Our engineers work inside your teams.
Forward-deployed AI engineers embedded with your existing full-stack teams — building the operational workflows, assessing the security, controlling the token spend and finding the next use case from inside the systems you already run.
Day in, day out — until your team does not need us.
One operational process, its volume, and what it costs you when it is late. That scopes an engagement faster than an assessment does.
This is for you if
If you have not decided what to build yet, start with AI consulting — that engagement produces the roadmap this one executes.
- You have engineering teams, and no AI engineering practice inside them
- A first AI workflow shipped and the cost is now uncomfortable
- Security has questions about agents that nobody can answer yet
- The obvious automations are done and the messy ones remain
Six workstreams, running at the same time.
Select one for what it involves in practice. Most engagements run three or four of these concurrently rather than in sequence.
The queues nobody has automated because they are messy.
Every enterprise has operational work that resisted RPA because the inputs vary too much — claims intake, supplier onboarding, exception handling, reconciliation. Agents handle variation, which is precisely why these are now automatable.
- Intake normalised across email, portal, SFTP and paper
- Extraction and validation against your systems of record
- Policy decisioning encoded from how your team already works
- Systems written to through existing interfaces, not screen-scraped
- Exceptions routed to a person with full context attached
- Delivered into the team that owns the process, not beside it
Turning a stated problem into a system that survives.
Most enterprise AI requests arrive as a solution already chosen — 'we want a chatbot on the policy documents'. Solutioning is the work of getting back to the actual problem and designing something that holds up under real volume and real scrutiny.
- Requirement traced back to the decision it is meant to improve
- Build-versus-buy called honestly, including 'you do not need AI here'
- Architecture designed for your deployment and residency constraints
- Evaluation approach agreed before the build starts
- Integration mapped against systems that already have owners
- Scoped so the first increment is genuinely useful
What your security function will ask, answered first.
AI systems introduce failure modes most enterprise security reviews have no template for — prompt injection, data leakage through retrieval, model supply chain, and what an agent is permitted to do unattended. We assess these before your review board does.
- Prompt injection and jailbreak testing against your own surfaces
- Retrieval boundaries verified — can a user reach what they should not
- Agent authority audited: what it can do without a human
- Model and dependency supply chain reviewed
- Data residency and retention traced end to end
- Findings written in the format your review board expects
AI spend behaves unlike any other line in your budget.
It scales with usage, moves when a provider changes pricing, and hides inside a cloud bill. We instrument it per workflow, then reduce it — usually substantially, because the first production version of almost any AI workflow is far more expensive than it needs to be.
- Token and inference spend attributed per workflow and per team
- Budgets and alerting set before a bill becomes a surprise
- Prompt and context size reduced where it changes nothing
- Model tiering: the expensive model only where it earns its place
- Caching and retrieval tuning to cut repeat cost
- Forecasts that survive a provider pricing change
The best candidates are already inside your estate.
Engineers embedded in your teams see the opportunities a discovery workshop never surfaces — the manual step in a running system, the report someone rebuilds monthly, the queue that quietly grew. Finding those is a standing part of the engagement.
- Candidates surfaced from systems already in production
- Sized against real volume, because we can see the real volume
- Prioritised with the team that owns the system
- Small automations shipped without a new business case each time
- Existing projects revisited as models and costs change
- Findings fed back into your roadmap and its review cadence
Your engineers should not need us in a year.
Forward-deployed means embedded and temporary. We work in your repositories and your ceremonies, and we deliberately transfer the AI engineering practice — evaluation, prompt discipline, cost awareness — to the people who will still be there.
- In your repos, your standups and your review process
- Pairing rather than parallel delivery
- Evaluation and cost practice taught, not just applied
- Internal capability measured as an engagement outcome
- Runbooks written for your on-call, not ours
- A defined reduction path rather than an open-ended retainer
The queues nobody has automated because they are messy.
Every enterprise has operational work that resisted RPA because the inputs vary too much — claims intake, supplier onboarding, exception handling, reconciliation. Agents handle variation, which is precisely why these are now automatable.
- Intake normalised across email, portal, SFTP and paper
- Extraction and validation against your systems of record
- Policy decisioning encoded from how your team already works
- Systems written to through existing interfaces, not screen-scraped
- Exceptions routed to a person with full context attached
- Delivered into the team that owns the process, not beside it
What an engineering leader is judged on.
The operational number the process owner is already measured on. If that does not move, nothing else about the engagement matters.
Token and inference spend attributed per workflow. The first production version of almost any AI workflow costs far more than it needs to — that gap is usually the fastest win available.
Whether your own engineers can extend and operate what was built. Forward-deployed means temporary; an engagement that creates dependency has failed on its own terms.
What “embedded” actually means.
The word is used loosely. Here it means our engineers are inside your delivery process rather than running one beside it — which is the only arrangement in which capability actually transfers.
Your codebase, your branching model, your review process — not a parallel repo handed over at the end.
Your standups, your planning, your incident channels. Embedded rather than adjacent.
Working alongside your engineers so the practice transfers while the work ships.
Our increments meet your quality bar and your release process, not a separate standard.
Your identity model, your access boundaries, your data residency. Cleared where the estate requires it.
Embedded engineers surface candidates from running systems instead of waiting for a business case.
Where we start from something
Not everything needs building from scratch.
Several of these workstreams have a productised starting point we already run in production elsewhere. Starting from working components is faster and cheaper than starting from a blank repository.
Let's talk about where AI actually pays off for you.
One operational process, its real volume, and what it costs when it is late. That scopes an engagement faster than a discovery exercise, and it gives your team something running to judge us on.
AI for enterprises — common questions
Our engineers work inside your teams — your repositories, your standups, your review process and your definition of done — rather than delivering in parallel and handing over. It also means temporary: the engagement has a reduction path, because an arrangement that creates permanent dependency has failed on its own terms.
Staff augmentation supplies senior engineers who work under your direction on work you have already scoped. This is an outcome engagement: we bring the AI engineering practice — solutioning, evaluation, security assessment, cost optimisation — and we are accountable for the workstreams, not just for filling seats.
Usually yes, and often substantially. The first production version of almost any AI workflow is more expensive than it needs to be — oversized context, the expensive model used everywhere, no caching, no per-workflow attribution. We instrument spend per workflow first, because you cannot reduce what you cannot attribute.
The failure modes most enterprise security templates do not yet cover: prompt injection against your own surfaces, whether retrieval boundaries can be crossed, what an agent is permitted to do unattended, model and dependency supply chain, and end-to-end data residency. Findings are written in the format your review board expects.
No, though it helps. If the priorities are not settled, our AI consulting engagement produces the roadmap and the review cadence first. If you already know what you want built, this is the engagement that builds it alongside your teams.