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What is the Contextual AI platform?
The Contextual AI platform is a context engineering layer for enterprise AI, enabling organizations to build specialized, production-ready AI apps grounded in their organization’s data in days, not months.
Unlike generic LLMs, our platform provides a suite of state-of-the-art (SOTA) context engineering tools that connect your private knowledge base (documents, system logs, workflows) with any LLM. This ensures the agent’s responses are grounded in your business context and helps eliminate hallucinations, thereby improving trust in the outputs.
Key capabilities include:
- Ingesting/unifying documents into vector stores
- Parsing and chunking unstructured data
- Building specialized AI apps with RAG pipelines
- APIs and SDKs for programmatic use
- Enterprise-grade security, scalability, and governance
- AI and system observability, and data management
What does “context engineering” mean?
Context engineering is the discipline of selecting, structuring, and delivering the right information to LLMs in order to deliver accurate, grounded responses. This includes:
- Information retrieval – Finding and preparing relevant data from enterprise sources
- Context optimization and management – Shaping prompts, guardrails, and agent behavior
- Workflow/agent orchestration – Connecting multi-step processes and enterprise systems
Use Cases & Industries
Which industries are best suited for Contextual AI?
Industries with large complex knowledge bases that use documents with rich media (e.g., charts, images, tables) in challenging file types (e.g., PDF, HTML, Markdown), and/or compliance/regulatory demands: financial services, engineering/manufacturing, legal and professional services.
Can you give examples of typical Contextual AI use cases?
Some examples include:
- Technical support agents who answer user queries with responses grounded in vast documentation.
- Policy & procedures agent for employees: making institutional knowledge accessible via chat queries.
- Internal knowledge agents for onboarding, reducing time-to-productivity of new hires.
How quickly can an enterprise go from concept to production?
Our context layer and platform capabilities enable the creation of production-ready apps with high accuracy out of the box, which removes the complexity of integrating and optimizing the accuracy of a DIY alternative.
Enterprises using our platform have significantly reduced their concept-to-production timelines (e.g., as low as ~30 days for initial production deployments) in some cases.
Data
What types of data sources can I connect?
You can ingest any source of enterprise content that you wish your agents to reference, including documents (PDFs, Word, HTML), logs, databases, knowledge bases, internal wikis, and cloud storage.
Can Contextual AI scale to my enterprise’s requirements on an ongoing basis?
Yes. Our platform is built for enterprise-scale deployments, offering state-of-the-art context engineering and production-grade agent capabilities.
Contextual AI customers routinely support:
- 2,000+ internal users
- 10,000+ support cases handled
- Millions of pages ingested
- Public-facing apps in production
- Multiple use cases on one unified platform
If your enterprise requires it, our platform can scale to meet your specific needs.
Yes, supported data sources include Google Drive, Microsoft SharePoint, Microsoft OneDrive, Box, and Confluence, with more being added continuously.
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-grade governance and access controls?
Yes, the Contextual AI platform supports role-based access control (RBAC), audit trails, enterprise authentication, respected entitlements at query time, and model armor to prevent agent misuse.
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 with Contextual AI?
For a guided experience, you can request a demo. For self-serve, you can sign up, create a workspace, ingest data (documents, logs, other sources), create an agent, and start querying via the UI or API. Our Beginner’s Guide walks you through steps such as API key creation, datastore creation, and querying.
Is there a free trial or credits available?
Yes, when you sign up, you’ll get $25 ($50 with a work email address) in free credits to explore the platform and build your first agent.
How is pricing structured?
Pricing is based on usage and varies by volume of queries, data ingress/egress, compute, and SLA. For more information, please refer to the Pricing & Billing page or contact [email protected] to request your usage profile and enterprise plan.
What support options are available?
We provide developer documentation (SDKs, APIs), onboarding guidance, enterprise-grade support SLAs, and professional services engagements to accelerate production deployments.
Technical Details & Integrations
Which programming languages and SDKs are supported?
Contextual AI is available via API: cURL, Python, JavaScript, PHP, Go, Java, Ruby or via SDK: Python or Node.js.
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 are the founders and where is the company based?
The company 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, ClaimWise, Advantest, Comply, HSBC, ShipBob, Element Solution, 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. 
