 2-hour initial ingestion, 48-hour full deployment timeline ACID Compliance: TiKV key-value store (Tier 2) manages resource metadata with transaction guarantees Three-Tier Storage: Tier 3 (S3/GCS blobs), Tier 2 (TiKV metadata), Tier 1 (sharded indexes)	Delivers sub-second replies with an optimized pipeline—efficient vector search, smart chunking, and caching. Independent tests rate median answer accuracy at 5/5—outpacing many alternatives. Benchmark Results Always cites sources so users can verify facts on the spot. Maintains speed and accuracy even for massive knowledge bases with tens of millions of words.
Customization & Flexibility ( Behavior & Knowledge)
Guidelines system: Define tone, preferred phrases, forbidden terminology, formatting rules Response length options: Short, Medium, Long Welcome messages and starter questions customizable Bot duplication for creating similar agents quickly Multiple bots per tier: Starter 2 bots → Growth unlimited → Enterprise unlimited Real-time knowledge updates - content used immediately after ingestion Chat2KB auto-learning eliminates duplicate responses with MECE classification Flows enable specialized workflows per customer segment or task type User context from backend systems enables dynamic personalization	30+ RAG Optimization Parameters: Fine-grained control over retrieval behavior Custom Chunking Strategies: Configurable text segmentation for optimal context windows Context Size Configuration: Adjust context sent to LLMs based on use case Hybrid Search Weighting: Balance keyword vs semantic search relevance Retrieval Agent Autonomy: Automatically select optimal strategies per query characteristics Embedding Model Flexibility: Switch per Knowledge Box (Nuclia multilingual + OpenAI options) Prompt Lab Experimentation: Test configurations with actual data before production deployment LLM Provider Switching: Change models without architectural changes (7 providers supported)	Lets you add, remove, or tweak content on the fly—automatic re-indexing keeps everything current. Shapes agent behavior through system prompts and sample Q&A, ensuring a consistent voice and focus. Learn How to Update Sources Supports multiple agents per account, so different teams can have their own bots. Balances hands-on control with smart defaults—no deep ML expertise required to get tailored behavior.
Pricing & Scalability
Note: Pricing NOT publicly disclosed - requires sales contact Starter (Free): GPT-4o mini, ~50 questions/month, ~50 docs, ~5 users, 2 bots, SSO only Growth: Estimated $999/mo (3rd party) - GPT-4o mini/Claude, 1K docs, unlimited users Growth includes: SOC 2, GDPR, ISO 27001, RBAC, Chat2KB, Sophia AI Evaluator Enterprise: Custom pricing - GPT-4o, Multi-layer models, unlimited docs Enterprise adds: Dedicated AI instance, AI Actions, full compliance, white-glove onboarding Cost model: Cost-per-resolution rather than per-seat pricing Zero-Pay Guarantee: Only pay if >80% accuracy thresholds met Note: Third-party mentions: "$0.10/interaction" (SaaSworthy) - unverified Support tiers: White-glove onboarding, 60-day implementation program Weekly alignment calls during implementation Enterprise: Dedicated AI engineers, customer success managers, 24/7 Slack channels	Fly Tier: $700/month - 10GB/15K resources, 750MB max file, 1 Knowledge Box, cloud only, 10K tokens/month Growth Tier: $1,750/month - 50GB/80K resources, 1.5GB max file, 2 Knowledge Boxes, Prompt Lab, 10K tokens/month Enterprise Tier: Custom pricing - Unlimited data/file size, 11 Knowledge Boxes, hybrid/on-prem deployment, 10K tokens/month Token Consumption: $0.008/token beyond 10K/month included across all tiers 14-Day Free Trial: Available without disclosed credit card requirement AWS Marketplace: Simplifies enterprise procurement with existing cloud spend commitments Competitive Entry Point: $700/month undercuts enterprise alternatives (Drift $30K+/year, Yellow.ai similar) Scaling Consideration: Token-based consumption pricing requires careful usage forecasting for budget predictability	Runs on straightforward subscriptions: Standard (~$99/mo), Premium (~$449/mo), and customizable Enterprise plans. Gives generous limits—Standard covers up to 60 million words per bot, Premium up to 300 million—all at flat monthly rates. View Pricing Handles scaling for you: the managed cloud infra auto-scales with demand, keeping things fast and available.
Security & Privacy
Confirmed certifications: - SOC 2 Type II: Certified (zero audit findings per Sprinto case study) - ISO 27001: Certified - ISO 42001: Certified (AI governance standard - rare achievement) - GDPR: Compliant with full data subject rights, EU data residency option Note: HIPAA status conflicting: Marketing claims compliance, but case study says "next up" PCI DSS: Claimed but not on official pricing page security section Data privacy guarantees: - "We do not train on your data" policy with formal DPA with OpenAI - PII Shield Layer: Auto-masks SSN, passport, driver's license, taxpayer ID, credit cards - AES-256 encryption at rest, TLS 1.3 in transit - EU and US data residency options - Dedicated AI instance option (Enterprise only) Access controls: RBAC (Growth/Enterprise), SSO (all tiers), audit logging Note: IP whitelisting not documented	SOC2 Type 2 Certified: Annual audits for enterprise security assurance ISO 27001 Certified: Annually audited information security management GDPR Compliant: Built-in PII anonymization automatically detects and removes personal data Encryption: AES-256 at rest, TLS in transit for comprehensive data protection AI Risk Classification: Low to minimal AI risk category with policy-as-code guardrails Human-in-the-Loop: Validation options for critical workflows Tenant Isolation: Customer data separation ensures multi-tenant security Audit Logs: Standard across all pricing tiers for compliance tracking API Key Management: Temporal keys and rotation for security hygiene CRITICAL: CRITICAL LIMITATION: NO HIPAA certification documented - healthcare organizations processing PHI must contact sales for compliance clarification Data Governance: Enterprise tier supports complete on-premise deployment for 100% data control	Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption. Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private. Security Certifications Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
Observability & Monitoring
Fini 2.0 Observability (January 2025 release): - AI resolution rate and fallback frequency - Message quality and confidence scores per response - CSAT trends over time - Agent productivity metrics (resolution time, escalation frequency) - Category-level performance breakdowns - Step-level drop-off analysis Chat History dashboard (February 2025): - Centralized view: source, question, answer, thread, categories, ticket ID, knowledge source - Filtering by channel, intent, escalation status, resolution rate, KB tags - Keyword/phrase search across historical conversations - CSV and JSON export for Looker, Tableau - Real-time updates as conversations occur AI Categorization: Auto-tags by topic (returns, login, pricing, shipping) Knowledge gap analysis: Identifies unanswerable questions with automated content improvement suggestions Bulk-flagging of problematic conversations	REMi Real-Time Dashboard: Answer relevance, context relevance, groundedness, correctness (0-5 scale) 7-Day Rolling Averages: Performance evolution graphs spanning 24 hours to 30 days Health Displays: Quality metrics shown in real-time for immediate visibility Four Quality Dimensions: Answer Relevance (query alignment), Context Relevance (passage quality), Groundedness (source derivation), Answer Correctness (ground truth alignment) REMi v2 Performance: 30x faster inference (Llama 3.2-3B) vs original Mistral implementation Benchmark Validation: Tested against Vectara on Docmatix 1.4k dataset with highest scores Audit Logs: Standard across all tiers for compliance and security tracking MISSING FEATURE: Proactive alerting not documented (monitoring exists, automated alerts unclear)	Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status. Lets you export logs and metrics via API to plug into third-party monitoring or BI tools. Analytics API Provides detailed insights for troubleshooting and ongoing optimization.
