AI systems for enterprise
Nexcor designs, builds and governs AI systems for organisations that cannot afford to be wrong in public. We integrate models into real operations, put enforceable boundaries around what they can see and do, and keep a record of every claim they make.
Assessment engagements start in two weeks. No model training required to begin.
Why this is hard
None of them is "the model wasn't smart enough." All three are engineering and governance problems — which means all three are solvable before launch.
Unreleased work, client records, strategy and IP get pasted into a chat window and leave the building. Policy documents do not stop this. Enforcement at the boundary does.
Invented figures, citations to sources nobody opened, test results that were never run. In a demo it is embarrassing. In a filing, a report or a customer communication it is a liability.
An agent changes a record, sends a message, signs a name. Without a per-action record and a human gate on consequential steps, you cannot audit it, defend it, or roll it back.
What we do
We do not sell a model. We build the layer between your data and a model you do not control — and we make that layer auditable.
Getting a model to do useful work against your actual systems, not a sample dataset.
Controls that run on every turn, not a policy PDF that runs on none.
Some material should never leave the room. We enforce that at the boundary.
An assistant that forgets is useless. One that confabulates is worse.
How we work
We do not start from a framework. We start from what actually goes wrong, and we build the mechanism that makes that specific failure impossible to repeat. If we cannot name the failure, we do not claim the control.
We establish what the system does today, on your data, before proposing a change. No baseline, no project.
Default-deny. Named grants. A human gate on anything consequential. The system earns capability; it is not given it.
Every claim is verified against the tools the turn really ran. Every figure must appear in a source. Disagreement is surfaced, not smoothed.
Per-turn, replayable evidence — what ran, what failed, what was flagged. Claims become auditable rather than remembered.
Evidence
These are method-level commitments, not marketing figures. Ask us for the underlying evidence on any of them in an assessment.
We will not ship a control we cannot demonstrate failing safely. We will not report a result we have not measured. And we will not let the thing that produced the work be the thing that judges it — including us.
Where we work
We are most useful where a wrong answer costs more than a slow one.
Engagement
Each stage stands alone. You are never committed to the next one.
We map where models already touch your data, what is exposed, and what would fail an audit today.
One workflow, bounded, with the guardrails and the record in place from day one.
Production deployment against your systems, with evaluation gates you define.
Ongoing monitoring, red-team cycles, and evidence packs for your auditors.
Start here
A readiness assessment is a fixed-scope, fixed-price engagement. You get a written finding: what is exposed, what is unverified, and what to fix first. If the answer is "nothing urgent," we will tell you that too.