AI / ML
Applied ML models and pipelines.
01
Models that beat the baseline on your metric
02
Reproducible training and promotion paths
03
Inference cost and latency under control
Model pipeline
From framing to inference — the path we run.
- 01
Scope
Boundaries & metrics
Define automation scope, data access, guardrails, and measurable success criteria.
- 02
Architecture
Agents, tools & eval
Design agent graphs, tool interfaces, evaluation harness, and failure modes.
- 03
Integrate
Production wiring
Connect to your stack with observability, rate limits, and human-in-the-loop paths.
- 04
Operate
Monitor & hand off
Ship runbooks, refine prompts and models, and transfer ownership to your team.
- Problem framing and labeled data strategy
- Feature pipelines and model selection experiments
- Training, validation, and shadow deployment
- Inference services with latency and cost budgets
Related · AI & Data
Continue in this practice.
Ready to start
Scope a AI / ML engagement with senior engineers.
Fixed-scope, dedicated squad, or staff augmentation — we'll map the right model in a short scoping call.