Support & Ecosystem
Founding team: Ex-Uber engineers (CEO led 4M+ interactions/month at Uber) Backed by: Y Combinator Summer 2022 ($125K seed), Matrix Partners Angel investors from Uber, Intercom, Softbank, McKinsey, Twitter Company metrics: ~$2.5M annual revenue, 14 employees, 500K+ tickets/month Customers: HackerRank, Qogita, Column Tax, Atlas, TrainingPeaks, Bitdefender, Duolingo, Meesho Implementation program: 60-day structured program (Discovery → Deployment → Optimization → Production) White-glove onboarding with dedicated implementation managers Enterprise: Dedicated AI engineers and customer success managers Dedicated Slack channels for 24/7 support Product roadmap: Upcoming SDKs, multi-agent systems with collaboration/self-repair	Dual Documentation Portals: docs.rag.progress.cloud (primary) + legacy docs.nuclia.dev (fragmentation concern) RAG Cookbook: Comprehensive downloadable guide for developers SDK Ecosystem: Python (~21K weekly downloads) + JavaScript/TypeScript with active developer usage 14-Day Free Trial: Hands-on evaluation without credit card requirement Progress Enterprise Support: Backed by 2,000+ employee parent company infrastructure AWS Marketplace: Available November 2025 for streamlined enterprise procurement Open-Source Community: NucliaDB 710+ GitHub stars with AGPLv3 license transparency API-First Support: Comprehensive REST API documentation with regional endpoints	Supplies rich docs, tutorials, cookbooks, and FAQs to get you started fast. Developer Docs Offers quick email and in-app chat support—Premium and Enterprise plans add dedicated managers and faster SLAs. Enterprise Solutions Benefits from an active user community plus integrations through Zapier and GitHub resources.
Additional Considerations
RAGless positioning: Fini criticizes RAG as "just smarter search engines" Claims RAG "fails in mission-critical customer support" and "will become obsolete" Action-taking vs. information-only: Key differentiator from traditional chatbots "It's the difference between 'You can find details here' and 'Done! I've processed that refund'" Target customer: Enterprise B2C with high support volume (fintech, e-commerce, healthcare) Less suitable for general-purpose document Q&A or content generation Competitive target: Positions against Intercom Fin with "agentic" narrative Claims 95%+ accuracy vs. Intercom's ~80% Platform agnostic: Works with any helpdesk vs. vendor lock-in	Recent Acquisition (June 2025): Progress Software acquired Nuclia for $50M - platform transitioning under new ownership with potential strategic direction changes Genuine No-Code + Developer Appeal: Dual-track value proposition - non-technical teams use dashboard, developers leverage API/SDKs for custom builds REMi Quality Differentiator: Proprietary continuous evaluation model (30x faster in v2) addresses hallucination problem absent from most RAG competitors Open-Source Trust Factor: NucliaDB (710+ GitHub stars, AGPLv3) provides code transparency vs black-box platforms - security audits possible Multimodal Strength: OCR for images, speech-to-text for audio/video creates comprehensive searchable corpus beyond text-only competitors Enterprise RAG Focus: Platform optimized for knowledge retrieval and semantic search - not conversational marketing/sales engagement like Drift/Yellow.ai Progress Ecosystem Integration: OpenEdge database connector, Sitefinity CMS integration provides distribution channels unavailable to standalone platforms Documentation Fragmentation: Dual portals (docs.rag.progress.cloud + legacy docs.nuclia.dev) during transition may cause developer confusion Competitive Pricing Entry: $700/month Fly tier undercuts enterprise RAG alternatives while providing genuine capabilities vs limited free tiers Best For: Organizations wanting model flexibility (7 providers), multimodal indexing, open-source transparency, and developer API access without managing infrastructure	Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required. Gets you to value quickly: launch a functional AI assistant in minutes. Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech. Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
No- Code Interface & Usability
Time to go live: - "2 minutes" initial setup (provide links to knowledge base) - "Day 1 Ready-to-Use" confirmed - Less than 1 week full integration (G2 review verified) - Enterprise: 1-2 weeks with no-code dashboard No-code deployment options: 1. Fini Widget (chat bubble - JavaScript snippet) 2. Fini Search Bar (embeddable knowledge search) 3. Fini Standalone (full-page interface) 4. Native helpdesk installations (one-click for Zendesk, Intercom) 5. Chrome Extension for agent productivity Admin dashboard structure: - Home Screen: Central hub for AI agent creation and deployment tracking - Knowledge Hub: External sync (Notion, Confluence, Drive), knowledge items - Prompt Configurator: Escalation guidelines, incident instructions, categorization, guardrails - All configurable without code Pre-built templates: E-commerce, fintech, SaaS onboarding workflows	Target Users: Non-technical teams (marketing, HR, legal, customer support) with zero coding required Visual Dashboard: Create Knowledge Box, upload documents, deploy search widget in single session Point-and-Click Widget Editor: Configure suggestions, filters, metadata, thumbnails, answer modes visually Pre-Built Ingestion Agents (Beta): Automated workflows for labeling, summarization, graph extraction, Q&A generation, content safety Prompt Lab: Visual interface for side-by-side LLM testing with actual data Role-Based Access Control: Visual permission management separating Account and Knowledge Box concerns Rapid Deployment: Progress explicitly markets minutes-to-production capability for business users Shadow DOM Architecture: Advanced users can apply CSS styling via cssPath attribute for customization	Offers a wizard-style web dashboard so non-devs can upload content, brand the widget, and monitor performance. Supports drag-and-drop uploads, visual theme editing, and in-browser chatbot testing. User Experience Review Uses role-based access so business users and devs can collaborate smoothly.
Competitive Positioning
Market position: Agentic AI platform specifically designed for customer support automation with Sophie's 5-layer supervised execution framework and RAGless architecture claiming 97-98% accuracy Target customers: Enterprise B2C companies with high support volumes (fintech, e-commerce, healthcare), helpdesk teams using Zendesk/Intercom/Salesforce Service Cloud, and organizations needing action-taking AI beyond simple Q&A Key competitors: Intercom Fin, Zendesk Answer Bot, Ada, Ultimate.ai, and traditional RAG chatbots (positions against Intercom with "agentic" differentiation) Competitive advantages: 97-98% accuracy vs. ~80% competitors, 20+ native helpdesk integrations without Zapier dependency, RAGless architecture eliminating "black box retrieval," Sophie's 5-layer supervised execution with PII masking, 100+ language support, AI Actions for autonomous CRM/Stripe/Shopify updates, Zero-Pay Guarantee (only pay if >80% accuracy), and Y Combinator backing with ex-Uber engineers Pricing advantage: Pricing not publicly disclosed (estimated ~$999/month Growth tier); cost-per-resolution model vs. per-seat pricing may benefit high-volume teams; 80% ticket resolution claim reduces support costs significantly; best value for enterprises prioritizing accuracy