TRACK RECORD
Foundations that hold.
Accuracy measurement is our sharpened focus for 2026. These are the data foundations we've built and run in production, the layer every trustworthy AI answer stands on. First accuracy before/after studies publish here as they complete.
Making an AI platform's agent behavior measurable.
When the agents were a black box, debugging took hours. We built the collection, storage, and dashboards that made behavior a measured quantity, and debugging time dropped 80%.
80% Faster Debugging · 3x Cost Visibility
Read case study →Foundation workReal-time answers for a parking platform: sub-second, self-hosted, governed.
Operational data from multiple sources unified into a pipeline where every dashboard answer is derived from one defined source of truth, the property that makes data AI-ready later.
2 Data Sources Unified · <1s Query Performance
Read case study →Foundation workFrom legacy database to self-service answers.
A legacy database nobody trusted, rebuilt into a clean analytics layer where anyone in the business can pull a number and know where it came from. Reports 10x faster, with zero visualization license cost.
10x Faster Reports · $0 Visualization Costs
Read case study →Find out what your AI actually scores.
One week. Thirty questions. A number instead of a hope.