What are the main differences between Deviniti and Progress Agentic RAG?

Deviniti excels in Strong compliance and security focus and Self-hosted solutions for data privacy, scoring 77/100 in user ratings with 81% 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: Deviniti focuses on Custom AI agent development 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: Deviniti or Progress Agentic RAG?

Progress Agentic RAG offers SOC 2 compliance for enterprise security. HIPAA compliance is available in Deviniti. For scalability, both offer cloud-based deployment options. Progress Agentic RAG leads with 82/100 enterprise readiness score.

How much does Deviniti cost vs Progress Agentic RAG?

Deviniti starts at custom pricing/custom (enterprise model), while Progress Agentic RAG begins at $700/monthly (paid model). Both require paid plans. This represents a significant price difference of $700/month. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Deviniti to Progress Agentic RAG?

Migration from Deviniti to Progress Agentic RAG typically requires API-based data export/import, which Progress Agentic RAG provides. Progress Agentic RAG provides comprehensive API access good documentation for the migration process. Estimated migration time: 1-2 weeks for full deployment. Contact Progress Agentic RAG support for migration assistance and best practices.

What integrations do Deviniti and Progress Agentic RAG support?

Deviniti integrates with Atlassian suite, Enterprise systems, Custom APIs, Banking systems, Legal platforms (7 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). Progress Agentic RAG offers API capabilities. Integration coverage is comparable, with webhook support for real-time data sync.

What are the main differences between Deviniti and Progress Agentic RAG?

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

Which is better for enterprises: Deviniti 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 Deviniti cost vs Progress Agentic RAG?

Deviniti 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 Deviniti to Progress Agentic RAG?

Migration from Deviniti 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 Deviniti and Progress Agentic RAG support?

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

Deviniti 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 Deviniti 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 Deviniti 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 Deviniti 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 Deviniti if: you value strong compliance and security focus
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 Deviniti

Deviniti is self-hosted genai solutions for compliance-critical industries. Deviniti is an AI development company specializing in secure, self-hosted AI agents and LLM solutions for highly regulated industries like finance, healthcare, and legal, with expertise in RAG architecture and custom AI development. Founded in 2010, headquartered in Kraków, Poland, the platform has established itself as a reliable solution in the RAG space.

Overall Rating

77/100

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, both platforms score similarly in overall satisfaction. From a cost perspective, Deviniti starts at a lower price point. The platforms also differ in their primary focus: AI Development 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

