Contextual AI

What is Contextual AI?

Contextual AI is a platform for building AI agents that answer questions from a company's own technical documents, specifications, manuals and logs. Teams create an agent from a template, connect or upload their files, and then ask questions and get answers with citations. Self-serve accounts start with Basic Search and Agentic Search templates, while Enterprise adds a visual workflow builder plus extra templates: root cause analysis, deep research, executing tasks and structured extraction. Developers who already run their own retrieval setup can instead call four component APIs: Parse for document understanding, Rerank for instruction-following reranking, Generate for grounded answers, and LMUnit for natural-language evaluation tests. The vendor says it never trains on customer data and that agents built on the platform remain the customer's property. Pricing is usage-based: an On-Demand option starts with $25 in free credits, the component APIs are billed per page or per million tokens, and Enterprise is custom.

Use cases

• Search technical documents with citations
• Analyze device logs for root causes
• Answer technical support questions
• Rerank retrieved passages in a RAG pipeline
• Parse PDFs, tables and figures
• Evaluate LLM answers with unit tests
• Extract structured data for compliance

Who it's for

• R&D teams
• AI engineers
• Technical support teams
• Enterprise knowledge teams

Why use Contextual AI

• Answers come with citations, so reviewers can check them against the source documents.
• Self-serve templates let a first agent run in minutes without writing code.
• Component APIs can be dropped into an existing RAG stack without rebuilding it.
• Usage-based billing with free starting credits avoids a fixed commitment.
• Customer data is not used for training, according to the vendor.

Contextual AI features

• Basic Search template for single-turn Q&A with citations
• Agentic Search template with iterative retrieval
• Document upload, datastores and connectors
• Parse API for text, charts, tables and code
• Rerank API steered by natural-language instructions
• Generate API with in-line source attributions
• LMUnit API for natural-language unit tests
• Python SDK and API access
• Enterprise visual workflow builder and RBAC

How to get started with Contextual AI

1. Sign up and create a datastore and an agent from the Basic Search or Agentic Search template.
2. Connect document sources or upload PDFs, specs, manuals and logs.
3. Ask questions and review answers with citations.
4. Refine the agent, or call the Parse, Rerank, Generate and LMUnit APIs from your own code.

Contextual AI limitations

• Root cause, deep research, task execution and extraction templates need an Enterprise engagement
• The visual workflow builder is an Enterprise feature

Contextual AI pricing

Yes, Contextual AI has a free plan. Paid plans start at $0.02 usage-based, according to the official pricing page on 24 Oct 2026.

Contextual AI plans

• On-Demand

  Usage-based

  pay as you go

  • $25 in free credits to start
  • No commitments or minimums
  • Basic Search and Agentic Search templates
  • Python SDK access

• Parse API

  $3 /1,000 pages

  Basic (text only); Standard (multimodal) is $40 / 1,000 pages

  • Document understanding pipeline

• Rerank API

  $0.05 per million tokens

  Rerank-v2; Rerank-v2-mini is $0.02 per million tokens

  • Instruction-following reranker

• Generate API

  $3 /1M input tokens

  Output is $15 / 1M tokens

  • Answers grounded in supplied knowledge

• LMUnit API

  $3 /1M tokens

  Input tokens

  • Evaluation model for natural-language unit tests

• Enterprise

  Custom

  • Guaranteed throughput and SLAs
  • VPC deployment
  • Dedicated support
  • Advanced agent templates and workflow builder

Plans as listed on the official pricing page on 24 Oct 2026. Prices can change and may vary by country or exclude tax. Check the official pricing page.

Contextual AI alternatives

The main Contextual AI alternatives listed on Debuly are Cohere, Vectara, Voyage AI, Glean, LlamaIndex and Unstructured. The table compares pricing and platforms; order is A–Z.

1. 

   Cohere

   Enterprise AI company offering Command, Embed and Rerank models by API, the North agent platform and private deployments.

   Developer platformsResearch and searchAutomation and agents
   FreemiumVisit Cohere (official site)
2. 

   Glean

   Enterprise AI platform that searches company apps, answers questions with permissions respected, and runs agents on your own data.

   ProductivityResearch and searchAutomation and agents
   Contact salesVisit Glean (official site)
3. 

   LlamaIndex

   Open-source framework plus the LlamaParse platform for parsing, extracting and indexing documents so LLM apps can use them.

   Developer platformsResearch and searchAutomation and agents
   FreemiumVisit LlamaIndex (official site)
4. 

   Unstructured

   Turns PDFs, Office files, emails and images into clean, chunked, embedded data that is ready for LLM and RAG pipelines.

   Developer platformsAutomation and agentsProductivity
   FreemiumVisit Unstructured (official site)
5. 

   Vectara

   Agentic AI and RAG platform that ingests enterprise data and answers with grounded, traceable responses.

   Automation and agentsDeveloper platformsResearch and search
   Free trialVisit Vectara (official site)
6. 

   Voyage AI

   Embedding models and rerankers, billed by usage, for search and retrieval-augmented generation pipelines.

   Developer platformsResearch and search
   FreemiumVisit Voyage AI (official site)

Contextual AI FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from contextual.ai · fetched 24 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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