Outcomes
- Faster path from prototype to reliable production release.
- Model and prompt behavior monitored with actionable telemetry.
- Operational controls for quality, privacy, and cost.
Deliverables
- Use-case assessment and feasibility scoring.
- Reference architecture for model serving and integration.
- MLOps plan covering evaluation, deployment, and monitoring.
Typical timeline: 4 to 10 weeks depending on model integration depth.
FAQ
Do you support both classical ML and LLM workflows?
Yes. Engagements can include data pipelines, model ops, retrieval systems, and application integration.
How do you prevent runaway inference costs?
Every implementation includes usage budgets, observability, and routing strategies to control cost/performance tradeoffs.