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Prove your AI is ready before you turn it on

AnchorDrift validates LLM-powered processes before they reach production: calibrated scoring against your own experts' judgment, human review of uncertain cases, and a validation report your organization can sign off on.

The Problem
The model works in the demo. Now someone has to sign their name to it.
Most teams testing an LLM for claims, underwriting support, or document processing are doing it by hand: experts reading outputs in spreadsheets, scoring by feel, for weeks. The result is slow, expensive, impossible to repeat when the model changes, and hard to defend when audit or a regulator asks how readiness was established.

Manual review does not scale

Expert hours are the scarcest resource in the building. Spending them grading hundreds of outputs per model change is not sustainable, so coverage shrinks exactly when scrutiny should grow.

Engineering self-assessment is not sign-off

Developer evaluation tools are good at catching regressions in a build pipeline. They were never designed to produce evidence that a risk committee, internal audit, or an examiner would accept.

Criteria that move are not criteria

When pass bars live in a spreadsheet or a config file, nothing stops them shifting after results are in. Evidence of readiness starts with criteria that were locked before the test ran.

How It Works
Guided validation in four steps
Every engagement starts with guided setup. We work with your subject matter experts to tune the evaluation to your specific use case, because a generic rubric proves nothing about your process.
01 / Define

Rubrics tuned to your use case

We start from field-tested templates for insurance workflows such as claims triage, then refine the scoring dimensions and rubric language with your experts until they describe what correct means for your process, your policies, and your regulatory obligations.

02 / Calibrate

The judge earns trust before it scores

Your experts score a calibration sample. We measure how closely automated scoring agrees with them before any result counts. The agreement evidence goes in the final report, because "why trust automated scoring" is the first question every reviewer asks.

03 / Validate

Run the full test set in one pass

Export your system's outputs and upload them in batch. No integration work required to start. Every case is scored on every dimension, pass criteria are locked and timestamped before the run, and cases where scoring is uncertain are routed to a human reviewer instead of being silently counted.

04 / Decide

A verdict you can stand behind

The run produces a clear result with an itemized findings list, coverage disclosure per dimension, and a validation report mapped to the frameworks your reviewers care about, including NAIC guidance for insurance workflows.

Guided setup, by design

Validation is not a self-service tool you are left alone with. Rubric tuning, test case design, and calibration are working sessions with our team and your experts. The setup is done once per workflow; every validation run after that reuses it, including when models, prompts, or vendors change.

What You Receive
Evidence, not just scores
A governed verdict
Pass, pass with findings, or fail, computed against criteria that were locked and timestamped before the run started.
Calibration evidence
Documented agreement between automated scoring and your own experts, established before results counted.
Human review record
Every case where scoring was uncertain, who resolved it, and how. Uncertainty is surfaced and adjudicated, never hidden.
Coverage disclosure
Exactly how many cases were tested per dimension, stated plainly, so the verdict is as honest as the testing behind it.
Framework-mapped report
A validation document written for the people who review it: risk committees, internal audit, and regulators. For insurance workflows, mapped to NAIC guidance.
Built for Your Role
One validation, four readers
Testing / QA Lead
Weeks of manual grading become hours of reviewing flagged cases.
Repeatable runs mean revalidation after every model or prompt change is routine, not a project.
VP Engineering
Ship the AI feature with evidence, not assurances.
A validation verdict backed by calibration gives the go-live decision something solid to rest on.
Business Product Owner
Know the process will help before it can hurt.
Rubrics written in your business language, scored at a scale manual review cannot reach.
Compliance & Audit
The same report that supports the launch supports the exam.
Locked criteria, calibration evidence, and human review records, retained and ready when asked.
After Sign-Off
Validation proves it is ready. Monitoring proves it stays that way.
A validation that passed in March says nothing about August. Model providers push silent updates, prompts get tuned, and inputs drift. The validation run that earned your sign-off becomes the baseline for continuous behavioral monitoring, so the question being watched in production is precise: is behavior still consistent with what was approved?
Define & Calibrate → Validate → Sign-off → Monitor in production → Revalidate on change
Get Started

Testing an LLM use case right now?

If your team is validating an LLM-powered process by hand today, we can show you what a governed version looks like using your own use case. A 30-minute conversation is enough to map your current testing to a validation plan.
Book a Discovery Call