AI.

Vertronyx AI builds intelligent systems for clients who need them to actually work in production: ML models trained on their data, agents that handle their workflows, vision pipelines that ship at scale. We don't write whitepapers; we ship inference. Every model we deploy has a defined task, a measurable benchmark, and a fallback path when it gets a question wrong.

Our teams pair applied research with production engineering. That means evaluating open-weight models against frontier APIs on the client's actual workload, fine-tuning where the data warrants it, and building the orchestration, retrieval, and tooling layers that make a model useful inside a real product. We work in Python and TypeScript, ship on whatever cloud the client already uses, and instrument everything so cost and accuracy are visible from day one.

Most engagements run as a fixed-scope build followed by a retainer for monitoring, retraining, and capability expansion. Clients keep the weights, the prompts, and the eval suite — we hand off everything we touch.