What we build
Evaluation Frameworks
Systematic quality measurement by task type. Classification: accuracy, precision, recall, F1. Extraction: field-level accuracy. Generation: LLM-as-judge, rubric-based human evaluation.
Benchmark Datasets
High-quality evaluation sets created with domain experts. Diverse, representative, including edge cases. Versioned and expandable.
Automated Evaluation Pipelines
Runs on every prompt change or model update. Catches regressions before production. Integrated into CI/CD.
Production Monitoring
Quality sampling, drift detection, cost monitoring, latency tracking (p50, p95, p99), error monitoring. Alerts when scores drop below thresholds.
A/B Testing
Route traffic between variants, collect quality metrics, compute statistical significance. Replace "it feels better" with "it is measurably better."
Quality Dashboards
Real-time visibility. Overall scores, trends, per-feature breakdowns, cost tracking. Different views for engineering, product, and leadership.
How it works
Define "Good"—Step 1
Concrete, measurable quality criteria for each AI system.
Build Benchmarks—Step 2
Evaluation datasets with domain experts.
Automate Evaluation—Step 3
Pipeline that runs on every change.
Instrument Production—Step 4
Monitoring, alerting, dashboards.
Continuous Improvement—Step 5
Evidence-driven iteration cycle.
Deliverables
What you get
- Evaluation framework with benchmark datasets
- Automated evaluation pipeline (CI/CD integrated)
- Production monitoring with dashboards and alerting
- A/B testing capability
- Quality trend analysis
- Operational runbook for quality alerts
Case Study
Hashlock AI Auditor
A production AI system where output quality directly matters — built with the evaluation harness and observability needed to measure audit performance over time.
AI in production but no systematic way to know if it’s working?
Most teams don’t invest in evaluation until something goes wrong. We build it in from the start because it’s the only way to operate AI systems with confidence.