AI decision benchmarking for regulated enterprises
Benchmark AI
before you trust it.
Authentia runs agents beside your experts, benchmarks every decision against theirs, and measures regulatory alignment — so autonomy is granted on evidence, not trust.
Senior credit officer — mitigation verified.
One decision, benchmarked — the entire product in one card
IBM watsonx partner
Provisioned and running end-to-end on IBM watsonx today.
watsonx.ai
reasoning
watsonx Orchestrate
orchestration
watsonx.data
enrichment
watsonx.governance
audit & guardrails
The problem
AI is making regulated decisions.
How do you know when it deserves autonomy?
Vendors ask you to trust their agents. Regulators ask you to prove your decisions. Between those two demands sits a gap no demo closes — only evidence does.
Authentia sits at the decision layer and replaces nothing. You're not migrating systems — you're benchmarking decisions.
Authentia runs agents beside your experts.
Every decision is benchmarked.
Every disagreement is explained.
Every action is auditable.
Built for regulated decisions in
The product — human × agent
Your experts are the champion.
Our agents are the challenger.
Champion/challenger is how risk teams have always proven a new decision strategy. Authentia applies it to AI: a bench of agents decides in shadow beside your experts, and every agreement, every drift, and every compliance delta becomes evidence — including which agent has earned which decision.
See the drift
Every human–agent divergence is surfaced, explained, and traced to its source — a threshold, an appetite call, or missing context.
Bench the agents
Not one model — a bench. Every case is scored across several reasoning agents from the watsonx.ai catalog, and the gauges show which one decides most like your best people, decision type by decision type.
Close the gaps
Tune the guidelines and teach the agents how your institution actually decides — with evidence from your own cases, not anecdotes.
Widen the mandate
An A/B test of your decision bottlenecks, not a big-bang migration. Nothing changes hands until you decide it should.
Trust here is a number on a board — and you set the bar it has to clear.
Approved with conditions — long-standing client, mitigation verified by the senior credit officer.
> refer · policy §4.2 · concentration threshold exceeded
bench · gpt-oss REFER · granite REFER · mistral REFER · llama APPROVE
Drift source: threshold interpretation. Your team approves with verified mitigation; the agents read §4.2 strictly. Seven similar cases this quarter — the policy and the practice disagree.
Agents decide in parallel. Nothing changes hands.
Agent drafts, your specialist signs every case.
Earned per segment. Dialed back any time.
Click a case, or open the agent bench — sample data from a 30-day shadow run
Benchmarking engine
Every decision runs the gauntlet.
Evidence comes out the other side.
Five stages, every case, continuously. Deterministic where it must be, agentic where it pays — and audit-ready at every step.
· Same case, same rules, every model on the bench — that's what makes it a benchmark
Adoption
Prove it in shadow.
Adopt at your pace.
A path you can stop at any step and still be better off. From first shadow run to autonomy, a person is in command the whole way.
same case, parallel decision — zero customer impact
Compliance
Built for the examiner
as much as the operator.
Governed AI
Agents operate inside guardrails you set — never beyond the mandate you’ve granted.
Explainability
Every decision ships with its reason, in language your reviewers can read.
Auditability
A complete, tamper-evident trail — the examiner’s evidence exists before the question.
Regulatory alignment
Decisions scored continuously against your policies and the rules that govern you.
Grant autonomy on evidence,
not trust.
See your own decisions benchmarked — human beside agent, compliance measured, drift explained. What the agents take on next is decided by their record, and by you.