GenAI ConsultingEst. 2023

GenAI that actually ships.

Most AI prototypes never reach production. The ones that do usually arrive late, expensive, and fragile. We build the other kind — systems with evals, guardrails, cost controls, and a clear story about what they do and don't do well.

Chatbots, autonomous agents, RAG, copilots, fine-tuned models, edge inference. Scoping to eval to deploy.

ChatbotsAI AgentsRAGCopilotsFine-tuningEdge InferenceLLM EvalsPrompt Engineering
ChatbotsAI AgentsRAGCopilotsFine-tuningEdge InferenceLLM EvalsPrompt Engineering
How we work

Scope, prototype, eval, ship, iterate.

01

Scope

We pin down the use case, the success metric, the eval dataset, and the guardrails — before a line of code is written. Most AI projects fail here, not in the build.

02

Prototype

A working end-to-end prototype in 1–3 weeks. Real data, real LLM calls, real answers. You test with actual users before we commit to production architecture.

03

Eval

Eval dataset, regression tests in CI, human-in-loop review flow. If we can't measure whether it got better, we don't ship it.

04

Ship

Production deploy with observability, cost controls, prompt versioning, drift monitoring. You own the stack and the weights — no vendor lock-in.

05

Iterate

Monthly eval review, prompt tuning, cost optimization. Most GenAI systems degrade without care. Ours get better.

How we think

Opinions we've earned the hard way.

Evals before features

If you can't grade it, you can't ship it. Every system we build has an eval harness from day one.

Cheap and small first

We start with the smallest model that works, then upgrade only where accuracy demands it. Your token bill stays predictable.

RAG > fine-tuning (usually)

Most use cases are better served by retrieval. Fine-tuning is a scalpel, not a hammer — we use it when the numbers actually justify it.

Guardrails are not optional

PII redaction, prompt-injection defense, rate limiting, fallback paths. Production AI needs production discipline.

You own the stack

No proprietary platforms. We build on OpenAI / Anthropic / open models, with infra you can swap out. If we part ways tomorrow, your system keeps running.

Numbers over narratives

We'd rather show you an accuracy delta than a pitch deck. Every project has baseline metrics, lift numbers, and a cost model.

GenAI in production

Case studies.

All case studies

Thinking about a GenAI project?

Bring us the use case. We'll give you back a scoped plan, an eval strategy, and a realistic cost model. No slideware.

Let's talk