−73%hallucination rate
Cut hallucinated answers by 73% before launch
Built a 3,000-case eval suite and citation grader for a banking copilot, blocking three regressions that automated tests had missed.
Read the case studyLLM evaluation, done rigorously
Ship language models you can actually trust.
AI Analytics is a Seattle-based consultancy for evaluating large language models. We design eval suites, run adversarial red-teaming, and build the benchmarks that turn “it feels better” into measurable confidence.
- 120+
- Eval suites shipped
- 40M+
- Graded model outputs
- 9
- Frontier labs advised
run #4821 · main
support-assistant-v3
Faithfulness94% +6.2
Instruction following91% +3.1
Refusal accuracy88% +11.4
Hallucination rate7 -4.8
2,480
Cases
12
Graders
$3.10
Cost
Trusted by teams shipping models to production
Northwind AILumina LabsCorvusApertureHelixQuanta
What we do
Evaluation infrastructure across the model lifecycle
From first prototype to production monitoring, we build the measurement layer that lets your team move fast without shipping regressions.
Eval suite design
We translate your product requirements into rubrics, golden datasets, and LLM-as-judge graders that actually correlate with user value.
Adversarial red-teaming
Systematic probing for jailbreaks, prompt injection, data exfiltration, and unsafe outputs — with reproducible attack libraries.
Model & prompt benchmarking
Head-to-head comparisons across providers, prompts, and fine-tunes so you choose on evidence, not vibes or vendor decks.
Regression testing in CI
Eval gates wired into your pipeline so every prompt change, model bump, or RAG tweak is scored before it reaches users.
Guardrails & safety
Layered input/output guardrails, refusal calibration, and policy alignment validated against your risk and compliance needs.
Production observability
Online evals, drift detection, and human-review workflows that keep scoring live long after launch day.
How we work
A measurement loop, not a one-off report
Every engagement follows the same rigorous path — and leaves your team with infrastructure they can run without us.
- 01
Scope & risk mapping
We interview your team, map failure modes, and define what good means for each capability and policy your model must uphold.
- 02
Dataset & rubric build
We assemble representative and adversarial test cases, then write graders — exact-match, model-based, and human — calibrated against expert labels.
- 03
Baseline & benchmark
We score current and candidate models to establish a defensible baseline and surface the trade-offs between quality, latency, and cost.
- 04
Integrate & monitor
We wire evals into CI and production, hand over dashboards, and train your team to own the loop long after the engagement ends.
Benchmarks
Decisions backed by a transparent scoreboard
A representative slice of a model-selection benchmark we run for clients — every number is reproducible and traceable to a test case.
benchmark · customer-support · 2,480 cases
updated 2h ago| Model | Accuracy | Safety | Latency | Cost / 1K | Verdict |
|---|---|---|---|---|---|
| gpt-frontier-4 | 92.4 | 96 | 1.9s | $8.20 | Recommended |
| claude-sentinel | 91.1 | 97 | 2.4s | $9.50 | Strong |
| open-mixtral-ft | 87.6 | 89 | 0.8s | $1.10 | Best value |
| gemini-pulse | 86.2 | 92 | 1.4s | $5.40 | Viable |
| legacy-baseline | 71.0 | 78 | 1.1s | $2.00 | Deprecate |
Selected work
Outcomes our clients can put a number on
Anonymized engagements that turned subjective model quality into decisions leadership could defend.
0unsafe outputs in audit
Cleared a clinical chatbot for regulated deployment
Red-teamed across 1,400 adversarial prompts and calibrated refusals, producing the evidence pack the compliance team needed to sign off.
Read the case study−68%cost per request
Saved 68% on inference with no quality loss
Benchmarked seven candidate models and a fine-tune, proving a cheaper open model matched the incumbent within the confidence interval.
Read the case studyEngagements
Pricing that scales with the stakes
Fixed-scope audits to embedded partnerships. Every engagement leaves you with infrastructure you own.
Eval Audit
$12kfixed, 2–3 weeks
A focused assessment of one model or product surface.
- Failure-mode & risk map
- Up to 500-case eval suite
- Baseline benchmark report
- Prioritized findings & roadmap
Build & Integrate
Most popular$28k+per engagement
End-to-end eval infrastructure wired into your pipeline.
- Everything in Eval Audit
- Custom graders & golden datasets
- Adversarial red-team library
- CI regression gates
- Team enablement & handover
Embedded Partner
Custommonthly retainer
An ongoing evaluation team alongside your engineers.
- Dedicated eval engineer
- Production observability & drift alerts
- Quarterly model re-benchmarking
- On-call for launches
FAQ
Questions teams ask us first
Model outputs are non-deterministic and open-ended, so you can't rely on exact assertions alone. We combine deterministic checks, model-based graders, and human review into rubrics that score quality, safety, and cost together.
Get started
Tell us what you're shipping
Send a few details about your model and goals. We'll reply within one business day with whether we can help and how.