/ AI

AI integration and agents

Useful AI in production. Not a demo, not a slide deck.

Engagement: Project, embedded, or retainer
Timeline: First feature live in four to six weeks

01/ Overview

What this looks like in practice.

Most companies have an AI strategy on a slide and nothing in production. We build the AI integration and agents that ship: LLM features embedded in your existing software, retrieval that actually returns the right document, and agentic workflows that survive contact with real users.

We design with the same discipline we bring to any other system. Evaluations before we trust an output. Cost and latency budgets before we let it loose. Fallbacks and escape hatches before we hand it to a customer. The model is one component of a larger machine, and the rest of the machine matters more.

We work with the frontier model that fits the job. Claude, GPT, Gemini, open-weights, on-prem when the data demands it. We build so you can swap providers without rewriting your product.

02/ What's included

Everything in scope, in writing.

01AI feature design and product scoping
02Retrieval pipelines with vector and keyword search
03Agentic workflows with tools, memory, and guardrails
04Custom Model Context Protocol servers and clients
05Prompt engineering with versioned, evaluated prompts
06Fine-tuning and distillation for cost and latency
07Evaluation harness, regression suite, and observability
08Cost, rate-limit, and provider-failover engineering
09Privacy, PII redaction, and on-prem deployment
10Knowledge transfer for your engineering team

03/ How we work

The work, broken into four parts.

Step 01
Frame
We start with a single concrete use case, not a platform. Define the user, the success metric, and the acceptable cost per call.
Step 02
Prototype
A working slice in two weeks, on real data, with a real evaluation harness. We learn what the brief got wrong before it gets expensive.
Step 03
Harden
Guardrails, fallbacks, observability, and a regression suite. The feature is ready for production when it has a runbook, not when the demo works.
Step 04
Operate
Models drift, providers change, costs creep. We monitor quality and cost weekly, and ship improvements without breaking what shipped before.

04/ Tech we use

An opinionated, boring stack.

We pick the model and the tooling for the job, not the other way round. We will happily run a small open-weights model on your hardware if that is what the problem needs.