Contextual AI
Context engineering platform for building production-grade AI systems on trusted company knowledge.
What is Contextual AI?
Contextual AI is a context engineering and retrieval platform for enterprise AI applications. It helps teams build production-grade RAG systems that retrieve trusted company knowledge, cite sources, and support more reliable AI assistants and agents.
Tools for building, hosting, testing, observing, connecting, and giving memory or computer access to AI agents.
See the full Agent Infrastructure guide to compare more tools, buyer criteria, and related workflows.
Use cases to evaluate
Build RAG applications over internal documents and enterprise knowledge
Improve AI assistant accuracy with managed retrieval and context controls
Create source-cited answers for regulated or business-critical workflows
Give agents cleaner context before they draft, answer, or recommend actions
Fit to evaluate
Enterprises building AI assistants on proprietary documents and data
Technical teams that need governed retrieval instead of generic chat
Support, legal, finance, or operations teams with high-stakes knowledge workflows
AI builders trying to improve answer quality, citations, and context control
Business fit
Right for you if generic AI assistants are not reliable enough for internal knowledge work. Contextual AI is infrastructure for technical teams; scope the first project around one knowledge domain, clear evaluation examples, and data-access rules.
How to evaluate Contextual AI
Use this category when a business wants agents that do work across tools, APIs, browsers, and data sources.
Confirm the exact workflow
Map Contextual AI to one concrete workflow first, such as build rag applications over internal documents and enterprise knowledge. Avoid buying before the owner, trigger, output, and success metric are clear.
Check category fit
Compare tool-calling, memory, browser automation, evals, observability, and deployment controls.
Compare practical alternatives
Compare Contextual AI with other Agent Infrastructure vendors before committing to a contract or migration.
Validate cost and rollout effort
Contextual AI uses sales-led pricing. Evaluate pricing by data volume, retrieval workloads, deployment model, security requirements, evaluation needs, and support for production AI applications. Also confirm implementation time, support needs, and whether the technical setup matches your team.
Compare Contextual AI with alternatives
Use this quick comparison before booking demos or moving data into a new system.
| Primary workflow | Build RAG applications over internal documents and enterprise knowledge, Improve AI assistant accuracy with managed retrieval and context controls |
|---|---|
| Best-fit team | Enterprises building AI assistants on proprietary documents and data, Technical teams that need governed retrieval instead of generic chat |
| Implementation effort | Technical setup and maintenance profile |
| Pricing check | Contact sales |
| Closest alternatives | Other Agent Infrastructure tools |
Contextual AI pricing
| Model | Contact sales |
|---|---|
| Snapshot | Contextual AI uses sales-led pricing. Evaluate pricing by data volume, retrieval workloads, deployment model, security requirements, evaluation needs, and support for production AI applications. |
| Checked |
Common questions about Contextual AI
What is Contextual AI?
Contextual AI is a context engineering and retrieval platform for enterprise AI applications. It helps teams build production-grade RAG systems that retrieve trusted company knowledge, cite sources, and support more reliable AI assistants and agents.
What is Contextual AI used for?
Common use cases: Build RAG applications over internal documents and enterprise knowledge; Improve AI assistant accuracy with managed retrieval and context controls; Create source-cited answers for regulated or business-critical workflows; Give agents cleaner context before they draft, answer, or recommend actions.
How much does Contextual AI cost?
Contextual AI uses sales-led pricing. Evaluate pricing by data volume, retrieval workloads, deployment model, security requirements, evaluation needs, and support for production AI applications.
Who is Contextual AI best for?
Contextual AI fits Enterprises building AI assistants on proprietary documents and data, Technical teams that need governed retrieval instead of generic chat, Support, legal, finance, or operations teams with high-stakes knowledge workflows, AI builders trying to improve answer quality, citations, and context control. Right for you if generic AI assistants are not reliable enough for internal knowledge work. Contextual AI is infrastructure for technical teams; scope the first project around one knowledge domain, clear evaluation examples, and data-access rules.