Service
AI Engineering POD as a Service
Embedded GenAI + full-stack squads that design, ship, and operate AI products with security, observability, and MLOps built-in.
Model strategy, tuning, evaluation, and RLHF with governance
Production pipelines: data prep, feature stores, CI/CD for models and services
Reliability and safety: tracing, guardrails, observability, red-teaming
What we deliver
Model lifecycle: selection, fine-tuning, evals, policy guardrails, and human-in-the-loop feedback.
Engineering foundations: secure APIs, latency budgets, rate limits, tracing, and observability.
MLOps: versioned datasets, feature stores, CI/CD for models and services, rollbacks, canary and shadow deployments.
How we engage
POD setup: charter, success metrics, architecture runway, and security checklist.
Sprints: design → build → ship → observe, with weekly demos and measurable KPIs.
Run: SLOs, on-call, cost/perf dashboards, and continuous improvement.