Embedded engineering teams that get AI into production, not just into a demo. Our engineers work inside your sprints, your systems, and your codebase until the outcome is real, not just demoed.
Talk to our team
85%
Long-term Partnerships
75%
Mid-to-Senior Engineers
5+
Avg. Years of Engineer Experience
98%
Would Recommend Us
You're probably here because one of these is true.
What we do under one practice
These aren't stages of one project, they're four separate services you can engage on their own. Take the one that matches where your project is stuck today, or combine them as your program grows.
Senior engineers who sit inside your team, tools, and standups.
Full-time senior engineersEmbedded sprint cadenceProduction code, not slidesScales with your program
Senior engineers who sit inside your team, your tools, and your standups to handle custom application development directly, not a separate vendor thread you have to manage. Full-time and embedded into your sprint cadence.
Make a prototype survive real data, load, and users.
Edge-case testingLatency and cost tuningHallucination guardrailsMonitoring and rollback
We take a prototype built through AI development and make it survive contact with real data, real load, and real users, covering edge cases, latency, cost, hallucination guardrails, and rollback.
Connect new capabilities to the systems that run your business.
Legacy API connectorsData pipeline designCore banking and ERPPhased rollout
We connect new AI and software capabilities to the systems that already run your business through careful data engineering, without a rip-and-replace project or a stalled backlog item.
The engagement ties to a measurable result, not a ticket list.
Agreed success criteriaWeekly metric checkpointsStays through adoptionClear handover plan
This model ties the engagement to a measurable business result, not a fixed list of tickets, with agreed success criteria and weekly business-metric checkpoints until the outcome lands.
Tell us where your project is stuck, and we'll point you to the piece that fits.
Why teams pick us for this.
Our forward-deployed engineers ship production changes in your codebase from week one. You get a working system, not a deck explaining why one is possible.
You work with the engineer who will still be on the project in month three, not a junior researcher who rotates off once discovery ends.
A demo that works once is not a system that survives real customers. Our teams stay through the adoption curve when the real edge cases show up.
Our engineers work in step with US, UK, and Gulf business hours, so reviews and standups don't wait on a time-zone gap.
Short, embedded, and accountable from week one. You work directly with the engineer who stays through the whole project, not a discovery team that hands off to someone else.
Talk to a consultant{ 01 }· Week 1
We look at where the pilot or project actually broke, whether that's data quality, integration, adoption, or scope, and agree on the outcome we're accountable for.
{ 02 }· Week 1 to 2
Our engineers join your tools, your repos, and your standups.
{ 03 }· Ongoing
This step means writing and shipping production code every sprint, not presenting a roadmap. Edge cases get fixed as they surface, not logged for later.
{ 04 }· Handover
Once the outcome is met, you get a system your own team can run and extend. If the next problem needs the same model, the same engineers can scale into it.
Questions, answered.
Forward-deployed engineering is the umbrella for four services: embedded engineering pods, AI production hardening, legacy and data integration, and outcome-linked delivery. Each solves a different stalled-project problem and stands on its own.
Just one is fine. Most clients start with the single service that matches where their project is stuck, often AI production hardening or legacy integration, and add the others later if they help.
Most engagements begin within two to three weeks of the initial scoping call, since the first step is agreeing which service, or combination, actually fits your stalled project. Cost and timeline depend on that choice, not a fixed package.
You don't have to hand the whole project over. Many clients keep their in-house team and bring in one of these services, most often embedded engineering pods, to work alongside them rather than replace them.
These four services plug directly into custom application development, AI development, and data engineering work we already do. If the project grows past scope, the same embedded team can extend into that larger build.
Tell us where your AI pilot, integration, or platform problem is stuck. A senior engineer replies within one working day.
Tell us where you're stuck
Your AI pilot, integration, or platform problem, in plain terms.
A senior engineer replies
A response within one working day, no sales pitch.
An honest fit check
We'll tell you plainly if this model fits, or if something simpler will do.
Tell us where your AI pilot, integration, or platform problem is stuck. A senior engineer replies within one working day.
