Prompts and data leave the building
Every call to an outside AI service takes your data somewhere you cannot see.
Precepta · The control plane
Precepta sits in front of every model your organisation uses. Each request is checked against your rules, routed to the right model, answered from cache when it repeats, stripped of sensitive data and recorded. It runs on your own servers, and nothing leaves your network.
The problem
Every call to an outside AI service takes your data somewhere you cannot see.
Each team writes its own limits into prompts, which the AI can be talked around.
What was asked, what the rules said, what the AI saw — none of it can be pieced back together.
What it does
There is no way round it. Every call takes the same checked path, before and after it reaches a model.
Firewall → Sensitivity → Policy → Smart Route → Cache → Inference → Audit
A signed proof that nothing left, plus continuous checks that the exit really is closed.
Every request, decision and answer is recorded and linked. That is your answer when compliance asks what happened.
Limits per API key, and caps on cost and usage. They sit outside the model, so it cannot talk its way around them.
Set the model to auto and it picks the best one available, balancing quality, cost and speed.
Personal data is removed before the model sees it, and the answer is checked for leaks on the way back.
It speaks the OpenAI API, so you point your existing code at it and change the address. Nothing else.
How it runs
Not "we look after your data carefully" — there is nowhere of ours for it to sit. The models, the gateway, the rules and the records are all on your servers.
Runs inside your cloud account or data centre, with no public route. Nothing reaches us — there is no telemetry and no phone-home.
Including networks with no egress at all. The same artifact runs in both, so what you evaluate is what you operate.
Networks with no outside connection at all are what this was built for, not an awkward exception.
What being in the path is worth
Anything that sees every request can do more than check it. Precepta is already in that position, so the controls that make AI defensible also make it cost less to run.
Ask for auto and the smart router sends the request to the model that can
answer it inside your boundary. Not every question needs your most expensive one.
Deterministic answers are cached. A request served from cache is a request you did not pay to run again.
Prompts are compressed before they are sent, and every key carries its own scopes and spending caps. Nobody finds out about a runaway job from the invoice.
Who it's for
The people who have to answer for what the AI touched — and who already own the servers it should run on.
How we price it
Because nothing leaves your network, we cannot meter your usage remotely. Capacity is agreed up front and checked against your own logs. That comes from how it is built, not from a gap in our billing.
What you get
| Capability | Deployment |
|---|---|
| Llama 3.2 in-boundary, Smart Router, local Nomic embeddings, OpenAI-compatible API | Self-hosted · on-premises · air-gapped |
| Firewall, sensitivity, policy, PII redaction, output leak scanning, cache, audit | Self-hosted · on-premises · air-gapped |
| Signed sovereignty attestation, zero-egress verification via live probes | Self-hosted · on-premises |
| No public route, no telemetry, no phone-home | Self-hosted · on-premises |
We set it up on your own servers and your team picks the tests — including trying to get data out.
Every model call is now traced. Intent Studio governs what work they were doing in the first place.