Hebbia launched its API and MCP server on January 26, 2026, letting firms embed the platform's retrieval and analysis capabilities directly into custom applications and existing AI assistants. The move targets enterprise teams that need their own tools to reason across documents, emails, databases, and internal systems - without switching to a separate interface.
Every firm sits on knowledge scattered across systems. Even strong models fail without the right context. Getting that context into an application usually means finding sources, mapping relationships, coordinating multi-step analysis, and verifying outputs. That chain of work is what Hebbia has focused on automating.
How retrieval works at scale
Hebbia processes a question step by step, pulling in evidence as the analysis unfolds. Developers can test the workflow, and users can trace every claim back to its source material. The system depends heavily on relationships between sources. A question about a company might hinge on its subsidiaries, past transactions, or notes filed under a different name. Hebbia works with customers to map those connections and account for internal terminology, so retrieval catches material a simple name search would miss.
As analysis progresses, Hebbia supplies relevant evidence to each model call. This lets an application work across a large document collection while using smaller context windows where appropriate.
Applications built with the team
A banking team might want an origination tool that traces a newly announced deal to downstream effects on its coverage universe. Hebbia can build an application that triggers on the announcement, draws on research, market maps, and internal notes, then surfaces the right client to call, the reason to call now, and the pitch to make. Every claim links to evidence the banker can review.
A professional services team advising on an acquisition needs to understand the cost of integrating the target's technology. Hebbia can connect system records, contracts, and workshop notes to identify consolidation opportunities and integration risks, with supporting evidence available in the firm's own client portal. The company works with each team to define the analysis, connect the relevant information, and build the application around its process - including how the firm interprets its records and where people need to review results.
Connecting existing AI tools through MCP
Through the Model Context Protocol server, assistants such as Claude and ChatGPT can call Hebbia to retrieve and analyze a firm's connected information, returning answers with supporting evidence. The choice of model and interface stays with the firm. This means teams can keep using the assistants they already know while Hebbia handles retrieval and analysis in the background.
For professionals building internal tools or looking to make existing AI assistants work better with company knowledge, the API and MCP server open a direct path. The approach mirrors broader trends in AI Agent Courses, where the focus has shifted from standalone chat interfaces to embedded, multi-step workflows that connect directly to business systems.
Why this matters for operations and knowledge workers
For people in sales, insurance, real estate, HR, and professional services, the bottleneck is rarely the AI model itself - it is getting the right information into the prompt at the right time. Headless Hebbia addresses that by letting teams build retrieval and analysis into the tools they already use, with every output traceable to a source. A sales team preparing for a client call does not need to search across five systems and cross-reference notes manually. The application does that work and shows its reasoning. For operations leads managing vendor contracts or integration projects, the same principle applies: evidence-backed answers, available through the firm's own portal or assistant.
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