Enterprise AI that reaches production, without the rebuild.
What stalls enterprise AI is rarely the model. It is the assumption that going live means replacing the systems the business already runs on. We attach intelligence to what you have, and we make the approval step something you can point at.
- A governed path into your systems of record. Read first, and write only where a named person approved it.
- Every output proposed by an agent, checked independently, then cleared by someone with the standing to refuse it.
- One process proven end to end before anything is allowed to become a programme.
Book a governed AI review.
Bring one use case that stalled. You leave with a scoped next step and an honest read on whether it is ready to go anywhere near production.
Adopting AI is easy. Trusting the output is not.
Three things stop enterprise AI at the pilot boundary, and none of them is the model.
Production is quoted as a replacement programme
The pilot works, then someone asks what it takes to run it properly and the answer arrives as a platform migration. The business declines, correctly, and the pilot quietly ends.
Everyone is accountable, so no one signs
Risk, legal and the business each hold a veto and none holds the pen. Without a named person who can clear an output, there is no route from a working demo to a live process.
Governance exists as a document, not as evidence
When the board asks what checked this, a policy is not an answer. What settles the question is a record of what was produced, what verified it, and who cleared it.
An agent can propose anything. Only a person can greenlight it.
Nothing is trusted on assertion.
Greenlight is not a trust adjective we apply to our own work. It is a step in the work, and it leaves a record.
The agent does the work
Grounded in your systems of record rather than in general knowledge, so what it produces is about your business and not about the internet.
AgentSomething independent checks it
Checked against the standard the output has to meet, by a path that is separate from the one that produced it. A check the author runs on itself is not a check.
IndependentA person clears it, or refuses
Someone with the standing to say no signs it off, and that decision is recorded with what it was based on. This is the part you point at when you are asked.
HumanA retrieval layer, not a new home for your data.
Your ERP stays your ERP. Nothing moves house for this to work.
Governed AI, in an environment that audits everything.
Contract intelligence where every output is reviewed
Equalis runs cooperative purchasing for public agencies, which means procurement documents that get read by people with the power to challenge them. AI drafts and assists on supplier scoring, and a person reviews every output before it counts. Read the story.
Changing engines without the service blinking
Blackwoods moved decades of operational history onto a new ERP backbone, phased so that the business kept trading throughout. The reason it is here: this is the kind of estate AI has to attach to. Read the story.
Score your own use case in three minutes
Ten questions across the five dimensions that decide whether an agent is ready for production. Verification scoring zero caps the result, whatever else is in place. Open the scorecard.
Thirty minutes, and three things you did not have before.
This is a working session, not a discovery call that ends in a proposal you did not ask for.
- Why it actually stalledAn outside read on the specific reason your use case stopped, which is usually the approval path or the integration boundary rather than the model.
- The governed version of itWhat the same use case looks like with the verification and sign-off steps in it, and what that changes about who has to agree.
- A scoped next stepThe smallest piece of real work that would prove or kill it, with what it takes to run. If the answer is that you are not ready, we say that.
What leaders ask on the first call.
It stays where it is. The agent reaches into your systems of record through a governed access path and works from what it reads there, which is why this does not start with a migration. Anything that leaves that boundary is a decision you make explicitly, not a default we ship.
A named person with the standing to refuse. What they are clearing is a specific output against a specific standard, with the verification result attached, so the decision is a judgement on evidence rather than on the general reputation of the system.
It gets caught at the verification step, which exists precisely because models are confident when they are wrong. The output does not reach the process, the failure is recorded, and the pattern feeds back into what gets checked. A system with no wrong answers has no verification, it has optimism.
Almost never. The blocker is a governed way in and out of the systems you already run, not the systems themselves. We start read-only and treat write access as a separate decision with a named approver.
You do, throughout, and that holds whether we build one use case or run a standing function for you. It is a structure, not a stage in a handover plan.
Pick one process, and let the evidence decide the rest.
Thirty minutes on the use case that stalled. No deck, no discovery phase, and no obligation at the end of it.