The caseworker never switches tools: the agent lives inside the business application they already use and calls official reference data through verified tools.
6 Mjobseekers in the operator's scope
Public hospital
The use cases built by caregivers
Trained caregivers start from a pre-validated base and build their own use cases. IT keeps the path to production without being the bottleneck.
23healthcare use cases built across 15+ hospitals
State platform
A whole state's procedures, one message away
One platform for every ministry, citizen agents in local languages and voice notes, and the paper archives digitized behind them.
9 Mcitizens in the state platform's scope
We believe in a world where
Everyone who needs help can receive it.
Our solution
The Bayes Platform
The Bayes Platform gives any social or public organization the ability to deploy AI agents that strictly follow their own protocols, resources, and guardrails, not whatever the model picked up from the open internet.
Powered by the Bayes Platform — the only architecture purpose-built for public-service AI.
The problem
Generic AI wasn't built for public & social services
Millions of people depend every day on frontline professionals. But commercial AI wasn't built for them.
It doesn't know their protocols. It can't protect sensitive data. It hallucinates when it matters most.
Who we are
The leading nonprofit for AI in public services
We believe AI can help solve the capacity gap between the number of people who need help and those on the frontlines. This is why we partner with public services and NGOs to build AI agents that address societal needs. Founded in Silicon Valley in 2014.
The agent answers from your handbooks, protocols and official references: resource libraries, tagged documents, PDF import, a site indexed by crawl. Never from the open web.
02
Every answer cites its source
Every answer cites its source
The sources panel shows the document title and the extract used, so whoever reads the answer can check it. An answer nobody can verify has no place in a public service.
03
Your protocol, held by the platform
Your protocol, held by the platform
The agent's settings are your organisation's. Every reply has to report its own bookkeeping: session title, categories drawn from your list, sources cited. The platform enforces that when the model skips it.
04
Draft, review, publish
Draft, review, publish
Every change writes a draft. Publishing makes it the version that runs, named and dated in the history, and every reply carries its version badge. Nothing reaches the public that a human has not published.
05
Built by your own domain experts
Built by your own domain experts
Your frontline teams create and tune their own agents, with no code and no internal data team. Your IT keeps the path to production without being the bottleneck.
06
Tested before it goes live
Tested before it goes live
Evaluation campaigns replay conversation and extraction datasets against a chosen version. Reviewers rate the answers blind, an LLM judge gives the scale, and two runs compare side by side.
07
Watched in production
Watched in production
Every exchange is filed under the life situations you defined. The topics your answers cover badly surface on their own, instead of waiting for a complaint.
08
It plugs into what you already run
It plugs into what you already run
Each agent exposes itself as an MCP server, so the assistant or portal your institution already runs calls it as a tool. A widget on your site or a public page when that is simpler. Nobody changes screen.
09
Runs where you decide, open-source
Runs where you decide, open-source
Managed cloud, or your own infrastructure from a single Docker image. Document isolation is written into the retrieval query itself rather than a checkbox, the retention period is yours, and the code is MIT-licensed.
How we work
A secure platform. Deployable directly within the institutions that deliver impact on the frontlines.
We work alongside governments, public agencies and hospitals, and major nonprofits to build and deploy agents that actually work in the field, at scale.
