A model that has never been attacked is a model that has never been tested. We evaluate capability and adversarially probe for failure before any weights go near production.
General benchmarks are necessary but insufficient; sovereign finance demands domain-specific proof.
Models are scored against evaluation data screened to be absent from training, so results reflect generalization rather than memorization. Contamination checks from curation make these numbers meaningful.
Beyond general benchmarks, models are tested on financial, legal, and regulatory reasoning tasks representative of real operator use. Performance is judged on the work the model will actually be asked to do.
We assess whether the model's confidence tracks its correctness, since a confidently wrong answer in settlement or policy is a hazard, not a rounding error. Miscalibration is treated as a defect to be surfaced, not tolerated.
Every evaluation is pinned to a fixed model checkpoint, a fixed test set, and a recorded harness. Scores are re-runnable and comparable across model versions rather than one-off impressions.
Factual reliability is evaluated as a first-class property because the cost of hallucination here is measured in policy and money.
Responses are tested for whether claims are supported by provided sources rather than invented. The model is probed specifically on its tendency to assert numbers, citations, or rules it cannot substantiate.
Tasks that involve figures, rates, and calculations are checked for arithmetic and transcription accuracy. A model that reasons well but reports the wrong number is failed on that basis alone.
We test whether the model declines or flags uncertainty when it lacks the information to answer, instead of confabulating. Appropriate refusal is scored as correct behavior, not as a shortcoming.
Factuality metrics are tracked across versions so a new checkpoint cannot silently trade truthfulness for fluency. A regression here blocks promotion regardless of other gains.
Adversarial testing is a structured campaign to make the model misbehave under pressure, not a spot check.
The model is subjected to injected and hidden instructions embedded in inputs and documents to test whether it can be hijacked away from its task. Financial workflows ingest untrusted text, so this is a core threat, not an edge case.
Structured attempts are made to bypass the model's guardrails and elicit prohibited outputs. Successful bypasses are logged as findings and drive alignment work before any deployment.
The model is probed for whether it can be induced to regurgitate sensitive or memorized training content. Leakage of governed source material is treated as a residency and confidentiality failure.
Red teamers construct deceptive financial and legal scenarios — misleading figures, contradictory instructions, edge-case instruments — to find where reasoning breaks. The goal is to surface failure modes on the terrain the model will operate in.
The output of this stage is evidence an owner and an auditor can inspect.
Every material failure from evaluation and red-teaming is recorded with its trigger, severity, and reproduction. The register is the shared artifact between engineering, alignment, and the owner.
All results are bound to a specific hash-addressed model version so there is no ambiguity about what was tested. A claim about a model's safety refers to an exact artifact, not a moving target.
Findings feed back into alignment and, where needed, data curation and retraining. A model is not patched around a failure; the failure is addressed and the model re-evaluated.
The evaluation and red-teaming record is submitted as evidence to the mandatory approval gate. No model advances on capability scores alone while adversarial findings remain unresolved.
Talk to us about evaluation and red-teaming in a sovereign deployment.