We evaluate, red team, and regression test AI systems for teams shipping models to production.
Evaluation cycle
#418 · assistant-v3
Cases scored
0
Rater agreement
κ 0.92
Regressions
3
Multi-month LLM evaluation engagements across coding, reasoning, safety, and instruction following.
Who we work with
You have a product going live. You need evaluation infrastructure before quality drift starts costing you users.
Build my harnessYour model scores look fine but users are complaining. We diagnose failure modes hiding inside aggregate metrics.
Diagnose my driftYou need continuous evaluation that runs on every prompt tweak and model swap. We run your eval program as a service.
Run my eval programServices
Three engagement tiers for teams at different stages.
Evaluation Audit
Two week engagement. We review your current eval setup, interview your team, and analyze your existing benchmarks and rubrics. You get a written audit with failure mode coverage gaps and a prioritized fix list.
Book a Discovery CallWhat you get
Evaluation Harness Build
Full custom evaluation harness. We define failure modes with your team, build calibrated rubrics, curate representative datasets, and deliver working harness code with one full evaluation cycle and report.
Book a Discovery CallWhat you get
Retained Evaluation Program
Ongoing evaluation on every model, prompt, or retrieval change. Continuous rubric refinement, adversarial dataset updates, and monthly drift analysis reports.
Book a Discovery CallWhat you get
Focused adversarial testing sprint. Jailbreak attempts, prompt injection tests, harmful output probes, and bias testing. Written report with reproducible test cases.
Traditional QA also available. Ask during your discovery call.
Method
The same discipline software QA has used for twenty years, adapted to AI outputs.
We start with what a bad output actually looks like in your system. Aggregate metrics hide the failures that matter.
Rubrics tied to real user harm. Verified with inter-rater agreement above 0.8 before any evaluation runs.
Production traffic samples, adversarial examples, and must-not-regress golden cases.
Not just a score. A diff. Which failure modes moved. By how much. On which subsets. Compared to which prior version.
Case Studies
Evaluation programs we have designed, staffed, and delivered.
Multi-month evaluation engagement covering coding tasks, reasoning quality, and instruction following. Six evaluators led by Boltz Automation. Delivered structured rubrics and calibrated ratings across thousands of model responses used in model training feedback loops.
Designed and applied a rubric-driven evaluation program for instruction following quality across multiple task categories. Contributed measurable signal to model refinement cycles.
Executed structured evaluation cycles on model outputs for coding assistant training. Rubric design, multi-rater calibration, and quality-tier analysis across thousands of coding responses.
Client names withheld under NDA. Details available on request during discovery calls.
Stack
Evaluation
Testing
AI Systems
Delivery
Founder & CEO
Automation Test Engineer with six years applying QA discipline to complex enterprise systems. Now leading a team bringing that same discipline to AI evaluation at Boltz Automation.
Get started
Book a 30-minute discovery call. We will review your current setup and tell you where the gaps are. No pitch.