Contextual AI vs Deviniti: A Detailed Comparison

Priyansh Khodiyar's avatar
Priyansh KhodiyarDevRel at CustomGPT
Comparison Image cover for the blog Contextual AI vs Deviniti

Fact checked and reviewed by Bill. Published: 01.04.2024 | Updated: 25.04.2025

In this article, we compare Contextual AI and Deviniti across various parameters to help you make an informed decision.

Welcome to the comparison between Contextual AI and Deviniti!

Here are some unique insights on Contextual AI:

Contextual AI focuses on enterprise-grade accuracy and security—fine-grained access control, robust guardrails, and advanced retrieval for large, sensitive datasets. Setup is API-driven and assumes a tech-savvy team.

And here's more information on Deviniti:

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Enjoy reading and exploring the differences between Contextual AI and Deviniti.

Comparison Matrix

Feature
logo of contextualaiContextual AI
logo of devinitiDeviniti
logo of customGPT logoCustomGPT
Data Ingestion & Knowledge Sources
  • Easily brings in both unstructured files (PDFs, HTML, images, charts) and structured data (databases, spreadsheets) through ready-made connectors.
  • Does multimodal retrieval—turns images and charts into embeddings so everything is searchable together. Source
  • Hooks into popular SaaS tools like Slack, GitHub, and Google Drive for seamless data flow.
  • 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
  • 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
  • Built for API integration first—no plug-and-play web widget included.
  • Enterprise-grade endpoints and a Snowflake Native App option make tight data integration straightforward. Source
  • 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
  • Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app.
  • Offers ready-made hooks for Slack, Microsoft Teams, WhatsApp, Telegram, and Facebook Messenger. 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.
Core Chatbot Features
  • Powers advanced RAG agents with multi-hop retrieval and chain-of-thought reasoning for tough questions.
  • Uses a reranker plus groundedness scoring for factual answers with precise attribution. Source
  • “Instant Viewer” highlights the exact source text backing each part of the answer.
  • 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
  • Powers retrieval-augmented Q&A with GPT-4 and GPT-3.5 Turbo, keeping answers anchored to your own content.
  • 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
  • Lets you tweak system prompts, tone, and content filters to match company policies—on the back end.
  • No out-of-the-box UI builder; you’ll embed it in your own branded front end. Source
  • 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
  • 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.
LLM Model Options
  • Runs on its own Grounded Language Model (GLM) tuned for RAG—tests show ~88 % factual accuracy.
  • Exposes standalone model APIs (reranker, generator) with simple token-based pricing. Source
  • 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
  • Taps into top models—OpenAI’s GPT-4, GPT-3.5 Turbo, and even Anthropic’s Claude for enterprise needs.
  • 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 (API & SDKs)
  • Offers solid REST APIs and a Python SDK for managing agents, ingesting data, and querying. Source
  • Endpoints cover tuning, evaluation, and standalone components—all with clear, token-based pricing.
  • 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
  • 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.
Integration & Workflow
  • Deploy in the cloud, a VPC, on-prem, or as a Snowflake Native App—whatever fits your stack.
  • Fits into CI/CD pipelines and event-driven flows through custom API calls. Source
  • Deeply embeds into enterprise flows—internal portals, mobile apps, you name it—using custom code.
  • Can trigger ERP actions or open tickets automatically when the bot escalates a query. Integration case
  • Gets you live fast with a low-code dashboard: create a project, add sources, and auto-index content in minutes.
  • Fits existing systems via API calls, webhooks, and Zapier—handy for automating CRM updates, email triggers, and more. Auto-sync Feature
  • Slides into CI/CD pipelines so your knowledge base updates continuously without manual effort.
Performance & Accuracy
  • RAG 2.0 approach tops industry benchmarks for document understanding and factuality. Source
  • Handles large, noisy datasets with multi-hop retrieval and robust reranking for grounded answers.
  • 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
  • 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)
  • Create multiple datastores and link them to agents by role or permission for fine-grained access.
  • Tune the LLM on your own data, add guardrails, and embed custom logic as needed. Source
  • 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
  • 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
  • Usage-based pricing tailored for enterprises—cost scales with agent capacity, data size, and query load. Source
  • Standalone component APIs are priced per token, letting you mix and match pieces as you grow.
  • 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
  • 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
  • SOC 2 compliant with encryption in transit and at rest; deploy on-prem or in a VPC for full sovereignty. Source
  • Implements role-based permissions and query-time access checks to keep data secure.
  • 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
  • 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
  • Built-in evaluation shows groundedness scores, retrieval metrics, and logs every step. Source
  • Plugs into external monitoring tools and supports detailed logging for audits and troubleshooting.
  • 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
  • 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
  • High-touch enterprise support with solution engineers and technical account managers.
  • Grows its ecosystem via partnerships (e.g., Snowflake) and industry thought leadership. Source
  • 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
  • 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
  • Great for mission-critical apps that need multimodal retrieval and advanced reasoning.
  • Requires more up-front setup and technical know-how than no-code tools—best for teams with ML expertise.
  • Handles complex needs like role-based data access and evolving multimodal content. Source
  • 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
  • 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
  • Web console helps manage agents, but there’s no drag-and-drop chatbot builder.
  • UI integration is a coding project—APIs are powerful, but non-tech users will need developer help.
  • 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.
  • 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.

We hope you found this comparison of Contextual AI vs Deviniti helpful.

For organizations needing strict compliance and high accuracy at scale, Contextual AI is compelling. Simpler use cases may find the engineering overhead more than they bargained for.

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Priyansh Khodiyar's avatar

Priyansh Khodiyar

DevRel at CustomGPT. Passionate about AI and its applications. Here to help you navigate the world of AI tools.