over affordability Use case fit: Ideal for enterprise B2C support teams needing action-taking AI (refunds, account updates, CRM sync) beyond information retrieval, organizations using Zendesk/Intercom/Salesforce requiring 20+ native integrations, and companies prioritizing 97-98% accuracy with ISO 42001 certification for regulated industries (fintech, healthcare)	Market Position: Enterprise RAG-as-a-Service with genuine no-code accessibility + developer-first API design (dual-track appeal) Pricing Advantage: $700/month entry undercuts enterprise competitors (Drift $30K+/year, Yellow.ai similar, CustomGPT varies) REMi Differentiator: Proprietary continuous quality monitoring addresses hallucination problem - capability absent from most competitors Benchmark Leadership: Achieved highest scores vs Vectara on Docmatix 1.4k dataset (answer relevance, context relevance, correctness) Open-Source Trust: NucliaDB transparency (710+ GitHub stars) vs black-box competitors (Lindy.ai, Drift, Yellow.ai) vs. CustomGPT: Similar RAG-as-a-Service category, Progress emphasizes REMi quality monitoring + open-source foundation differentiation vs. Drift/Yellow.ai: TRUE RAG platform vs conversational marketing/sales engagement platforms (fundamentally different categories) vs. Lindy.ai: Full API/SDK access vs NO public API (Progress developer-friendly, Lindy no-code only) Integration Gaps: NO native messaging channels (Slack/WhatsApp/Teams) vs omnichannel competitors - requires custom development HIPAA Gap: No documented certification creates healthcare trust gap vs compliant competitors (Drift has HIPAA) Recent Acquisition Risk: June 2025 Progress purchase means platform still maturing under new ownership with potential direction changes Progress Ecosystem Advantage: Integration with OpenEdge, Sitefinity CMS provides distribution channels unavailable to standalone competitors	Market position: Leading all-in-one RAG platform balancing enterprise-grade accuracy with developer-friendly APIs and no-code usability for rapid deployment Target customers: Mid-market to enterprise organizations needing production-ready AI assistants, development teams wanting robust APIs without building RAG infrastructure, and businesses requiring 1,400+ file format support with auto-transcription (YouTube, podcasts) Key competitors: OpenAI Assistants API, Botsonic, Chatbase.co, Azure AI, and custom RAG implementations using LangChain Competitive advantages: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, SOC 2 Type II + GDPR compliance, full white-labeling included, OpenAI API endpoint compatibility, hosted MCP Server support (Claude, Cursor, ChatGPT), generous data limits (60M words Standard, 300M Premium), and flat monthly pricing without per-query charges Pricing advantage: Transparent flat-rate pricing at $99/month (Standard) and $449/month (Premium) with generous included limits; no hidden costs for API access, branding removal, or basic features; best value for teams needing both no-code dashboard and developer APIs in one platform Use case fit: Ideal for businesses needing both rapid no-code deployment and robust API capabilities, organizations handling diverse content types (1,400+ formats, multimedia transcription), teams requiring white-label chatbots with source citations for customer-facing or internal knowledge projects, and companies wanting all-in-one RAG without managing ML infrastructure
A I Models
Starter (Free): GPT-4o mini only for ~50 questions/month Growth: GPT-4o mini + Claude (version unspecified) with 1K docs and unlimited users Enterprise: GPT-4o + Multi-layer model architecture with unlimited documents Multi-layer model architecture (Enterprise): Automatic routing to best-suited LLM per query part - complex queries decomposed into sub-queries with specialized agents Cost optimization: Maximizes accuracy while controlling costs through intelligent model routing No user-controlled runtime switching: Plan-based model selection only, no manual model switching interface Target accuracy: 97-98% accuracy claim across marketing materials and customer testimonials Human-in-the-loop: Suggested reply customization before sending when confidence is low	Anthropic Models: Claude 3.7, Claude 3.5 Sonnet v2 for safety-focused, high-quality generation OpenAI Models: ChatGPT 4o, 4o mini for industry-leading language capabilities Google Models: Gemini Flash 2.5, PaLM2 for multimodal and search-optimized tasks Meta Models: Llama 3.2 for open-source flexibility and customization Microsoft/Azure: Mistral Large 2 for enterprise deployments with Azure integration Cohere Models: Command-R suite for retrieval-optimized generation Nuclia Private GenAI: 100% data isolation mode for maximum security without third-party LLM exposure Model Switching: Change providers without architectural changes via Prompt Lab for side-by-side testing Embedding Flexibility: Configurable per Knowledge Box (Nuclia multilingual default + OpenAI embeddings)	Primary models: GPT-5.1 and 4 series from OpenAI, and Anthropic's Claude 4.5 (opus and sonnet) for enterprise needs Automatic model selection: Balances cost and performance by automatically selecting the appropriate model for each request Model Selection Details Proprietary optimizations: Custom prompt engineering and retrieval enhancements for high-quality, citation-backed answers Managed infrastructure: All model management handled behind the scenes - no API keys or fine-tuning required from users Anti-hallucination technology: Advanced mechanisms ensure chatbot only answers based on provided content, improving trust and factual accuracy
R A G Capabilities
RAGless architecture: Query-writing AI, not traditional vector search - "no embeddings, no hallucinations" with precise source attribution Bypasses retrieval at inference: Deterministic results without "black box retrieval" typical of RAG systems 6-mechanism hallucination prevention: LLM filtering, confidence-based gating, LLM-reviewed responses, guardrails layer, deterministic skill modules Real-time knowledge updates: Content used immediately after ingestion without retraining delays Chat2KB auto-learning (Growth/Enterprise): Auto-extracts Q&A pairs from conversations, emails, tickets with MECE classification Intelligent conflict resolution: Automatically removes contradictory information from knowledge base Customer accuracy results: Column Tax (94% accuracy, 98% queries resolved), Qogita (90% accuracy, 88% ticket resolution, 121% SLA improvement) Positioning: Criticizes RAG as "just smarter search engines" claiming "will become obsolete" - emphasizes action-taking over information-only responses	Agentic RAG Engine: Retrieval agents autonomously select optimal strategies based on query characteristics Four-Index Hybrid Search: Document (property filtering), Full Text (keyword/fuzzy), Vector/Chunk (semantic), Knowledge Graph (entity relationships) 30+ RAG Parameters: Custom chunking strategies, context size configuration, hybrid search weighting for fine-tuned optimization REMi v2 Quality Monitoring: Continuous evaluation across Answer Relevance, Context Relevance, Groundedness, Correctness (30x faster inference) Benchmark Leadership: Highest scores vs Vectara on Docmatix 1.4k dataset (answer relevance, context relevance, correctness) Pre-Built Ingestion Agents (Beta): Labeler (auto-classification), Generator (summaries/JSON), Graph Extraction (entities/relationships), Q&A Generator, Content Safety Multimodal Processing: OCR for scanned documents/images, automatic speech-to-text for audio (MP3, WAV, AIFF), video transcript extraction 60+ Document Formats: PDF, Word, Excel, PowerPoint, plain text, email formats with automatic parsing Open-Source Foundation: NucliaDB (710+ GitHub stars, AGPLv3) provides transparency into retrieval mechanisms vs black-box platforms	Core architecture: GPT-4 combined with Retrieval-Augmented Generation (RAG) technology, outperforming OpenAI in RAG benchmarks RAG Performance Anti-hallucination technology: Advanced mechanisms reduce hallucinations and ensure responses are grounded in provided content Benchmark Details Automatic citations: Each response includes clickable citations pointing to original source documents for transparency and verification Optimized pipeline: Efficient vector search, smart chunking, and caching for sub-second reply times Scalability: Maintains speed and accuracy for massive knowledge bases with tens of millions of words Context-aware conversations: Multi-turn conversations with persistent history and comprehensive conversation management Source verification: Always cites sources so users can verify facts on the spot
