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Enterprise RAG🟡Low Code
C

Contextual AI

Context engineering platform that turns generalist LLMs into trusted enterprise experts across technical documentation, specs, and institutional knowledge.

Starting atContact sales
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In Plain English

Context engineering platform that turns generalist LLMs into trusted enterprise experts across technical documentation, specs, and institutional knowledge.

OverviewFeaturesPricingUse CasesFAQ

Overview

Contextual AI is a platform for what it calls 'context engineering' — turning generalist LLMs into trusted domain experts that can reason over highly technical documents like engineering datasheets, regulatory filings, legal contracts, and product specifications. Co-founded by Douwe Kiela (one of the original authors of the RAG paper at Meta), the platform pairs research-grade retrieval, grounding, and evaluation components with an enterprise-ready operations layer. The headline product, Agent Composer, lets teams assemble agents that perform root cause analysis on device logs, deep IP and compliance research, requirements traceability across audit-ready documents, and structured extraction from messy data rooms. Underneath, Contextual exposes RAG Component APIs — parsing, embedding, reranking, generation, and grounding — that customers can compose into their own stacks or use end-to-end. The pitch is concrete: customers like Qualcomm and ShipBob report TCO savings of 70%, concept-to-production in roughly 30 days, and tens of thousands of employee hours saved per year. Industries served include financial services, engineering and manufacturing, and legal/professional services where hallucinations are unacceptable and documents are dense, multi-format, and full of jargon. Pricing is enterprise (book-a-demo), with no public self-serve tier.

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Key Features

Feature information is available on the official website.

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Pricing Plans

Enterprise

Contact sales

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    Best Use Cases

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    Customer engineering and technical support agents

    ⚡

    Deep research on IP, compliance, and prior art

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    Audit-ready requirements traceability matrices

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    Diagnosing errors in large device log files

    Pros & Cons

    ✓ Pros

    • ✓Research lineage: founded by Douwe Kiela, a co-author of the original RAG paper
    • ✓Composable APIs (parse, embed, rerank, generate, ground) let teams swap in only what they need
    • ✓Strong references in regulated industries (Qualcomm, ShipBob) with concrete ROI numbers
    • ✓Built specifically for dense technical and regulatory documents, not generic Q&A
    • ✓Cloud or VPC deployment for compliance-sensitive customers

    ✗ Cons

    • ✗No self-serve tier — every engagement requires sales contact
    • ✗Pricing opacity makes it hard to evaluate for smaller teams
    • ✗Overkill for general-purpose chatbot or simple knowledge-base use cases
    • ✗Implementation typically requires professional services support to hit the advertised outcomes

    Frequently Asked Questions

    How much does Contextual AI cost?+

    Contextual AI pricing starts at Contact sales. They offer a single pricing plan.
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    Quick Info

    Category

    Enterprise RAG

    Website

    contextual.ai
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