How we engage.
Six ways in. They share one engineering bar and one operating assumption — that the people who advise you are the people who build it and get paged for it.
Applied AI in the workflows where decisions get made — advised, built, and run inside your perimeter through security, legal and audit.
The pilot works and it has not passed review.
ExploreForward-deployed engineers inside your teams — operations automation, solutioning, security assessment and token optimisation.
You have decided what to build and need it built alongside your teams.
ExploreGreenfield builds, legacy modernisation, AI-assisted delivery, data platforms and the run layer.
Every change to the product is getting slower.
ExploreDefence-grade MR training with SOP-gated scenarios, multiplayer roles and after-action analytics.
The procedure is too risky or costly to rehearse for real.
ExploreCo-building AI-native products, plus the security and evaluation evidence enterprise buyers demand.
The demo lands and procurement stops everything.
ExploreProduction-grade AI engineers embedded in your team, from our own product teams.
Hiring is taking longer than the project allows.
ExploreStart from where you are
Which one is yours?
The service names matter less than the situation. Pick the one that sounds like your organisation right now.
There is a mandate but no shortlist.
AI is on the agenda, there is budget or there will be, and the list of candidate use cases came from vendors rather than from the people doing the work. What is missing is an ordered, costed view of what is actually worth doing.
- Use cases sourced from vendors, not operators
- No agreed way to compare candidates
- Budget exists before the plan does
- Nobody owns the adoption number
- Pilots that never got a second phase
- Board asking what the return was
Capable engineers, stalled AI work.
The full-stack teams are good and busy, but the AI work keeps slipping behind delivery commitments, spend is drifting with nobody watching token budgets, and the security team has questions nobody has answered yet.
- AI work queued behind delivery commitments
- Nobody owns model or token spend
- Security review is blocking a launch
- Use cases sitting unnoticed in live systems
- Back-office processes still manual
- Your team should own it afterwards
Capital and domain expertise, no build team.
You know the market and you can fund it, but the technical half of the founding team is missing and hiring one before product-market fit is the wrong order. What you need is engineers who will stress-test the idea, not just implement it.
- Domain expertise, no technical co-founder
- Pre-product-market-fit resource constraints
- Need feasibility and cost before committing
- Confidential and non-attributed engagement
- Category exclusivity available
- Fixed-price, retainer, equity or hybrid
Every change to it costs more than the last.
The platform works, but shipping anything takes longer each quarter, a rewrite has been proposed and nobody can size the risk of it. The question is engineering, not AI — though AI-assisted delivery is how a small team covers the ground.
- Delivery slowing quarter on quarter
- A rewrite proposed but not sized
- Team owns the screens, not the platform
- Modernisation vs rewrite still undecided
- Production operations need an owner
- Handover must leave you independent
The gap is a quarter, not a headcount.
The plan is sound and the team is short of a specific skill for a specific window. Hiring takes longer than the project allows, and a permanent hire is the wrong answer for work that ends.
- Hiring slower than the project timeline
- Need production AI experience, not theory
- A launch needs hands for a quarter
- Work may sit inside a security perimeter
- Your process, your repo, your standup
- Knowledge has to stay when they leave
The drill is expensive or risky to stage.
Competence has to be evidenced, not assumed, and the procedure is one you cannot run often enough for real — because it is hazardous, costly to set up, or depends on which instructor happened to run the session.
- High-risk or high-cost procedure
- Quality varies by instructor
- Attendance recorded, competence not evidenced
- Regulator wants proof, not registers
- Facility has no outbound connectivity
- Scenarios must change when the SOP does
There is a mandate but no shortlist.
AI is on the agenda, there is budget or there will be, and the list of candidate use cases came from vendors rather than from the people doing the work. What is missing is an ordered, costed view of what is actually worth doing.
- Use cases sourced from vendors, not operators
- No agreed way to compare candidates
- Budget exists before the plan does
- Nobody owns the adoption number
- Pilots that never got a second phase
- Board asking what the return was
Not sure which one you need?
Describe the problem rather than the service. Most engagements start as one thing and turn out to be another — that conversation is free and usually short.