Engineers who stay with the problem until it's solved.
Forward-deployed engineers work inside your organization, close to the people who feel the problem. They scope it with your operators, build the software, integrate it with your systems and stay until it runs in production. This is how AI pilots stop being pilots.
Extend
What a pod takes on
Problem framing with operators
Time with the teams who do the work, turning their pain points into a specific, measurable build.
End-to-end delivery
One small team covers data, backend, AI, interface and deployment, so nothing waits on a handoff.
Integration with what you already run
ERPs, CRMs, core banking, warehouse systems and the spreadsheets that hold them together.
AI pilots taken to production
Existing proofs of concept hardened with security, monitoring, evals and support processes.
Customer-facing deployments
For product companies: engineers who deploy your platform into your customers' environments and feed what they learn back to product.
Capability transfer
Your people pair with ours throughout, so the knowledge stays when the pod moves on.
How it runs
From first week to handover.
Durations are typical. We agree the actual plan with you during scoping.
Embed
Week 1The pod joins your teams, gets access, and spends time with the people who own the problem.
Frame
Weeks 1 to 2A written problem statement, success measures and a delivery plan you sign off.
Build and deploy
Weeks 3 to 12Weekly releases into your environment, with a working demo for stakeholders every week.
Hand over or extend
End of termRunbooks, recorded walkthroughs and paired handover, or the pod moves to the next problem.
AI in the loop
Where AI changes the work
Faster first versions
Agents scaffold integrations and interfaces quickly, so operators react to working software in days instead of mockups.
Reading unfamiliar systems
We use AI to map undocumented codebases, APIs and data models in your estate before we change anything.
Operators in the loop
AI features ship with review steps your operators control, so trust builds with use.
Measured in production
Every deployment tracks the business metric it was built to move, not only uptime.
What you own at the end
- A system in production, used by the people it was built for
- Measured movement on the agreed business metric
- Integration code and infrastructure in your accounts
- Runbooks, decision records and recorded walkthroughs
- Your engineers able to run and extend it
Ways to engage
Forward-deployed pod
A small senior team embedded in your business that owns an outcome end to end.
Pilot to production
One use case built on your data and released to real users, against success measures agreed up front.
FAQ
Questions we hear.
How is this different from staff augmentation?
Staff augmentation adds engineers to your plan, managed by your leads. A forward-deployed pod owns an outcome: we frame the problem, decide how to solve it with you, and are accountable for the result.
What does a pod look like?
Usually two to four engineers, led by a senior engineer who is your single point of accountability, with an architect available as the work needs.
Do engineers need to be on site?
Some of the most valuable time is on site, especially in the first weeks. After that most pods work remotely with regular on-site days, depending on the problem and your location.
How long is a typical engagement?
Most run three to six months per problem. Pods are contracted monthly, so you can extend, change scope or wind down with notice.
Related
Often paired with.
Build
AI-First Product Engineering
Products designed around models from the first sprint: agents, copilots, retrieval and automated workflows, built with evals, guardrails and cost controls.
Extend
Staff Augmentation
Senior engineers who join your team, your tools and your rituals, with the AI-assisted habits that make them productive from the first sprint.
What are we building?
Tell us about the problem. An engineer, not a sales team, will reply.