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InlayAI

Use cases

Different products. Shared foundations.

Your users need an outcome, not an orchestration framework. Build the experience around the work they need to do, with Inlay underneath.

01 / Product pattern

A copilot inside your product

A planning workspace that builds a useful document, not just a reply.

Bind live outputs and tables to your interface. Let the agent call your app's tools and remember user context between conversations.

Typed clients / live state / client tools

Connect an agent to your UI

02 / Product pattern

A research product with sources

A research assistant that works from a customer's own knowledge base.

Combine retrieval with web tools, assemble a structured result, and surface the supporting passages. Inspect and evaluate retrieval separately from the final prose.

Retrieval / citations / structured outputs

Build with your knowledge

03 / Product pattern

Work that waits for a decision

An outreach draft or proposed support resolution that a person reviews.

Present a client-tool approval in your app. Reopen a saved pending decision, request a revision, and explicitly resume with the user's answer.

Client tools / saved pauses / history

Add a human review step

04 / Product pattern

An assistant on a schedule

A recurring research brief that is ready when your user starts the day.

Schedule non-interactive agents with a timezone and user identity. Use scoped memory and inspect each resulting run. Keep interactive approval flows separate.

Schedules / memory / observability

Schedule the work

These are illustrative product patterns, not customer case studies. You supply the interface, permissions, and domain integrations. Inlay supplies the agent infrastructure underneath.

Turn the experience into an offer.

Add plans, credits, and hosted payments to your own product.

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Build the product only you can build.

Tell us who it's for and what the agent needs to do. Inlay is in private beta, with access reviewed and onboarding handled manually.

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