The cost of ungrounded AI
In low-stakes work, a confident guess is annoying. In high-stakes work, it is the whole problem.
Confident hallucinations
The model states a wrong answer with the same certainty as a right one. You cannot tell them apart until it is too late.
No audit trail
When it matters who knew what and when, there is nothing to point to. The reasoning lived in a chat window and is gone.
Context evaporates
What it learned last session is lost this session. Every conversation starts from zero, and so do the mistakes.
No learning
The same wrong turn gets taken again next week. Nothing accumulates, nothing improves.
Software engineering
This is the domain we cracked. A coding agent running on Empirica keeps a track record, has to understand before it acts, and gets its confidence checked against real evidence: did the tests pass, were the goals met. The result is an agent you can hand real work, because you can see exactly what it did and why. Everything below is the same discipline, pointed at higher stakes.
Built for the critical domains
The discipline that makes a coding agent trustworthy is not specific to code. It is exactly what these domains have been waiting for, and where we are taking it.
Legal and compliance
A hallucinated citation is malpractice. A missed requirement is a fine.
Research and review where every claim has to trace back to a real source, and the trail has to survive an audit.
Finance
A confident wrong number is a loss, or worse.
Analysis and reporting where the model has to show its work, flag what it is unsure of, and never paper over a gap with a plausible guess.
Healthcare
An ungrounded recommendation is a risk to someone.
Decision support where evidence quality is tracked, uncertainty is stated plainly, and the system refuses to act past what it actually knows.
Safety and audit
Reviewing AI you cannot inspect is theatre.
Red-teaming and oversight that need a record of what an AI knew, assumed, and decided, not a summary written after the fact.
One requirement runs through all of them: you have to know what the AI knew before it acted. Not after, not from a chat log, but from a record you can audit.
Your practices are training data
Every practice produces something rare: calibrated, grounded reasoning with a full trail of what was known, assumed, and checked. That is exactly the kind of data you need to train a model that pushes back on nonsense instead of confabulating its way past it.
We have the approach, and a way to measure whether it works: a benchmark that scores how well a model resists confidently wrong prompts, comparing a base model against one trained on Empirica's reasoning traces. It improves grounding. It doesn't magically eliminate it, and we won't pretend otherwise. This is research, and it is real.
Beyond the product, this is Foundation: a consent-clean corpus of calibrated reasoning traces, and open-weights models trained on them, meant to belong to the people who build it. The goal is AI that pushes back when grounding fails instead of confabulating confidence. It is the point of all of this, and it is research-stage today.
Work in a domain that cannot afford a guess?
Tell us about it. We will show you what grounded, auditable AI looks like for the work you actually do.
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