Use Cases
Enterprise B2C customer support: High-volume fintech, e-commerce, and healthcare companies needing 80% ticket resolution with 97-98% accuracy Action-taking AI agents: Autonomous refund processing, account updates, CRM sync (Salesforce), Stripe payment handling, Shopify order management beyond simple Q&A Helpdesk platform integration: 20+ native integrations (Zendesk, Intercom, Salesforce Service Cloud, Front, Gorgias, HubSpot, LiveChat, Freshdesk, Help Scout) without Zapier Multi-channel support: Slack, Discord, Microsoft Teams for internal/community support; website embedding (Fini Widget, Search Bar, Standalone) 100+ languages: Locale-based routing and real-time translation for global customer bases PII-sensitive industries: Auto-masking of SSN, passport, driver's license, taxpayer ID, credit cards with PII Shield Layer NOT suitable for: General-purpose document Q&A, content generation, or organizations without existing helpdesk platforms (Zendesk/Intercom/Salesforce)	Enterprise Knowledge Management: Non-technical teams (marketing, HR, legal, customer support) deploying knowledge bases in minutes Healthcare & Pharma: Althaia Hospitals medical protocol search for 5,000+ healthcare professionals with HIPAA-grade security needs Financial Services: BrokerChooser replaced keyword search with generative AI for significant conversion increases Education: Columbia Business School and Barry University for academic knowledge discovery and institutional knowledge management Engineering & Research: NAFEMS knowledge discovery across thousands of technical publications for international membership Trade Organizations: CCOO (Spain's largest union) serving 1M+ members with knowledge retrieval platform Intelligent Document Processing: Automatic document classification, routing, extraction, risk identification, and summary generation Dynamic Knowledge Management: Continuous updates, gap identification, and automatic documentation generation Developer RAG Backend: API-first infrastructure for building custom AI applications with Prompt Lab experimentation	Customer support automation: AI assistants handling common queries, reducing support ticket volume, providing 24/7 instant responses with source citations Internal knowledge management: Employee self-service for HR policies, technical documentation, onboarding materials, company procedures across 1,400+ file formats Sales enablement: Product information chatbots, lead qualification, customer education with white-labeled widgets on websites and apps Documentation assistance: Technical docs, help centers, FAQs with automatic website crawling and sitemap indexing Educational platforms: Course materials, research assistance, student support with multimedia content (YouTube transcriptions, podcasts) Healthcare information: Patient education, medical knowledge bases (SOC 2 Type II compliant for sensitive data) Financial services: Product guides, compliance documentation, customer education with GDPR compliance E-commerce: Product recommendations, order assistance, customer inquiries with API integration to 5,000+ apps via Zapier SaaS onboarding: User guides, feature explanations, troubleshooting with multi-agent support for different teams
Security & Compliance
SOC 2 Type II certified: Zero audit findings per Sprinto case study with annual audits ISO 27001 certified: International information security management standard ISO 42001 certified: AI governance standard - rare achievement demonstrating AI-specific compliance GDPR compliant: Full data subject rights with EU data residency option available HIPAA status conflicting: Marketing claims compliance, but case study says "next up" - verify before healthcare deployment PCI DSS: Claimed but not listed on official pricing page security section - verify for payment data "We do not train on your data" policy: Formal Data Processing Agreement (DPA) with OpenAI PII Shield Layer: Auto-masks SSN, passport, driver's license, taxpayer ID, credit cards in conversations AES-256 encryption at rest, TLS 1.3 in transit EU and US data residency options: Choose data storage location Dedicated AI instance (Enterprise): Single-tenant deployment for maximum data control RBAC (Growth/Enterprise), SSO (all tiers), audit logging	SOC2 Type 2: Annually audited for enterprise security assurance ISO 27001: Annually audited information security management certification GDPR Compliant: Built-in PII anonymization automatically detects and removes personal data Encryption: AES-256 at rest, TLS in transit for comprehensive data protection AI Risk Classification: Low to minimal AI risk category with policy-as-code guardrails Human-in-the-Loop: Validation options for critical workflows requiring human oversight Tenant Isolation: Customer data separation ensures multi-tenant security with isolated Knowledge Boxes Audit Logs: Standard across all pricing tiers for compliance tracking and security monitoring API Key Management: Temporal keys and rotation for security hygiene CRITICAL LIMITATION: NO HIPAA certification documented - healthcare organizations processing PHI must contact sales for compliance clarification Data Governance: Enterprise tier supports complete on-premise deployment for 100% data control and sovereignty	Encryption: SSL/TLS for data in transit, 256-bit AES encryption for data at rest SOC 2 Type II certification: Industry-leading security standards with regular third-party audits Security Certifications GDPR compliance: Full compliance with European data protection regulations, ensuring data privacy and user rights Access controls: Role-based access control (RBAC), two-factor authentication (2FA), SSO integration for enterprise security Data isolation: Customer data stays isolated and private - platform never trains on user data Domain allowlisting: Ensures chatbot appears only on approved sites for security and brand protection Secure deployments: ChatGPT Plugin support for private use cases with controlled access
Pricing & Plans
