Forward Deployed · AI
10x the output of your engineering team
Your senior engineers are the best asset you have. Our Forward Deployed AI engineers embed with them and turn AI into real throughput: the right tooling for your stack, wired into the daily work of engineering, design, QA and product, with the quality checks that keep the speed safe.
Partnering with Releaseworks really raised the bar for us; transparent, structured and professional. It feels like our company motor just got a powerful, high-performance upgrade!
Carola KrasCOO & Co-founder, iClaim
What you get
AI-Powered Software Team, From Day One
- Real, hands on expertise getting your team using AI in software delivery
- Standardised on best-in-class AI tooling, chosen for your environment
- AI in the daily work of engineers, designers, QA and PMs
- Cost, quality and safety you can put in front of a board
Three questions. No call required.
Partnering with Releaseworks really raised the bar for us; transparent, structured and professional. It feels like our company motor just got a powerful, high-performance upgrade!
Carola KrasCOO & Co-founder, iClaim
We have shipped production systems for
Production first
we ship AI features, not pilots and slide decks
Whole delivery org
engineering, design, QA and product, not only developers
1 named engineer
in your team, not a rotating bench
The problem
The demo was brilliant. Nine months later it is still a demo.
The prototype impressed everyone, then hit the questions nobody had answered: what does it cost per user, how do we know it got better rather than different, what happens when it says something wrong, whose data went where. It sits in a branch while the team argues about model choice.
At the same time, tools are available but adoption is uneven. A couple of engineers get real leverage, designers and QA are on their own, and nobody can say whether any of it changed a number. The gap is not enthusiasm, it is production engineering.
Signals you will recognise
- An AI prototype that has not shipped
- No way to measure quality, so it is a matter of opinion
- Personal accounts and no view of what data leaves the org
- Every team building its own retrieval plumbing
- Adoption claimed, impact unmeasured
How it works
An engineer who has shipped this before, working in your codebase.
Our engineer joins your team and takes the AI work from prototype to production: retrieval that behaves, automated quality checks that catch regressions, a gateway with cost and safety controls, observability on every call. Pull requests in your repo, reviewed by your engineers.
In parallel we make AI useful outside engineering: sanctioned tooling and working patterns for design, QA, product and delivery, plus the measurement to show whether it actually changed lead time or throughput. Flexible by design, from a day a week to full time for a push.
Outcomes
Tangible outcomes, not an AI strategy deck.
The AI feature actually in production
The stalled prototype rebuilt to production standards: retrieval, guardrails, fallbacks, rate limits and a rollback path, live for real users.
Quality you can measure
A set of known-good test cases run automatically on every change, so a model or prompt tweak is a measured decision rather than a gamble.
One LLM platform and gateway
A single sanctioned route to models with keys, quotas, caching, per-team cost attribution and audit logging. Shadow AI stops being a risk.
Retrieval that behaves
Ingestion, chunking, embeddings and a vector store built for your data, with permissions honoured and answers you can trace to a source.
AI across the delivery lifecycle
Working patterns for spec drafting, design exploration, test generation, code review, release notes and incident summaries, adopted by discipline.
Governance the board can defend
Acceptable-use policy, data-flow map, model and prompt versioning, and reporting on cost, quality and adoption that stands up to scrutiny.
How we embed
Clear stages. You're in control.
Day one: inside the team
Repos, data access, standups. Our engineer reads the prototype and the constraints, then tells you plainly what stands between it and production.
Week one: something real in front of users
A thin production slice with quality checks attached, so the value is demonstrated before you commit to anything longer.
Steady state: ship, measure, widen
Features shipped as pull requests, quality checks extended each sprint, and adoption widened one discipline at a time with a weekly written update.
Exit: your team owns it
Platform, tests and runbooks live in your repos, your engineers run the quality checks, and the patterns are documented for the rest of the org.
Why Releaseworks®
A team you can trust with production.
Senior engineers, based in London
Over 20 years building and running production SaaS and business-critical platforms for global brands across finance, health, and retail.
Full-time, background-checked staff
No subcontractors, no offshore handoffs. The person on the kickoff call is the person writing the code.
Embedded with your team
We work in your repo, your tools, and your standups, so your engineers see every decision and keep the capability after we leave.
Trusted advisors, not vendors
Founders and CTOs keep us on speed-dial long after the engagement ends, because we tell them the truth and stay accountable for outcomes.
Technologies
Certified, and hands on with it every day.
Our AI FDE engineers hold certifications in these tools and use them in production, not just on slides. If your stack is not listed, ask. We have almost certainly run it somewhere.
In practice
What working with us actually looks like.
Real productivity gains across product, design, engineering and QA, not just coders with autocomplete. Shipped AI features, adoption you can measure, governance the board can defend.
- Your repos, your cloud, your data. Nothing moves to our tenancy.
- Everything lands as pull requests your team reviews and merges.
- Named engineer, UK-based, background-checked. No rotating bench.
- Every AI change is judged against real test cases, not vibes.
- Day rate or monthly rate. Scale up for a launch, down afterwards.
- We run working sessions per discipline so adoption is not left to chance.
FAQ
The questions we get before kickoff.
We only need one feature shipped. Is this right for us?
If the scope is sharp and fixed, our AI to Production engagement is usually the better fit. The embedded model suits teams who want ongoing AI capability rather than a single delivery.
Which models do you use?
Whichever fits the task, the budget and your data constraints. We build behind a gateway so switching models is a config change, and our test cases tell you whether the switch helped.
Does our data go to a third party?
Only where you decide it should. We map the data flows first, keep sensitive workloads inside your own cloud where required, and make every route auditable.
Is this just developer tooling?
No. Developer productivity is part of it, but the bigger gains usually come from design, QA, product and delivery. We work with all of them.
How is this priced?
A daily or monthly rate depending on how much of the engineer you want. Three questions in the quote funnel and you will see the numbers.
Next step
Get a AI engineer on your team.
Three questions and you will see how our Forward Deployed Engineers work, who you get, and what it costs daily or monthly.