We free the SAP data your AI can't reach.
We use AI with intent.
Both sides of AI. On purpose.
AI is on every homepage now. We didn't ship it as a badge — we built it where it changes the work, and so it keeps pace as AI moves.
The Design
AI changes weekly. Rebuilding a product doesn't.
In most software, an AI feature is hardcoded — wired to one model, one set of behaviors, frozen the day it ships. When the models move, the product falls behind until someone re-engineers it.
DataXover is built the other way. The rules the AI follows aren't buried in code — they're written, versioned documents the platform reads at the moment it runs. Change the document, and the behavior changes. No rebuild. No release. No redeploy. Every revision is versioned, every run records the version it ran under, and nothing the AI produces applies itself.
So when AI moves — a new model, a sharper way to do something — the platform keeps pace by publishing a revision, in minutes. Those rules are written per model, so it meets whatever model you run where it actually is. And they get better over time — refined from real use and testing, not frozen at launch.
Most AI features are a snapshot. This one is designed to move as fast as the field it depends on.
Enterprises don't fear AI that's occasionally wrong.
They fear AI that's confidently wrong.
The Architecture
Three owned inputs. The AI composes them. You decide.
The reason this can be trusted against a live system isn't a promise — it's structure. Every run stands on three inputs, and each has a clear owner. The AI reasons across all three and authors none of them.
You write the intent. Your system holds the truth. Our thesis sets the rules. The AI reasons within all three and authors none — you decide.
Discipline
Built to refuse, not to guess.
That fear is the whole reason the thesis exists. It does everything it can to keep the model from filling a gap with a plausible assumption and presenting it as fact.
The thesis sets hard limits on what the AI is allowed to propose. A proposal outside them isn't a preference the platform weighs — it's simply wrong, and it doesn't get applied. The rules are explicit, not implied. The platform validates every reply against its contract before it can even be imported — and nothing applies without a person.
When the objective is unclear, or depends on something that isn't actually there, the AI doesn't paper over it. It refuses — and tells you exactly what's missing. It's built not to invent intent from what it happens to see; reinterpreting your goal to make it fit is treated as a failure, not a convenience.
It reasons over what your system actually contains and cites what it read. Where it can't stand on cited ground, it stops and says so. That's the design — not a disclaimer — and a person is always the one who decides.
Built to refuse — rather than be confidently wrong.
Two more guardrails, by design. The AI layer is optional: data integration, B2B distribution, and compliance all run with it switched off. And the model is your choice — bring your own, from the providers we support, running on your own account.
The Pattern
One way of working, wherever the work used to require an expert.
Across the platform, the same shape repeats. You state your intent in plain words. The platform proposes a draft, or surfaces what you need to see. You review it and decide. And where it produced something, it checks that against your real data before anything is used.
AI proposes or clarifies. You decide. The system verifies.
What Changes
Same capability. A different person can now reach it.
How It Stays Trustworthy
AI proposes. People decide. The system verifies.
Nothing the AI produces changes your system on its own. Every proposal lands as a draft for a person to review and commit — active state moves only on a human decision, through the platform's ordinary controls.
And every proposal is checked against your live data. It's a starting point the platform tests, not an answer taken on faith. The AI makes the platform reachable; it never makes the platform's decisions.
Reachable to more people. Still under human control.
One approach, across the platform.
Wherever the work has been expert-only — compliance, configuration, monitoring — the same pattern brings it within reach, without giving up control of what actually happens. And the other side of the hero holds too: the SAP data your own AI has been starved of flows to it through DataXover, on your models, on your account.