For SaaS founders
Build kickass AI-native products, without months of plumbing.
Why it stalls
What every team hits after the first demo.
Useful agents need real customer data. We give them access while keeping them inside your permission model.
One customer can blow the AI bill. We show you which tenant is driving it so you can act.
When your customers ask where the model runs, the answer is the EU, not a region you have to paper over.
A spinner is not a feature. We show what the agent is actually doing so users can follow the work.
Almost every agent needs a vector store. We chunk, index, sync, and scope your data to each tenant.
A second tool stack is the trap. Re-use your existing MCP to take the agent further without rebuilding tools.
The solution
Fency is a hosted API and webapp SDK you add to your product. It is fully whitelabel and built to enhance your product, not to become it. It does not dictate your UX, cost model, permission model, or how the product works. We only focus on solving the hard parts of integrating AI, so you can deliver more customer value, faster.
Use cases
Fency is not built for one feature. It is a versatile, scalable framework that grows with your product. These are only some of the use cases it covers.
Chat locked to your docs and each tenant's data. It says so when the answer isn't there.
Ask questions across their documents. Get answers with citations to the page.
Plain-language questions over records become numbers, charts, and a PDF report they can download.
Users describe the rows they need and get an Excel file built from their own data.
Upload a PDF or Word file. Validated fields land in your app, not a wall of prose.
Next-step proposals from what they just uploaded or typed, grounded in their data.
Nightly batches and long reviews run in the background. Webhooks when they finish.
Agents create and update records through your MCP tools. Approvals on destructive writes.
Semantic search over large corpuses, with citations. No vector store to build or babysit.