They share one memory, so nothing is said twice
Three layers: directive, orchestration, execution
An agentic assistant that answers from your own material instead of guessing. It cites what it used, and it says it does not know rather than inventing something plausible.
The SOP, written down. Goals, inputs, the tools to use, the outputs and the edge cases — in plain language, versioned like code. When we learn an API limit the hard way it gets written here, so it is never learned twice.
The only probabilistic layer. It reads the directive, calls the tools in order, handles the errors and asks when something is genuinely ambiguous. It does not do the work itself.
Deterministic scripts. Same input, same output, every run. Ninety per cent accuracy per step is fifty-nine per cent over five, so anything that must be right lives down here instead.
The gap report is the part clients end up valuing most. Every question the corpus could not answer is a content brief you did not have to guess at — the assistant tells you what your material is missing.
Also connects to
Read-only wherever read-only is enough. Nothing gets write access it doesn't need, and every write is logged.
Every answer is traceable to retrieved passages — no ungrounded generation.
Below the confidence floor it refuses instead of guessing.
Unanswered questions are logged rather than silently dropped.
That consistency is the whole point. The same checks and the same artifacts on every run, so when a number moves you know it moved because your site moved — not because the method did.
| Spec | Standard | Deployable |
|---|---|---|
| Where it runs | Our infrastructure | Yours |
| Who holds the API keys | Us, scoped per client | You |
| Cadence | Monthly, or weekly | Any schedule |
| Raw data retention | Rebuilt each run | Your policy |
| Artifact delivery | Your Drive and tracker | Your choice |
| Source code access | — | Full |
| Runs unattended | Yes | Yes |
| Failure alerting | Slack and email | Your channels |
Deployable hands over the software, the hosting and the keys. You run it; we stay on for support.
A RAG system is an assistant that answers from your own documents instead of from general training data. Retrieval-augmented generation means it finds the relevant passages in your material first, then answers from them, and cites what it used.
It says so. The system is built to decline rather than produce a plausible answer it cannot ground in your material, because a confident wrong answer costs more to undo than no answer costs to escalate.
Wherever you choose. On Deployable the system runs inside your own infrastructure with your own keys, so your documents never leave your environment.