Pricing NOT publicly disclosed - requires sales contact for quotes Starter (Free): GPT-4o mini, ~50 questions/month, ~50 docs, ~5 users, 2 bots, SSO only Growth (estimated $999/mo): GPT-4o mini/Claude, 1K docs, unlimited users, SOC 2, GDPR, ISO 27001, RBAC, Chat2KB, Sophia AI Evaluator Enterprise (custom): GPT-4o, Multi-layer models, unlimited docs, dedicated AI instance, AI Actions, full compliance, white-glove onboarding Cost-per-resolution model: Pay based on resolved tickets rather than per-seat pricing - benefits high-volume teams Zero-Pay Guarantee: Only pay if >80% accuracy thresholds met (unique risk mitigation) Third-party estimates: "$0.10/interaction" (SaaSworthy) - unverified Implementation program: 60-day structured program (Discovery → Deployment → Optimization → Production) with weekly alignment calls Enterprise support: Dedicated AI engineers, customer success managers, 24/7 Slack channels	Fly Tier: $700/month - 10GB/15K resources, 750MB max file, 1 Knowledge Box, cloud only, 10K tokens/month included Growth Tier: $1,750/month - 50GB/80K resources, 1.5GB max file, 2 Knowledge Boxes, Prompt Lab access, 10K tokens/month Enterprise Tier: Custom pricing - Unlimited data/file size, 11 Knowledge Boxes, hybrid/on-prem deployment, 10K tokens/month Token Consumption: $0.008/token beyond 10K/month included across all tiers for usage-based scaling 14-Day Free Trial: Available without disclosed credit card requirement for hands-on evaluation AWS Marketplace: Available November 2025 for simplified enterprise procurement with existing cloud spend commitments Competitive Entry Point: $700/month undercuts enterprise alternatives (Drift $30K+/year, Yellow.ai similar, LiveChat per-agent scaling) Scaling Consideration: Token-based consumption pricing requires careful usage forecasting for budget predictability beyond included tier Best Value For: Organizations wanting to control costs through usage optimization vs fixed seat-based or per-project pricing models	Standard Plan: $99/month or $89/month annual - 10 custom chatbots, 5,000 items per chatbot, 60 million words per bot, basic helpdesk support, standard security View Pricing Premium Plan: $499/month or $449/month annual - 100 custom chatbots, 20,000 items per chatbot, 300 million words per bot, advanced support, enhanced security, additional customization Enterprise Plan: Custom pricing - Comprehensive AI solutions, highest security and compliance, dedicated account managers, custom SSO, token authentication, priority support with faster SLAs Enterprise Solutions 7-Day Free Trial: Full access to Standard features without charges - available to all users Annual billing discount: Save 10% by paying upfront annually ($89/mo Standard, $449/mo Premium) Flat monthly rates: No per-query charges, no hidden costs for API access or white-labeling (included in all plans) Managed infrastructure: Auto-scaling cloud infrastructure included - no additional hosting or scaling fees
Support & Documentation
Founding team: Ex-Uber engineers with CEO leading 4M+ interactions/month at Uber Backed by: Y Combinator Summer 2022 ($125K seed), Matrix Partners, angel investors from Uber, Intercom, Softbank, McKinsey, Twitter Company metrics: ~$2.5M annual revenue, 14 employees, 500K+ tickets/month processed Customers: HackerRank, Qogita, Column Tax, Atlas, TrainingPeaks, Bitdefender, Duolingo, Meesho 60-day implementation program: White-glove onboarding with dedicated implementation managers (Discovery → Deployment → Optimization → Production) Enterprise support tiers: Dedicated AI engineers and customer success managers with 24/7 Slack channels Documentation quality: Basic REST API documentation with Python and Node.js examples (completeness 3/5, error handling 2/5, rate limits 1/5) NO official SDKs: No Python, JavaScript, or other language SDKs - only API examples provided Open-source tool: Paramount (github.com/ask-fini/paramount) for agent accuracy measurement Product roadmap: Upcoming SDKs, multi-agent systems with collaboration/self-repair capabilities	Dual Documentation Portals: docs.rag.progress.cloud (primary) + legacy docs.nuclia.dev (fragmentation concern during transition) RAG Cookbook: Comprehensive downloadable guide for developers with implementation patterns and best practices SDK Ecosystem: Python (~21K weekly downloads via pip install nuclia) + JavaScript/TypeScript (@nuclia/core on NPM) REST API: Regional endpoints https://{region}.rag.progress.cloud/api/v1/ with complete programmatic control Key Endpoints: /ask (generative answers), /find (semantic search), /upload (ingestion), /remi (quality evaluation) 14-Day Free Trial: Hands-on evaluation platform without credit card requirement Progress Enterprise Support: Backed by 2,000+ employee parent company infrastructure with dedicated account management Open-Source Community: NucliaDB 710+ GitHub stars with AGPLv3 license transparency and community contributions Integration Examples: WordPress, Strapi plugins, Pipedream official app, Zapier API-compatible, Chrome extension for web indexing Progress Ecosystem: OpenEdge database connector, Sitefinity CMS integration ("first Generative CMS") for distribution advantages	Documentation hub: Rich docs, tutorials, cookbooks, FAQs, API references for rapid onboarding Developer Docs Email and in-app support: Quick support via email and in-app chat for all users Premium support: Premium and Enterprise plans include dedicated account managers and faster SLAs Code samples: Cookbooks, step-by-step guides, and examples for every skill level API Documentation Open-source resources: Python SDK (customgpt-client), Postman collections, GitHub integrations Open-Source SDK Active community: User community plus 5,000+ app integrations through Zapier ecosystem Regular updates: Platform stays current with ongoing GPT and retrieval improvements automatically
Limitations & Considerations
Pricing opacity: No public pricing - requires sales contact creating friction for evaluation vs transparent competitors HIPAA status conflicting: Marketing claims compliance but case study says "next up" - verify before healthcare deployment PCI DSS unverified: Claimed but not on official pricing page - verify for payment data handling Documentation limitations: Basic API docs (3/5 completeness, 2/5 error handling, 1/5 rate limits), no official SDKs Small team (14 employees): Limited support capacity compared to enterprise competitors (Intercom, Zendesk) RAGless positioning controversial: Claims RAG "will become obsolete" but many enterprises rely on proven RAG architectures Platform lock-in: Requires existing helpdesk platform (Zendesk/Intercom/Salesforce) - not standalone solution Less suitable for: General-purpose document Q&A, content generation, startups without established helpdesk infrastructure, organizations prioritizing transparent pricing Best for: Enterprise B2C support teams with high volumes prioritizing 97-98% accuracy over pricing transparency, willing to commit to 60-day implementation	NO HIPAA Certification Documented: Healthcare organizations processing PHI must contact sales - major compliance gap vs competitors with documented HIPAA NO Native Messaging Channels: No Slack, WhatsApp, Telegram, or Microsoft Teams integrations - requires custom API-based development Documentation Fragmentation: Dual portals (docs.rag.progress.cloud + docs.nuclia.dev) during Progress acquisition transition may cause confusion Recent Acquisition Risk: June 2025 Progress purchase means platform still maturing under new ownership with potential direction changes Scalability Concerns: Multiple problems limit scalability - hard to scale nodes up/down, write operations affect concurrent search performance NO Dropbox Integration: Missing Dropbox connector vs competitors - limits cloud storage sync options NO Notion Integration: Missing Notion connector - gap for knowledge management workflows NO YouTube Transcript Extraction: Not explicitly documented vs competitors with video indexing features Token-Based Billing Complexity: $0.008/token beyond 10K/month requires careful usage forecasting vs predictable seat-based pricing Missing Features: NO lead capture, NO human handoff/escalation workflows, NO proactive alerting (monitoring exists, alerting undocumented) Learning Curve: 30+ RAG parameters and Prompt Lab may feel technical for non-developer teams despite no-code dashboard Best For: Development teams and technical users - powerful for experts but may overwhelm business users wanting simple deployment	Managed service approach: Less control over underlying RAG pipeline configuration compared to build-your-own solutions like LangChain Vendor lock-in: Proprietary platform - migration to alternative RAG solutions requires rebuilding knowledge bases Model selection: Limited to OpenAI (GPT-5.1 and 4 series) and Anthropic (Claude, opus and sonnet 4.5) - no support for other LLM providers (Cohere, AI21, open-source models) Pricing at scale: Flat-rate pricing may become expensive for very high-volume use cases (millions of queries/month) compared to pay-per-use models Customization limits: While highly configurable, some advanced RAG techniques (custom reranking, hybrid search strategies) may not be exposed Language support: Supports 90+ languages but performance may vary for less common languages or specialized domains Real-time data: Knowledge bases require re-indexing for updates - not ideal for real-time data requirements (stock prices, live inventory) Enterprise features: Some advanced features (custom SSO, token authentication) only available on Enterprise plan with custom pricing
