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What is the Contextual AI platform?
The Contextual AI platform is a context layer for enterprise AI. You build agents that are grounded in your data and can retrieve information, reason over it, and take actions. Retrieval (search over your datastores, with citation) is a core capability of these agents, but they can also use tools (APIs, MCP, code execution), run multi-step workflows, and produce structured outputs. You build agents with Agent Composer (templates or custom workflows), connect data via datastores and connectors, and query via the UI or API.
How does it differ from generic chatbots or LLM APIs?
Unlike generic LLMs, Contextual AI agents are wired to your private knowledge and can act on it. They retrieve relevant content from your datastores, cite sources, and—depending on the workflow—can call tools, run multi-step research, or execute tasks. That keeps responses accurate and aligned with your docs and policies while enabling agents to do more than single-turn Q&A. Agent Composer lets you control the full path: retrieval, reranking, tools, and generation.
What are the core capabilities?
- Agents & Agent Composer — Build agents from templates (Basic Search, Agentic Search) or custom YAML/visual workflows. Agents combine retrieval (RAG) with multi-step reasoning, tool use (search, APIs, MCP), and cited or structured generation.
- Datastores & connectors — Ingest and sync content from local upload, GitHub, Confluence, Google Drive, SharePoint, Box, and more; incremental sync and published-content filtering where applicable.
- Parsing & chunking — State-of-the-art parsing for PDF, HTML, Markdown, and other formats; configurable chunking so agents can retrieve and reason over your content effectively.
- APIs & SDKs — Create agents, ingest documents, run queries, and manage evaluation/tuning via REST API and Python/Node SDKs.
- Security & governance — RBAC, audit trails, model armor, and compliance documentation (SOC 2, HIPAA, GDPR, etc.).
What does “context engineering” mean?
Context engineering is the practice of selecting, structuring, and delivering the right information to LLMs so that agent responses are accurate and grounded. On Contextual AI this includes: retrieval (search, rerank, filter over your datastores—a key part of every agent), orchestration (Agent Composer workflows, tools, conditional logic, multi-step actions), and prompt/config (system prompts, generation settings).
Use Cases & Industries
Which industries are best suited for Contextual AI?
Contextual AI fits industries with large knowledge bases, complex documents (e.g. PDFs, specs, manuals, logs), and/or strong compliance needs. Typical fits include:
- Engineering and manufacturing — Technical support agents, root cause analysis over device or process logs, and agents over manuals and specifications.
- Technical support — Documentation and support agents that answer from product docs, wikis, and runbooks with citations.
- R&D — Research agents over patents, trials, and internal knowledge; structured extraction and reporting.
- Financial services — Policy, research, and compliance-oriented agents over internal docs and regulations.
- Legal and professional services — Research and contract/document agents with citations and source control.
For when and why to use Contextual AI and how these map to workflows and templates, see Why Contextual AI.
What are typical use cases?
- Documentation & support agents — Q&A over product docs, internal wikis, or policy; agents retrieve and cite sources so users can verify and share.
- Device log & root cause analysis — Multi-step agents that ingest logs, correlate with specs and process history, and produce investigation reports with ranked hypotheses (enterprise templates).
- Research and internal knowledge — Agents over Confluence, Drive, or SharePoint for onboarding, planning, and decision support; multi-step retrieval and synthesis.
- Task execution & planning — Agents that predict failures, recommend maintenance, or produce plans with structured outputs (enterprise).
- Structured extraction — Compliance evidence collection, incident and postmortem analysis, and other extraction workflows with defined output schemas (enterprise).
- Custom workflows — Agent Composer for domain-specific retrieval, tool use (APIs, MCP), and cited or structured generation.
For the full set of workflows, template availability (self-serve vs. enterprise), and example use cases per workflow, see Why Contextual AI.
How quickly can we go from concept to production?
Self-serve users can create a Basic Search or Agentic Search agent and connect a datastore in minutes. Enterprises using connectors and custom workflows have reached production in weeks (e.g. on the order of 30 days for initial deployments). The platform is built for production: scale, observability, and security are included.
Data
What data sources can I connect?
