Services

Three pillars cover almost everything modern AI/ML teams actually build. Each engagement draws from one or more of them, depending on where the highest-value problem lives.

Foundation models

Get the most out of LLMs in production.

• Model selection — closed vs. open-weight, with honest tradeoffs on cost, latency, and control.
• Retrieval-augmented generation (RAG) — chunking, indexing, hybrid search, re-ranking, evaluation.
• Fine-tuning and adapters — LoRA, PEFT, when to bother and when not to.
• Eval harnesses and regression suites — so you can answer “is it better?” with data.
• Inference, serving, and cost optimization — quantization, batching, caching, routing.

Agentic orchestration

Move past single-call chatbots.

• Multi-step planning and tool use — designing agents that don’t loop forever.
• Frameworks and custom orchestration — LangGraph, custom state machines, plain code.
• Memory, state, and guardrails — short-term, long-term, and the safety surface in between.
• Observability and trace debugging — so failures are inspectable instead of mysterious.
• Human-in-the-loop patterns — for the steps where full autonomy is the wrong answer.

Traditional AI/ML

The unglamorous work that still wins.

• Forecasting, classification, ranking — the workhorses behind most real ML ROI.
• Feature engineering and feature stores — including the question of whether you need one.
• Training pipelines and MLOps — reproducibility, lineage, deployment.
• Drift detection and monitoring — so models that quietly stop working don’t stay quiet.
• ML platform and infrastructure design — sized to your team, not a Big Tech reference architecture.

How the pillars combine

Most real systems live in the seams between these pillars. A customer-support agent might need RAG (foundation models), tool-calling and a memory layer (agentic orchestration), and a classifier that decides when to escalate to a human (traditional ML). Picking the right capability for each step — and not over-reaching into LLMs when a 50-line classifier would do — is most of the design work.

The next step is usually one of the engagement models — pick the one that fits the moment, and we’ll blend others in as the work evolves.