Core Agent Features
Sophie AI Agent: Fully autonomous customer service agent designed to act like a company's best support representative, resolving up to 80% of tickets end-to-end without human intervention 5-Layer Supervised Execution Framework: Layer 1 - Safety Guardrails: 40+ filters, PII masking (SSN, credit cards, passports), brand tone compliance Layer 2 - LLM Supervisor: Core orchestration brain determining resolution paths and task routing Layer 3 - Skill Modules: Deterministic modules for Search, Write, Follow Process, Take Action capabilities Layer 4 - Live Feedback: Auto-validates outputs, detects errors, learns from corrections in real-time Layer 5 - Traceability: Full audit trail of decisions and reasoning for transparency and compliance Multi-Layer Model Architecture (Enterprise): Automatic routing to best-suited LLM per query part - complex queries decomposed into sub-queries with specialized agents handling each component for maximum accuracy while controlling costs Action-Taking Capabilities: Goes beyond information retrieval - autonomous refund processing, account updates, CRM sync (Salesforce), Stripe payment handling, Shopify order management without human involvement AI Actions (Growth/Enterprise): Autonomous CRM/Stripe/Shopify updates triggered by conversation context - "It's the difference between 'You can find details here' and 'Done! I've processed that refund'" Continuous Learning: Sophie learns from every interaction through Chat2KB auto-learning (Growth/Enterprise), getting smarter, faster, and more accurate over time with MECE classification eliminating duplicate responses 100+ Language Support: Automatic translation with locale-based routing and real-time language detection - serve global customer bases without multilingual content management Intelligent Escalation: Human handoff preserves full conversation context with configurable triggers (keywords, sentiment analysis, topic-based rules, confidence thresholds) - seamless transition to human agents when needed	Retrieval Agents: Autonomously select optimal retrieval strategies based on query characteristics Pre-Built Ingestion Agents (Beta): Labeler (auto-classification), Generator (summaries/JSON/extraction), Graph Extraction (entities/relationships), Q&A Generator (automatic FAQ), Content Safety (inappropriate content flagging) Web Components: <nuclia-search-bar> and <nuclia-chat> for website embedding Widget Configuration: Point-and-click for suggestions, filters, metadata display, thumbnails, answer-only modes CSS Customization: Shadow DOM architecture with cssPath attribute for advanced styling White-Labeling: Full OEM deployment support via API-first design MISSING FEATURES: NO lead capture, NO human handoff/escalation workflows, NO proactive alerting (monitoring exists, alerting undocumented)	Custom AI Agents: Build autonomous agents powered by GPT-4 and Claude that can perform tasks independently and make real-time decisions based on business knowledge Decision-Support Capabilities: AI agents analyze proprietary data to provide insights, recommendations, and actionable responses specific to your business domain Multi-Agent Systems: Deploy multiple specialized AI agents that can collaborate and optimize workflows in areas like customer support, sales, and internal knowledge management Memory & Context Management: Agents maintain conversation history and persistent context for coherent multi-turn interactions View Agent Documentation Tool Integration: Agents can trigger actions, integrate with external APIs via webhooks, and connect to 5,000+ apps through Zapier for automated workflows Hyper-Accurate Responses: Leverages advanced RAG technology and retrieval mechanisms to deliver context-aware, citation-backed responses grounded in your knowledge base Continuous Learning: Agents improve over time through automatic re-indexing of knowledge sources and integration of new data without manual retraining
R A G-as-a- Service Assessment
Platform Type: AGENTIC AI CUSTOMER SUPPORT PLATFORM with RAGless architecture - NOT traditional RAG-as-a-Service but query-writing AI specifically designed for customer support automation Architectural Approach: RAGless architecture using query-writing AI instead of traditional vector search - "no embeddings, no hallucinations" with precise source attribution and deterministic results Platform Overview Controversial Positioning: Criticizes RAG as "just smarter search engines" claiming "will become obsolete" - emphasizes action-taking over information-only responses, positioning against traditional RAG platforms Agent Capabilities: Sophie's 5-layer supervised execution framework with Safety Guardrails, LLM Supervisor, Skill Modules (Search, Write, Follow Process, Take Action), Live Feedback, and Traceability - 97-98% accuracy claim Developer Experience: Basic REST API (v2) with Bearer Token authentication but LIMITED - NO official SDKs (Python, JavaScript, or any language), only basic Python/Node.js examples, documentation quality concerns (3/5 completeness, 2/5 error handling, 1/5 rate limits) No-Code Capabilities: Dashboard for agent configuration, 20+ native helpdesk integrations (Zendesk, Intercom, Salesforce), "2 minutes" initial setup, "Day 1 Ready-to-Use" - but requires existing helpdesk platform Target Market: Enterprise B2C companies with high support volumes (fintech, e-commerce, healthcare), helpdesk teams using Zendesk/Intercom/Salesforce Service Cloud requiring action-taking AI beyond simple Q&A Technology Differentiation: 6-mechanism hallucination prevention (RAGless architecture, LLM filtering, confidence-based gating, LLM-reviewed responses, guardrails, deterministic skill modules), 97-98% accuracy vs ~80% competitors, Zero-Pay Guarantee (only pay if >80% accuracy) Deployment Model: Cloud-hosted SaaS tightly integrated with helpdesk platforms - NOT standalone deployment, requires Zendesk/Intercom/Salesforce as foundation Enterprise Features: SOC 2 Type II, ISO 27001, ISO 42001 (AI governance), GDPR compliant, HIPAA status conflicting (verify before healthcare use), PII Shield Layer auto-masking, EU/US data residency, dedicated AI instance (Enterprise) Pricing Model: NOT publicly disclosed (estimated ~$999/month Growth tier), cost-per-resolution model vs per-seat pricing, Zero-Pay Guarantee, 60-day implementation program with weekly alignment calls Use Case Fit: Enterprise B2C support teams needing action-taking AI (refunds, account updates, CRM sync) beyond information retrieval, organizations using Zendesk/Intercom/Salesforce requiring 20+ native integrations, companies prioritizing 97-98% accuracy with ISO 42001 certification NOT A RAG PLATFORM: Explicitly positions AGAINST traditional RAG - uses query-writing AI bypassing retrieval at inference for deterministic results, fundamentally different approach than RAG-as-a-Service competitors NOT Suitable For: General-purpose document Q&A, content generation, organizations without existing helpdesk platforms, developers needing programmatic RAG API access, teams wanting traditional RAG architecture Competitive Positioning: Positions against Intercom Fin with "agentic" differentiation claiming 95%+ accuracy vs ~80%, competes with Zendesk Answer Bot, Ada, Ultimate.ai - unique RAGless approach vs traditional RAG chatbots	Platform Type: TRUE RAG-AS-A-SERVICE PLATFORM - Core mission is retrieval-augmented generation backend with developer-first API access Core Focus: Semantic search and generative Q&A across knowledge bases with transparent NucliaDB architecture RAG Backend Design: Fully managed RAG infrastructure with embeddable widgets (NOT closed conversational marketing like Drift/Yellow.ai) Programmatic Access: Complete REST API + dual SDKs (Python/JavaScript) for full knowledge base management LLM Flexibility: 7 provider options switchable without architectural changes (Anthropic, OpenAI, Google, Meta, Cohere, Azure, Nuclia) Open-Source Transparency: NucliaDB foundation (710+ GitHub stars) provides visibility into retrieval mechanisms vs black-box platforms (Lindy.ai) Comparison Alignment: Direct architectural comparison to CustomGPT.ai is valid - both are RAG-as-a-Service platforms with API-first design Use Case Fit: Organizations prioritizing knowledge retrieval, semantic search, and generative Q&A over conversational marketing/sales engagement	Platform Type: TRUE RAG-AS-A-SERVICE PLATFORM - all-in-one managed solution combining developer APIs with no-code deployment capabilities Core Architecture: Serverless RAG infrastructure with automatic embedding generation, vector search optimization, and LLM orchestration fully managed behind API endpoints API-First Design: Comprehensive REST API with well-documented endpoints for creating agents, managing projects, ingesting data (1,400+ formats), and querying chat API Documentation Developer Experience: Open-source Python SDK (customgpt-client), Postman collections, OpenAI API endpoint compatibility, and extensive cookbooks for rapid integration No-Code Alternative: Wizard-style web dashboard enables non-developers to upload content, brand widgets, and deploy chatbots without touching code Hybrid Target Market: Serves both developer teams wanting robust APIs AND business users seeking no-code RAG deployment - unique positioning vs pure API platforms (Cohere) or pure no-code tools (Jotform) RAG Technology Leadership: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, proprietary anti-hallucination mechanisms, and citation-backed responses Benchmark Details Deployment Flexibility: Cloud-hosted SaaS with auto-scaling, API integrations, embedded chat widgets, ChatGPT Plugin support, and hosted MCP Server for Claude/Cursor/ChatGPT Enterprise Readiness: SOC 2 Type II + GDPR compliance, full white-labeling, domain allowlisting, RBAC with 2FA/SSO, and flat-rate pricing without per-query charges Use Case Fit: Ideal for organizations needing both rapid no-code deployment AND robust API capabilities, teams handling diverse content types (1,400+ formats, multimedia transcription), and businesses requiring production-ready RAG without building ML infrastructure from scratch Competitive Positioning: Bridges the gap between developer-first platforms (Cohere, Deepset) requiring heavy coding and no-code chatbot builders (Jotform, Kommunicate) lacking API depth - offers best of both worlds
R E Mi Evaluation Model ( Core Differentiator)
N/A	Proprietary Investment: Significant R&D differentiator addressing hallucination problem - absent from most competitors REMi v2 (Current): Llama-REMi v1 based on Llama 3.2-3B with 30x faster inference vs original Mistral implementation Continuous Quality Monitoring: Evaluates EVERY interaction across four dimensions (0-5 scale) Answer Relevance: Measures how directly response addresses the query Context Relevance: Assesses quality of retrieved passages relative to question Groundedness: Evaluates degree to which answers derive from source context (hallucination detection) Answer Correctness: Alignment with ground truth when available (optional dimension) Benchmark Validation: Nuclia with OpenAI embeddings achieved highest scores vs Vectara on Docmatix 1.4k dataset across answer relevance, context relevance, correctness Real-Time Visibility: Dashboard health displays with 7-day rolling averages and performance graphs (24h to 30d) Competitive Advantage: Most RAG platforms lack continuous quality evaluation - Progress makes this core differentiator	N/A
Open- Source Nuclia D B Foundation
N/A	GitHub Presence: 710+ stars, AGPLv3 license provides full transparency into core retrieval mechanisms Technology Stack: Python and Rust implementation for performance and reliability Managed Infrastructure: Progress removes operational burden while maintaining technical transparency Three-Tier Storage: Tier 3 (S3/GCS blob storage), Tier 2 (TiKV key-value with ACID), Tier 1 (sharded indexes) Four Index Types: Document Index (property filtering), Full Text (keyword/fuzzy search), Chunk/Vector (semantic similarity), Knowledge Graph (entity relationships) Dynamic Sharding: Automatic shard creation as vectors grow with index node replication for fault tolerance Embedding Flexibility: Switchable per Knowledge Box (Nuclia multilingual + OpenAI options) 100M Vector Performance: Full ingestion and optimization in ~20 minutes with sufficient worker allocation Developer Trust: Open-source foundation allows code inspection and contribution vs black-box competitors	N/A
Multi- Lingual Support
N/A	Nuclia Multilingual Embedding Model: Default model supporting multiple languages out-of-box 60+ Document Format Processing: Multi-language content across PDF, Word, Excel, PPT, text, email Automatic Transcription: Multi-language speech-to-text for audio/video content Configurable Embeddings: Per Knowledge Box language optimization LLM Provider Flexibility: 7 providers with varying multilingual capabilities (Claude, GPT, Gemini, Llama, etc.) Global Customer Base: Deployed across Spain, US, international markets indicating production multilingual usage	N/A
Deployment & Infrastructure
N/A	Fully Managed Cloud: EU (primary) and US data centers with regional API routing (https://{region}.rag.progress.cloud/api/v1/) Hybrid Deployment: Cloud processing with on-premise NucliaDB storage for data sovereignty requirements Complete On-Premise: Enterprise tier supports 100% on-premise deployment for maximum data governance AWS Marketplace: Available November 2025 for streamlined enterprise procurement with existing cloud spend Three-Tier Architecture: S3/GCS blob storage (Tier 3), TiKV metadata (Tier 2), sharded indexes (Tier 1) Dynamic Scaling: Automatic shard creation as vector counts grow with index node replication Web Component Embedding: <nuclia-search-bar> and <nuclia-chat> for website integration Multi-Region Support: Regional data residency options (EU/US) for compliance requirements	N/A
Customer Base & Case Studies
N/A	SRS Distribution (Wholesale Building Materials): "Progress Agentic RAG has fundamentally changed how we access and act on information across our organisation. Its ability to deliver fast, accurate, and verifiable insights from our unstructured data has been a game-changer for productivity and decision-making." BrokerChooser (Financial Services): Replaced keyword search with generative AI, reporting significant conversion increases and better user experience NAFEMS (Engineering Simulation Association): Knowledge discovery across thousands of technical publications for international membership community Althaia Hospitals (Spain's Largest Central Catalonia Hospital): Medical protocol search supporting 5,000+ healthcare professionals Columbia Business School: Academic knowledge discovery and research support Barry University: Education sector deployment for institutional knowledge management CCOO (Spain's Largest Trade Union): 1M+ members served with knowledge retrieval platform Buff Sportswear: Commercial deployment for product and customer knowledge management Pre-Acquisition Scale: ~20 customers across healthcare, pharmaceutical, education, public administration sectors	N/A

