Veriftrix gives startups working AI systems — from fraud detection to backend ML pipelines — scoped, built, and shipped by engineers who've done it before.
We scope tightly and ship systems that keep working after we leave — every build tested, documented, and explainable.
Production-ready machine learning systems with explainability, monitoring, evaluation, and deployment — not just notebooks.
Real-time fraud detection, anomaly detection, behavioral analytics, and explainable risk scoring for financial applications.
Automated ETL pipelines, feature engineering, validation, and data quality workflows powering reliable ML systems.
Clean, well-documented APIs that connect your product to the models and data behind it.
The same sequence on every engagement, so you always know what's next.
We size the problem, flag risks, and quote real timelines — no padding, no guesswork.
Models and systems are built incrementally, with tests written alongside the code, not after.
Architecture, decisions, and trade-offs are written down so your team isn't dependent on memory.
Deployed, and explained — your team can operate and extend it without us in the room.
Representative projects across the kind of work we take on.
Veriftrix started as a small studio with one standard: every build gets tested, documented, and reviewed before it goes out the door. We come from research and engineering backgrounds, so rigor isn't an extra step we charge for later — it's how we build by default.
Tell us what you're building. We'll reply within a day or two with honest scope and timeline — not a sales call.