AI Engineering
— production-grade

We build, deploy and run AI systems that survive real traffic — LLMOps and MLOps pipelines, evaluation-driven development, enterprise AI APIs and infrastructure sized for cost and latency.

Evals are the new unit tests

Nothing ships without an evaluation suite — golden datasets, LLM-as-judge with calibration, regression gates in CI.

Multi-model is normal

Routing across Anthropic, OpenAI, Google and open-weight models by task, cost and residency through a gateway is standard architecture.

Observability matured

Traces of prompts, tools, retrievals and costs per request; drift and quality monitoring feed back into evals.

Lakehouse, feature store, labelling, synthetic data.

Fine-tuning (LoRA), RLHF/DPO where justified, experiment tracking, registry.

Task metrics, groundedness, safety, bias, latency, cost — automated in CI.

Gateways, routers, caching, streaming, fallback across providers.

Tracing, drift, audit logs, lineage from prompt to output.