Deviniti	Progress Agentic RAG	CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Builds custom pipelines to pull in pretty much any source—internal docs, FAQs, websites, databases, even proprietary APIs. Works with all the usual suspects (PDF, DOCX, etc.) and can tap uncommon sources if the project needs it. Project case study Designs scalable setups—hardware, storage, indexing—to handle huge data sets and keep everything fresh with automated pipelines. Learn more	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
Plugs the chatbot into any channel you need—web, mobile, Slack, Teams, or even legacy apps—tailored to your stack. Spins up custom API endpoints or webhooks to hook into CRMs, ERPs, or ITSM tools (dev work included). Integration approach	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
Builds a domain-tuned AI chatbot with multi-turn memory, context, and any language you need (local LLMs included). Can add lead capture, human handoff, and tight workflow hooks (e.g., IT tickets) exactly as you specify. Case study	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
Everything’s bespoke: UI, tone, flows—whatever matches your brand. Slots into your existing tools with custom styling and domain-specific dialogs—changes just take dev effort. Custom approach	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
Pick any model—GPT-4, Claude, Llama 2, Falcon—whatever fits your needs. Fine-tune on proprietary data for insider terminology, but swapping models means a new build/deploy cycle. Our services	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)
Delivers a project-specific API—JSON over HTTP—made exactly for your endpoints. Docs, samples, and support come straight from Deviniti engineers, not a public SDK. Project example	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
Uses best-practice retrieval (multi-index, tuned prompts) to serve precise answers. Fine-tunes on your data to squash hallucinations, though perfecting it may need ongoing tweaks. Our approach	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)
Total control: add new sources with custom pipelines, tweak bot tone, inject live API calls—whatever you dream up. Everything’s bespoke, so updates usually involve a quick dev sprint. Case details	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
Project-based pricing plus optional maintenance—great for unique enterprise needs. Your infra (cloud or on-prem) handles the load; the solution is built to scale to millions of queries. Client portfolio	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
Deploy on-prem or private cloud for full data control and compliance peace of mind. Uses strong encryption, access controls, and hooks into your existing security stack. Security details	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
Custom monitoring ties into tools like CloudWatch or Prometheus to track everything. Can add an admin dashboard or SIEM feeds for real-time analytics and alerts. More info	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
Hands-on support from Deviniti—from kickoff through post-launch—direct access to the dev team. Docs, training, and integrations are built around your stack, not one-size-fits-all. Our services	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
Can build hybrid agents that run complex, transactional tasks—not just Q&A. You own the solution end-to-end and can evolve it as AI tech moves forward. Custom governance	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
No out-of-the-box no-code dashboard—IT or bespoke admin panels handle config. Everyday users chat with the bot; deeper tweaks live with the tech team.	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: Custom AI development agency (200+ clients served) specializing in self-hosted, enterprise RAG solutions with domain-specific fine-tuning and legacy system integration Target customers: Large enterprises needing fully custom AI solutions, organizations with legacy systems requiring specialized integration, and companies requiring on-premises deployment with complete data sovereignty and compliance control Key competitors: Azumo, internal AI development teams, Contextual.ai (enterprise), and other custom AI consulting firms Competitive advantages: 200+ enterprise clients demonstrating proven track record, model-agnostic approach with fine-tuning on proprietary data, on-prem/private cloud deployment for full data control, custom API/workflow development tailored to exact specifications, white-glove support with direct dev team access, and complete solution ownership with bespoke UI/branding Pricing advantage: Project-based pricing plus optional maintenance; higher upfront cost than SaaS but provides long-term ownership without subscription fees; best value for unique enterprise needs that can't be met with off-the-shelf solutions and require custom integrations Use case fit: Ideal for enterprises with legacy systems needing specialized AI integration, organizations requiring domain-tuned models with insider terminology, companies needing hybrid AI agents handling complex transactional tasks beyond Q&A, and businesses demanding on-premises deployment with complete data sovereignty and custom compliance measures	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
Model-agnostic approach: Supports any LLM - GPT-4, Claude, Llama 2, Falcon, Cohere, or custom models based on client needs Custom model fine-tuning: Fine-tune models on proprietary data for domain-specific terminology and insider jargon Local LLM deployment: On-premises model hosting for complete data sovereignty and offline operation Multiple model support: Deploy different models for different use cases within same infrastructure Model flexibility: Swap models through new build/deploy cycle as requirements evolve Custom training pipelines: Build specialized training workflows for continuous model improvement	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
Custom RAG architecture: Best-practice retrieval with multi-index strategies and tuned prompts for precise answers Domain-specific fine-tuning: Train on proprietary data to eliminate hallucinations and improve accuracy for insider terminology Multi-hop retrieval: Complex query workflows requiring multiple retrieval steps Custom vector databases: Choose and configure optimal vector DB backend for your scale and performance needs Hybrid search: Combine semantic and keyword search strategies tailored to your data characteristics Source attribution: Full citation tracking with confidence scores and document references Continuous improvement: Ongoing tweaks and refinements to perfect retrieval accuracy over time	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 knowledge bases: Self-hosted chatbots with custom knowledge bases for internal company documentation Legacy system integration: AI agents that interface with proprietary APIs, ERPs, CRMs, and ITSM tools Regulated industries: On-prem deployment for healthcare, finance, and government with complete data control Multi-lingual support: Custom chatbots supporting any language with local LLM deployment Hybrid AI agents: Complex transactional workflows beyond Q&A (IT ticket creation, workflow automation) Custom channel deployment: Integrate into any channel - web, mobile, Slack, Teams, or legacy applications Domain-tuned assistants: Specialized agents with fine-tuned models for technical or medical terminology	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
