[ Selected work ]

The production layer, not the prototype

Most teams can stand up an AI demo. Very few ship systems that hold up in production — with evals, guardrails, observability, and the infrastructure to run them reliably. That is the layer we build. Below are honest examples of the engagements we take on, organized around our two focus areas.

We keep client details confidential. The examples here describe the problem and what we built; concrete outcomes are shared under NDA on a call.

[ AI Agents & GenAI Systems ]

Agentic workflows, RAG, and applied generative AI

LLM integration, retrieval over proprietary data, agentic automation, and the evals and human-in-the-loop controls that make them safe to ship.

Agentic automation

Agentic automation for an operations-heavy workflow

Problem. A US team ran a multi-step internal process by hand — reading documents, looking up records, and routing decisions — which was slow and inconsistent at scale.

What we built. A human-in-the-loop agentic workflow that orchestrates tool calls across their systems, with explicit guardrails, eval coverage on each step, and an audit trail so a person reviews and approves before anything is committed.

Result. Faster, more consistent handling of the workflow with humans kept in control of the decisions that matter.

RAG & retrieval

RAG over proprietary documents and knowledge

Problem. Critical knowledge was locked in scattered documents and systems. Staff spent real time hunting for answers, and a naive chatbot would hallucinate on questions that mattered.

What we built. A retrieval-augmented system grounded strictly in the client's own data, with citations back to source, retrieval evals to catch regressions, and guardrails that decline rather than guess when confidence is low.

Result. Grounded, source-cited answers staff can trust, with measurable quality tracked over time instead of vibes.

LLM integration

Customer-facing assistant taken to production

Problem. A promising LLM prototype could not go live — no eval harness, no guardrails, no monitoring, and unpredictable behavior on edge cases.

What we built. We hardened it for production: an offline eval suite tied to CI, prompt and tool-call guardrails, escalation to a human on uncertain cases, and observability on quality, cost, and latency in the live system.

Result. A prototype turned into a monitored, evaluated system the team could safely put in front of customers.

Explore AI Agents & GenAI Systems

[ AI Platform & MLOps Engineering ]

The infrastructure that keeps AI running

Deployment pipelines, evaluation and observability, data and feature infrastructure, inference scaling, and model governance — the platform underneath the product.

MLOps pipeline

Model deployment pipeline and release process

Problem. Models shipped manually and inconsistently. There was no reliable way to roll back a bad release or reproduce what was running in production.

What we built. A versioned deployment pipeline with automated evals as a release gate, staged rollout, and one-step rollback — so shipping a model became a routine, reversible operation.

Result. Repeatable releases with a clear gate and a safe path back, replacing ad-hoc deploys.

Eval & observability

Eval and observability for a live AI system

Problem. An AI feature was already in production, but the team was effectively flying blind — no way to know if quality regressed, and cost and latency were hard to attribute.

What we built. An evaluation and observability layer that tracks output quality against a labeled set, traces requests end to end, and surfaces cost, latency, and failure modes so regressions get caught before users do.

Result. Visibility into quality, cost, and reliability — and an early-warning signal when something drifts.

Inference & data infra

Inference scaling and feature infrastructure

Problem. Growing usage strained an AI system — inference costs climbed, latency was inconsistent, and feature/data pipelines were fragile under load.

What we built. A more efficient inference path (batching, caching, right-sized serving) and hardened data/feature infrastructure with model governance, so the platform scaled with demand instead of against it.

Result. A platform that handles higher load with more predictable cost and latency.

Explore AI Platform & MLOps

Why no logos or numbers here

We share specifics under NDA, not on a marketing page

Our engagements involve proprietary data and internal systems, so we don't publish client names, logos, or metrics. On a call we can walk you through detailed results, architecture decisions, and references relevant to your situation — under NDA on request. You own all code and IP we deliver.

Have a prototype that needs to reach production?

Engagements start at $100K, priced on the outcomes we deliver. Let's talk through yours.

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