You can ingest content via connectors (Confluence, Google Drive, Microsoft SharePoint, OneDrive, Box), local file upload, or the documents API. Supported content includes PDFs, Word, HTML, Markdown, and (for enterprise workflows) logs and structured data. Sync is incremental where supported so your agents stay up to date.
Does it scale to enterprise?
Yes. The platform supports large deployments: thousands of users, millions of pages ingested, and multiple use cases on one workspace. Datastores and agents scale with your data and query volume.
Do you train your models on our data?
No. We never use your documents, data, or interactions to train or fine-tune our underlying models. Your data remains isolated and is used only to power the AI agents you create within your own workspace.
Your data is processed solely for retrieval, reasoning, and real-time inference by your agents. It is not stored for model improvement, shared with other customers, or used outside your environment.
You do. Any agents you develop—including their configurations, behaviors, and outputs—are entirely your intellectual property.
No. We do not claim ownership over your agents or your data. You retain full rights to everything you create and upload.
Is our data shared with other customers?
Never. Each customer environment is isolated, and your data is not accessible to or used by any other organization.
Security & Governance
How is customer data handled and protected?
Customer data is processed in accordance with the customer’s agreement, and our Platform Services processes data for the benefit of the customer and their authorized users. We publish a Privacy Policy detailing how we collect, use, and share personal information.
Do you support enterprise governance and access control?
Yes. The platform supports role-based access control (RBAC), audit trails, enterprise authentication, and model armor to reduce misuse. Entitlements can be enforced at query time.
Does Contextual AI adhere to major security and privacy frameworks?
Yes, we design with security and privacy in mind (data residency, encryption at rest/in transit, access control) and provide documentation to support enterprise compliance needs. We comply with major data protection standards, including HIPAA, SOC 2, CCPA, and GDPR, with specific certifications varying by region and customer requirements. For more information, visit the Contextual AI Trust Center.
Getting Started & Pricing
How do I get started?
For a guided experience, request a demo. For self-serve, sign up at app.contextual.ai, create a workspace, then follow the getting started and platform (GUI) guides to create a datastore and agent (e.g. Agentic Search template) and run your first query.
Is there a free trial or credits?
Yes. New signups receive $25 in free credits ($50 with a work email) to build and query agents.
How is pricing structured?
Pricing is usage-based (queries, ingestion, compute, SLA). See the Contextual AI pricing page for plans, feature comparison, and component API pricing, or contact sales@contextual.ai for your usage profile and enterprise options.
What support do you offer?
We provide documentation, SDKs, APIs, onboarding guidance, and enterprise support SLAs. Professional services are available for production deployments.
Technical & Integrations
What languages and SDKs are supported?
You can use the REST API from any language (cURL, Python, JavaScript, Go, etc.). We offer official Python and Node.js SDKs. Agent Composer workflows are defined in YAML or built in the visual builder.
Can I deploy on-premises or in my private cloud?
Yes, our architecture supports flexible and secure deployment options, including fully managed cloud, private cloud, or customer VPC.
How does the Contextual AI platform scale for high-volume/real-time queries?
The Contextual AI platform is designed for production-grade reliability: auto-scaling compute for agents, vector store indexing, caching layers, and optimized retrieval pipelines to support high query volumes with low latency.
Company & Trust
Who founded Contextual AI and where are you based?
Contextual AI was founded in 2023 by Douwe Kiela and Amanpreet Singh and is headquartered in Mountain View, California, USA.
Who are some of Contextual AI’s clients or reference customers?
Contextual AI has enterprise clients across various sectors, including technology, financial services, and professional services. Some notable customers include Qualcomm, Advantest, NVIDIA, Comply, HSBC, ShipBob, Element Solutions, IGS, Sevii, and others.
What is Contextual AI’s mission?
Our mission is to replace the DIY complexity of building enterprise AI by providing a unified “context layer” so that AI is accurate, secure, scalable, and specialized to business knowledge and workflows.
How do you stay ahead in AI and maintain the trustworthiness of your agents?
We invest in research, model development (including grounded language models, rerankers), enterprise-grade engineering, and alignment to ensure the agents’ outputs are fact-based, cite sources when appropriate, and support audit and traceability. 