On-premises deployment: Deploy on-prem or private cloud for complete data control and air-gapped environments Compliance customization: Build custom compliance measures for HIPAA, GDPR, SOC 2, or industry-specific requirements Strong encryption: AES-256 encryption at rest and TLS 1.3 in transit with custom key management Access controls: Role-based access control (RBAC) integrated with existing identity management systems Security integration: Hooks into existing security stack (SIEM, monitoring, alerting, audit logging) Data residency: Full control over where data is stored and processed (US, EU, on-prem) No third-party data sharing: Complete data sovereignty with no cloud vendor dependencies Custom monitoring: Integrated with CloudWatch, Prometheus, or enterprise monitoring tools	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
Project-based pricing: Custom quotes based on scope, complexity, and integration requirements Typical project range: $50K-$500K+ for initial development depending on complexity Optional maintenance: Ongoing support and enhancement contracts available post-launch Infrastructure costs: Client manages cloud or on-prem infrastructure costs separately No per-seat fees: Own the solution outright without subscription charges Professional services: Consulting, integration, training, and documentation included in project scope Long-term value: Higher upfront cost but no recurring SaaS fees - best for permanent enterprise solutions 200+ client portfolio: Proven track record across Fortune 500 and mid-market enterprises	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
White-glove support: Direct access to development team from kickoff through post-launch Custom documentation: Tailored documentation for your specific implementation and tech stack Training programs: Custom training for IT teams and end users on solution usage and maintenance Dedicated project manager: Single point of contact throughout development lifecycle Post-launch support: Optional maintenance contracts with SLA guarantees and priority response Integration support: Hands-on help connecting to existing enterprise systems and workflows Knowledge transfer: Complete handoff of code, architecture docs, and operational runbooks Enterprise focus: Proven experience with large-scale deployments and complex requirements	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
High upfront cost: $50K-$500K+ initial development vs $29-$999/month SaaS solutions Longer time to value: 2-6 month development cycle vs instant SaaS deployment Custom maintenance required: Updates and changes require development work, not self-service No out-of-box features: Everything built from scratch - no pre-built templates or no-code tools Technical expertise required: IT team needed for ongoing management and infrastructure Project-based approach: Each enhancement or change may require additional development sprint Not for budget-constrained SMBs: Best suited for large enterprises with significant AI budgets Best for unique needs only: Only justified when off-the-shelf solutions cannot meet requirements	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
Custom AI Agents: Build autonomous agents using advanced LLM architecture with planning modules, memory systems, and RAG pipelines tailored to exact business requirements Agent Development Planning Module: Agents break down complex tasks into smaller manageable steps using task decomposition methods - enabling multi-step autonomous workflows Memory System: Retains past interactions ensuring consistent responses in long-running workflows, maintaining context to improve handling of complex tasks over time RAG Integration: Agents use specialized RAG pipelines, code interpreters, and external APIs to gather and process data efficiently - enhancing ability to access and use external resources for accurate outcomes RAG Implementation Tool & API Integration: Agents execute actions beyond Q&A - integrate with CRMs, ERPs, ITSM tools, proprietary APIs, and legacy systems through custom webhooks and endpoints Domain-Tuned Behavior: Fine-tune on proprietary data for insider terminology, multi-turn memory with context preservation, and any language support including local LLM deployment Hybrid Agent Capabilities: Build agents that run complex transactional tasks beyond simple Q&A - handle workflows like IT ticket creation, CRM updates, and approval processes Hybrid Agents Real-World Proven: Deployed AI Agent in Credit Agricole bank for customer service automation - routes simple queries automatically, flags complex ones for human support, and drafts personalized replies	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: CUSTOM AI DEVELOPMENT CONSULTANCY - not a platform but professional services firm building bespoke enterprise RAG solutions and AI agents from scratch (200+ clients served) Core Offering: Project-based custom development of self-hosted AI agents, RAG architectures, and LLM applications tailored to exact specifications - not pre-built software or SaaS Agent Capabilities: Build fully autonomous AI agents with planning modules, memory systems, RAG pipelines, and tool integration - proven in regulated industries like banking (Credit Agricole deployment) Agent Services Developer Experience: White-glove professional services with dedicated dev team, project-specific API development (JSON over HTTP), custom documentation and samples, hands-on support from kickoff through post-launch No-Code Capabilities: NONE - everything requires custom development work. No dashboard, visual builders, or self-service tools. IT teams or bespoke admin panels handle configuration post-delivery Target Market: Large enterprises with legacy systems needing specialized AI integration, organizations requiring on-premises deployment with complete data sovereignty, companies with unique needs that can't be met with off-the-shelf solutions RAG Technology Approach: Best-practice retrieval with multi-index strategies, tuned prompts, fine-tuning on proprietary data to eliminate hallucinations, custom vector DB selection, and hybrid search strategies tailored to data characteristics RAG Approach Deployment Model: On-prem or private cloud only - complete data control with no cloud vendor dependencies, custom infrastructure managed by client, strong encryption and access controls integrated with existing security stack Enterprise Readiness: ISO 27001 certification, GDPR and CCPA compliance, custom compliance measures for HIPAA or industry-specific requirements, AES-256 encryption, RBAC integrated with existing identity management Pricing Model: Project-based $50K-$500K+ initial development plus optional ongoing maintenance contracts - higher upfront cost but no recurring SaaS fees, full solution ownership Use Case Fit: Enterprises with legacy systems needing specialized AI integration, domain-tuned models with insider terminology, hybrid AI agents handling complex transactional tasks, on-premises deployment with complete data sovereignty NOT A PLATFORM: Does not offer self-service software, API-as-a-service, or turnkey solutions - exclusively custom development consultancy requiring sales engagement and multi-month build cycles Competitive Positioning: Competes with other AI consultancies (Azumo, internal AI teams) and enterprise RAG platforms - differentiates through 200+ client track record, regulated industry expertise (banking, legal), and complete customization	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: Deviniti vs Progress Agentic RAG

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

When to Choose Deviniti

You value strong compliance and security focus
Self-hosted solutions for data privacy
Domain expertise in regulated industries

Best For: Strong compliance and security focus

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 Deviniti 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

Deviniti 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 Deviniti 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 11, 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.

Deviniti vs Progress Agentic RAG