An engineer in your team who owns the outcome.
Forward-deployed engineering means we embed with your team, learn how the business actually runs and stay until a system is live and the number has moved. We do not hand over a slide deck or a prototype. It is how AI labs deploy into enterprises, offered to growing businesses that cannot hire a full-time forward-deployed engineer.
What a forward-deployed engineering system looks like.
Every workflow run, failure and approval in one place, with hours saved tracked against the baseline.
Four ways we put it to work.
Embedded discovery
We shadow the real process, map hand-offs and data, and quantify where hours and money leak before proposing anything.Workflow automation
Intake, approvals, reporting and hand-offs automated in n8n, Make, Zapier or custom code, with failures reported to a named owner.AI inside operations
Extraction, classification and drafting steps with a person approving high-stakes cases, and more going straight through as accuracy is proven.Adoption & hand-over
Training, documentation and runbooks so the system becomes how your team works, and keeps working after we step back.Who this is for
- Teams whose operations run on spreadsheets, copy-paste and one person who “knows how it works”
- Founders with an AI idea and no one to take it from first conversation to production
- Companies that bought AI tools but never got them into the daily workflow
- Businesses that need senior engineering for a quarter, not a permanent hire
Where the savings come from
- Manual hours removed and measured against a baseline
- Overlapping tools consolidated
- Errors and rework caught by validation
- A fractional engineer instead of a full-time senior hire
Questions
An engineer who embeds with your team and owns getting a working system into production, rather than advising from the outside. The model was popularised by Palantir and is now how AI labs deploy into large companies.
A few hours in the first week to map the process with the people who do the work, then short weekly check-ins while we build and ship.
Typically 3–8 weeks for a first system, then an optional monthly arrangement to run and extend it.
Find the workflow worth automating first.
Tell us about one process that costs your team hours every week. We will tell you honestly whether AI or automation is the right fix, and what a first build would take.