Services
AI & Machine Learning
From model strategy to governed production systems.
How we approach it
Most AI programs die between the demo and the audit. We start from the governance requirement — evaluation, attribution, guardrails — and build backwards to the model, so what ships is what compliance can sign.
Tools of the trade
- PyTorch
- Ray
- LangGraph
- Algoryq Mind
- vLLM
- Weights & Biases
What you get
- 01
AI strategy and use-case portfolio with measurable ROI targets
- 02
Production LLM systems with evaluation and guardrails built in
- 03
MLOps platforms your own teams can operate
How we deliver
01
Discover
Two weeks inside your domain. We leave with the problem stated in one sentence.
02
Architect
Systems designed on paper first — reviewed, costed, and stress-tested before a line is written.
03
Build
Weekly shippable increments. Production quality from the first commit.
04
Ship
Progressive rollout with observability, runbooks, and rollback rehearsed.
05
Evolve
We stay through scale — measuring, hardening, compounding.
Adjacent capabilities
