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FORWARD-DEPLOYED AI INFRASTRUCTURE

Forward-deployed engineers for AI infrastructure

Senior engineers who join your team and get your AI systems working in production: model serving, GPUs, quality tests, and monitoring. When we leave, your team runs it.

Book 30 minutes Read the brief
01/ OFFERS

How we work with you

Every engagement has a fixed scope, a fixed price, and the same two engineers from start to finish.

01 / SPRINT

Production Sprint

2–4 weeksFixed fee

We take one AI feature that works in a demo and make it reliable enough to run your business on.

YOU GET
  • The feature running in production on real traffic
  • Automated quality tests built from your own examples
  • Monitoring, a runbook, and a handover
See what's included

For example, an assistant that answers your team's questions from company data, or a pipeline that reads and files incoming documents.

  • The quality tests block any change that makes answers worse
  • Your team can operate the feature on its own after the handover
02 / DEPLOY

Private Inference

About 2 monthsFixed fee

We build a model-serving platform in your cloud or on your own servers, so you can run open models, lower API costs, and keep data in your environment.

YOU GET
  • GPUs sized to your models, response times, and budget
  • One gateway across your own models and your API providers
  • A known cost per request, load-tested before launch
See what's included

It suits companies whose API bills have grown large, whose data can't leave their environment, or who want to run open models such as Llama, Qwen or DeepSeek.

WE HANDLE
  • Serving software, autoscaling, and monitoring
  • Routing each request to the cheapest model that can handle it
  • A tested process for adding new models, with security checks and quality tests on your own examples
  • Cost tracking per team and per application from day one
  • Runbooks, and a handover your team has practiced before we leave
03 / TEAM

Embedded Team

3–6 monthsRetainer

Two of us join your engineering team and work on your AI roadmap as if we were on staff.

YOU GET
  • Working features merged into your codebase every week
  • Your engineers on the code alongside us
  • A team that owns and understands everything when we leave
See what's included

We attend your standups, work in your code, and follow your review process. It suits teams with several AI projects underway and not enough senior people to lead them.

  • We review your architecture and flag decisions that will be expensive to change later
  • We help write the job descriptions and interview the people who will take over from us
02/ PROOF

What we've built

Two recent projects for a delivery fleet operator.

MODEL ROUTING · LOGISTICS

Sending each question to the right model

THE PROBLEM

The company's assistant ran every question on the same model. Most questions were simple lookups that a cheaper model could answer. A few needed a stronger one.

WHAT WE DID

We built a router that reads each question and chooses the model. Its rules come from how the company's own data tools work, not from how hard a question sounds. We tested it on questions their team had labeled with the right answer.

WHAT WE FOUND ALONG THE WAY

The model doing the routing sometimes gave different answers to the same question. We traced the cause, fixed it, and measured what routing adds to each request in cost and time.

21/21labeled questions routed correctly
8/8correct on questions it had never seen
0.1¢measured routing cost per request
AI ASSISTANT · LOGISTICS

An assistant that answers from live fleet data

THE PROBLEM

Fleet owners needed answers that were spread across rosters, safety scores, schedules, time off, and compliance records. Getting them meant opening several systems and doing the math by hand.

WHAT WE DID

We built an assistant that owners talk to in plain English. Behind it are purpose-built data tools. The assistant decides which ones to call, combines the results, and explains the answer.

EXAMPLE

"Who are my three worst drivers on safety over the last six weeks, and why?"

23data tools behind one assistant
5tool steps allowed per question, at most
Liverunning on the company's real data
ALSO BUILT

An AI gateway with cost tracking

Records the cost of every request and ties it to a customer and a feature, across model providers. Spending limits are enforced before a request reaches a model.

A secure GPU and AI platform

Built and run in-house for a research organization's teams, to strict security standards, without outside consultants.

03/ PROCESS

How an engagement runs

  1. 01 / WEEK 1

    Discover

    We read your code, look at real traffic, and agree on the one result we are there to improve.

  2. 02 / WEEKS 2+

    Build

    We work in your repository, your cloud, and your CI, with daily commits and a demo every week.

  3. 03 / CONTINUOUS

    Measure

    Quality tests, load tests, and cost per request, compared with where things stood in week one.

  4. 04 / FINAL WEEK

    Hand over

    Runbooks, dashboards, and a walkthrough with whoever owns it next. Everything stays with you.

04/ FIELD NOTES
WHITE PAPER · 001 · EXECUTIVE SUMMARY

Inference Is a Utility

How to build, run, and price a GPU inference platform when the models change every month. It covers why utilization decides cost per token, how to keep up with new open models, and why metering has to start on the first day.

Request the brief We'll email it to you personally. No mailing list.

Tell us what you're working on. Thirty minutes is usually enough to see what it needs.

Book 30 minutes