The reviewer that never sleeps
A reviewer that reads every regulation, contract and specification and flags where the actual work drifts from what you signed
What it solves
A clause that slips doesn't make noise when it slips. It surfaces later: in an audit, in a dispute, when an external reviewer asks about a line nobody had rechecked. By then, fixing it costs money, and proving you did things properly is hard.
The problem isn't any single check, it's the volume over time. Obligations come from regulations, contracts, specifications and policies that change, pile up, and sometimes contradict each other. No one can reread every specification each time a process changes, keep track of every version of a contract, and compare them all with what the team is actually doing.
This scenario is for anyone with a lot riding on a single line: quality, procurement, regulated sectors, anyone who has to prove they met an obligation, not just state it. An AI reviewer keeps the documents aligned and compares them with real processes and outputs, highlighting where they don't add up.
How it works
It reads the documents and extracts the obligations
The reviewer processes regulations, contracts, specifications and policies and extracts obligations and clauses in a structured form. Someone on the team checks the extraction and confirms what's actually binding, so the starting point is validated rather than left entirely to automation.
It compares the obligations with what happens in the systems
The reviewer checks the obligations against real processes and outputs: a progress status, a delivery that's out of spec, a step that got skipped. It does this every time a document or a process changes, not just when an audit falls due.
It flags the drift and cites the clause
When something drifts from an obligation, the reviewer opens a flag with a reference to the clause it comes from. The person responsible starts from that exact reference, judges whether it's a false positive or a real issue, and acts with the context already in hand.
Why it stays under control
- Auditable by design: every flag cites the clause it comes from, so you can trace the reasoning and show it to anyone who asks
- Inside your own systems: the reviewer reads the documents and the real processes you already have, not a cleaned-up copy in a separate environment
- Governed: the extraction of obligations goes through a person, and every flag stays traceable; no verdicts that come out of nowhere
What changes
- Obligations stop living in a few people's heads and become something the system keeps rereading, so the knowledge doesn't leave with whoever changes role
- Drift surfaces as it happens, not after the fact when fixing it costs more
- When an external reviewer asks about a decision, the reference clause is already there, on record
What we don't promise
- It doesn't replace legal or quality sign-off: it points you to where to look, it doesn't pass judgement on your behalf
- It doesn't close the case for you: what to do with a flag is for the person responsible to decide
- It doesn't guarantee full compliance: the quality of its flags depends on how complete the documents and processes you feed it are
Frequently asked questions
How can I trust a flag raised by the AI
It doesn't ask you to take its word for it: every flag carries the clause it comes from. The person responsible starts from that exact reference and checks for themselves whether it's a real issue or a false positive.
Does the AI make compliance decisions for us
No, it stays a control tool. It flags the drift and shows where it comes from, but the judgement and the decision remain yours, just like legal or quality sign-off.
What happens if a regulation or a contract changes
The reviewer rereads the updated document, re-extracts the obligations and checks them against the processes again. Before the update takes effect, someone on the team reviews the new extraction, so a version change doesn't trigger flags blindly.
Our documents are scattered across different versions, is that a problem
That's the normal situation; we find it almost every time. In the Inception package we start from a narrow slice of scope precisely to see what your documents look like and whether the extraction holds up, before extending it to the rest.
At a glance
Tags
Does this scenario sound familiar?
We start with an Inception package to see, on your actual case, whether and where it makes sense.
Book a callTell us where you're stuck
A fragile prototype, a burdensome legacy codebase or unpredictable delivery: that's where we start