Most companies have an AI strategy. Almost none have AI in production.
We embed senior engineers with your team and ship working AI into production. Weeks, not quarters.
The model isn’t the hard part anymore. Getting it into your operations is. That’s the work we do.
- 01
Diagnostic
Week 1We find where AI creates the most value in your operations.
- 02
Embed & build
Weeks 2+Senior engineers work inside your team on the priority workflows.
- 03
Ship to production
LiveOne workflow goes live, in front of real users, proving impact.
- 04
Scale
OngoingWhat works becomes a pattern we roll across the business.
The model isn’t the hard part anymore. Deployment is.
Forward deployment is the model Palantir pioneered, and that OpenAI, Anthropic, and Databricks all adopted in 2026: put engineers inside the business to build, instead of handing over a strategy deck.
We’ve been doing it for years.
From your operations to production, in weeks.
- 01
Diagnostic
We map your workflows and find where AI creates the most value. You get a prioritized plan, not a strategy deck.
- 02
Embed and build
Senior engineers work alongside your team, in your systems, building the priority workflows end to end.
- 03
Prove it
We ship one workflow to production and measure the impact against how the work gets done today.
- 04
Scale
What works becomes a repeatable pattern. We extend it across teams and keep it running.
We own the outcome, not the deck.
Strategy firms describe what you should build. Staff augmentation builds pieces and leaves. We embed, build the whole thing, and stay until it runs in production.
- Engineers, not advisors. We write the code and ship it inside your stack.
- Production, not pilots. The bar is working software your team uses every day, not a proof of concept.
- Outcomes, not hours. We scope to your priorities and measure against how the work gets done today.
In production today.
We embed inside client teams and ship working systems into production. For one organization alone, we’ve built and shipped more than ten, from assessment to editorial to support.
Editors hand-wrote 300 to 400 word case summaries under strict style, citation, and redaction rules. We embedded and built a multi-step pipeline that drafts, validates every claim against the source, and enforces the style guide, running 25 in parallel. A person approves each summary before it ships.
Exam items were written by hand in a legacy tool, disconnected from the source material. We built a multi-agent system that drafts items grounded in the real exam bank, validates each one against the source, and assembles complete exam forms.
A legacy content monolith ran the editorial team by hand. We replaced it with an authoring agent: pull the sources, draft to your templates, generate the visuals, and publish straight to the CMS. Editors tune the prompts themselves.
Reps drafted every reply from scratch across a sprawling knowledge base. We built an agent that reads each ticket, reproduces the issue, and drafts the reply over a 170+ article knowledge base. Reps review and send.
Start with a diagnostic.
Every engagement starts by finding where AI creates the most value in your operations. From there we scope a focused build around your priorities. No six-month strategy decks.
- Diagnostic
- We map your workflows and find the highest-value places AI can move the needle. You get a prioritized plan.
- Embedded build
- Engineers embed with your team and ship the priority workflows into production, inside your stack.
Engagements are scoped to your priorities. Let’s talk about where to start.
Frequently asked questions.
What does an engagement look like?
We start with a short diagnostic to find where AI creates the most value, then embed with your team to build the priority workflows. You get working software in production, not a report. Most first workflows are live within weeks.
How is this different from a consulting firm?
Consultants hand you a strategy and leave the building to you. We write the code and ship it inside your systems. The deliverable is working AI in production, and we own that outcome.
Who owns what you build?
You do. The systems, the code, and the data are yours. We build inside your stack and your accounts, so nothing is locked to us.
What about our data and security?
Your data stays yours. We work inside your environment, we don't train on your data, and we don't reuse it across clients.
Do you work with our existing stack and models?
Yes. We're model-agnostic and meet your systems where they are, whether that's your cloud, your data, or the tools your team already uses.
How much does it cost?
Engagements are scoped to the work and your priorities, so pricing depends on what we're building. Book a call and we'll scope it together.