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Final Thoughts

Final Verdict: Fini AI vs Progress Agentic RAG

After analyzing features, pricing, performance, and user feedback, both Fini AI and Progress Agentic RAG are capable platforms that serve different market segments and use cases effectively.

When to Choose Fini AI

You value industry-leading 97-98% accuracy claim backed by customer testimonials
True action-taking capabilities - executes refunds, KYC, account updates beyond Q&A
RAGless architecture eliminates hallucinations with precise source attribution

Best For: Industry-leading 97-98% accuracy claim backed by customer testimonials

When to Choose Progress Agentic RAG

You value proprietary remi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors
Open-source NucliaDB transparency (710+ GitHub stars) with managed infrastructure removes operational burden while maintaining technical visibility
Genuine no-code accessibility: business users (marketing, HR, legal, support) can deploy functional RAG pipelines in minutes via visual dashboard

Best For: Proprietary REMi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors

Migration & Switching Considerations

Switching between Fini AI and Progress Agentic RAG requires careful planning. Consider data export capabilities, API compatibility, and integration complexity. Both platforms offer migration support, but expect 2-4 weeks for complete transition including testing and team training.

Pricing Comparison Summary

Fini AI starts at custom pricing, while Progress Agentic RAG begins at $700/month. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.

Our Recommendation Process

Start with a free trial - Both platforms offer trial periods to test with your actual data
Define success metrics - Response accuracy, latency, user satisfaction, cost per query
Test with real use cases - Don't rely on generic demos; use your production data
Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
Check vendor stability - Review roadmap transparency, update frequency, and support quality

For most organizations, the decision between Fini AI and Progress Agentic RAG comes down to specific requirements rather than overall superiority. Evaluate both platforms with your actual data during trial periods, focusing on accuracy, latency, ease of integration, and total cost of ownership.

📚 Next Steps

Ready to make your decision? We recommend starting with a hands-on evaluation of both platforms using your specific use case and data.

• Review: Check the detailed feature comparison table above
• Test: Sign up for free trials and test with real queries
• Calculate: Estimate your monthly costs based on expected usage
• Decide: Choose the platform that best aligns with your requirements

Last updated: December 12, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

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Priyansh Khodiyar

DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.

Fini AI vs Progress Agentic RAG