LLM Assurance
LLM Assurance tests guardrails, retrieval context and response behavior before release, on every change and in production — for lenders, servicers and insurers, and for models you build or buy.
The problem
A language model is not a release you control line by line. Its behavior moves when the model is upgraded, when a prompt is edited, when a policy document is replaced — and it moves silently.
A vendor model update or a refreshed knowledge source can change what your assistant tells a borrower or a policyholder, with no code change to review and no test that would have caught it.
Spot-checking a handful of conversations tells you how the system behaved once. It tells your risk function nothing about how it will behave next week.
In mortgage, Freddie Mac Bulletin 2025-16 and Fannie Mae Lender Letter LL-2026-04 require oversight of AI wherever it appears. In insurance, the NAIC AI Model Bulletin, adopted by many states, expects insurers to govern AI systems, including those from vendors.
What we test
Every scenario traces back to the clause of your specification it tests, so a failure is a finding against a requirement — not an opinion about a transcript.
Guardrails
It stays inside the topics it is allowed to discuss, and stays out of advice it must not give — rate quotes, eligibility or coverage promises, legal or tax guidance.
Guardrails
It declines what it must decline, and hands off to a person where it is supposed to — complaints, hardship, disputed claims — every time, not most of the time.
Security
Adversarial inputs that try to override its instructions, and requests that try to extract another borrower's data or the system's own configuration.
Context
The right passages from the right, current source are retrieved for the question asked — not a superseded guide or a neighbouring product's policy.
Context
Answers are supported by the retrieved context, and say so when the context does not contain the answer instead of filling the gap.
Completeness
It never quietly drops a fact the response is required to carry — the disclosure, the deadline, the condition that changes the customer's next step.
How it works
The system under test is non-deterministic — which is precisely why the harness must not be. Scenarios, thresholds and verdicts are versioned, so any run can be reproduced and compared.
Specify
Your business authors what the AI must and must not do — topics, refusals, handoffs, required facts.
Build the suite
The specification becomes reviewed scenarios and agreed thresholds, each traced to the clause it tests.
Run on every change
A model, prompt, knowledge or rule change triggers a full re-run and a diff against the baseline.
Learn from production
Agreed signals are monitored; reviewed findings become new regression scenarios.
Three moments that matter
Before release
The full suite runs against the release candidate. Results are reviewed against the thresholds your business agreed, and the baseline is recorded — what "good" looked like on the day it shipped.
On every change
A model version, prompt, knowledge source or rule change triggers a re-run with a diff against the baseline — so you see what moved, and decide, before your borrowers do.
In production
Agreed quality and safety signals are monitored, alerts are investigated, and reviewed findings are fed back into the suite — so it gets stronger every time something surprises you.
What you get
Straight answers
Test scenarios, customer-like data and results stay inside your boundary. Where an automated evaluator is used, it can run against a private or self-hosted model, and it is calibrated against human review before anyone relies on it.
Repeatable tests do not make a language model deterministic, and no test suite guarantees full accuracy. What assurance gives you is a measured, versioned picture of behavior — and a defensible answer when someone asks how you know.
Questions buyers ask
Testing and evaluation for the language models in mortgage and insurance products. It tests guardrails, retrieval context and response behavior before release, on every model, prompt or knowledge change, and in production — for AI you build or buy.
Permitted topics, refusal and handoff behavior, prompt injection and data leakage, retrieval relevance, grounded responses, and whether required facts and disclosure language are present.
The test harness is deterministic even though the model is not. Scenarios, thresholds and verdicts are versioned and agreed before the run, every verdict traces to a clause in your specification, and any automated evaluator is calibrated against human review.
Yes. The GSE requirements in mortgage and the NAIC AI Model Bulletin in insurance both expect oversight of vendor AI, and LLM Assurance can evaluate AI systems you bought as well as those you built.
No. Repeatable tests do not make a language model deterministic or guarantee full accuracy. Assurance gives a measured, versioned picture of behavior and a defensible record of how it was tested.
Where to start
One assistant, one workflow or one model change you are worried about. We will scope the specification, the scenarios and the thresholds with you, and show you the first results against your own system.
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