AI integration engineering
Retrieval pipelines, structured outputs, prompt versioning, and LLM behavior monitoring for production. The demo worked — we make sure the next sprint doesn't break it silently.
SOFTWARE ENGINEERING
Experienced engineering,
without the management overhead.
Named engineers from week one, accountable for the full stack — not a rotating pool of juniors or a tech lead you rarely see. Scoped to what needs to ship, not to how many hours you'll approve.
What we do
Engineering that ships and holds.
API design & system integration
RESTful and GraphQL APIs, third-party integrations, and event-driven architectures. We design for the downstream consumers and the teams who will maintain the integration, not just the immediate caller.
Full-stack product engineering
End-to-end feature delivery across frontend, backend, and data layers. One engineer owns the domain — frontend, backend, data — without handoffs between a discovery team and a delivery team.
Performance optimisation
Database query analysis, caching strategy, and request latency reduction. We diagnose root causes, not symptoms, and fix them in a way that holds under load — not just under the current traffic pattern.
Technical debt resolution
Incremental refactoring alongside feature delivery. We scope the risk, prioritise by business impact, and make measurable progress without stopping the product or creating a six-month freeze.
Who it's for
When Engineering is the right call.
Senior capacity inside your hiring window
Experienced engineers take four to six months to hire. When a board deadline, a fundraise, or a critical integration is inside that window, you need capacity now — not after a recruiting cycle.
Architecture decisions with long-term consequences
You're at an inflection point — a new integration, a scaling problem, a platform migration. The decision will be lived with for years. It needs someone who has made it before, not someone learning on your production system.
AI features that need to survive the next sprint
Your LLM feature worked in the demo and in staging. Production is different — traffic patterns, edge cases, behavior drift, silent regressions. You need engineers who understand the gap, not ones who hand it back to the ML team.
Technical debt that's compounding against the roadmap
Velocity is falling and every new feature touches old code. The team knows what's wrong but can't stop to fix it without dropping active delivery. That's not a discipline problem — it's an architecture problem.
No lead available to manage external engineers
You need engineers who self-direct — who surface blockers early, flag risk without being asked, and communicate directly with the technical stakeholders, not through a PM translating requirements.
Fintech, healthcare, or another regulated context
Audit trails, access control, data residency, and compliance constraints aren't retrofit work — they're architectural decisions made in week one. You can't afford engineers who learn this after they've shipped.
How we deliver
From scope to shipped — without the coordination tax.
Stage 01
Technical scoping
We map the problem domain, the existing codebase constraints, the integration points, and the risk surface before writing a line of code. Decisions made here determine whether the engagement delivers or drifts.
Output: scoped plan, risk flags, defined acceptance criteria
Stage 02
Named engineer from week one
A senior engineer is assigned to your domain. They run it — architecture decisions, code reviews, async communication with your team — without an account manager translating between you. The engineer you meet in the scoping call is the engineer doing the work.
Output: named engineer active, first working increment in your repository
Stage 03
Iterative delivery with direct visibility
Structured releases on a cadence you can plan around. No black-box periods. You see the work, raise concerns early, and the engineer responds directly — not through a PM's interpretation of your feedback.
Output: shipped increments, documented decisions, live feedback loop
Stage 04
Handoff or continuation — both are clean
At close, you receive an architecture decision log, a maintained test suite, integration runbooks, and a dependency map your team can operate without us. If the engagement continues, it runs under the same engineer with the same context — no re-onboarding, no knowledge loss.
Output: architecture log, runbooks, test suite — or ongoing engagement under existing context
70+ production systems delivered since 2019
95% client retention across multi-quarter engagements
40+ senior engineers across EU and US
7 years building for fintech, healthcare, and regulated industries
What you get
Concrete outputs, not just capacity.
ENGAGEMENT DELIVERABLES
Architecture decision record
Every significant technical choice documented — what was evaluated, what was ruled out, and the reasoning. Reviewable by your team, not just the engineer who made the call.
Working, tested software
Shipped to your repository, peer-reviewed, with test coverage scoped to what actually matters in production — not coverage numbers built around implementation details.
Integration runbooks
Step-by-step operational documentation for every system your internal engineers will need to operate, extend, or debug after the engagement closes.
Risk and dependency map
What is coupled to what, where the known failure modes are, what to monitor in production, and what decisions were deferred and why.
Selected work
Engineering work with architecture, delivery, and handoff.
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Common questions
Do you replace our internal engineering team or work alongside them?
Neither framing quite fits. We take a specific domain — a feature area, an integration, a system that needs rebuilding — and run it end to end: scoping, architecture, implementation, and release. Your team keeps control of priorities and decisions that affect the rest of the product; we don't insert a layer of process between you and the work.
What happens if the named engineer leaves or gets reassigned?
The architecture decision log, runbooks, and integration maps described in "What you get" exist for exactly this reason — they're written as we go, not reconstructed after the fact. If continuity is interrupted, the documentation is built to make the transition possible without starting from zero.
Can you take over a codebase you didn't build?
Yes, and it's most of what "technical debt resolution" actually is. The first stage is scoping — we read the code, find the constraints nobody wrote down, and flag what's risky before committing to a delivery plan. We don't promise velocity on unfamiliar code without that step.
How does pricing work — hourly, fixed scope, or retainer?
Engagements are scoped around what needs to ship, not tracked hourly. The exact structure — a fixed-scope build, an ongoing monthly capacity arrangement, or a hybrid — depends on whether the work is a defined deliverable or continuous engineering capacity. This gets defined during scoping, before any commitment.
Do you work inside our existing stack, or do you push your own tools and frameworks?
Inside your stack. A new framework or tool only gets introduced if it solves a specific problem your current setup can't, and that gets flagged and discussed before it happens — not decided unilaterally mid-engagement.
What if the AI feature we need help with doesn't have a clear "correct" output?
That's the normal case for AI features, not an edge case. Before we touch the code, we define what "working" means for that feature — acceptable ranges, known failure modes, what a regression looks like. Testing and monitoring get built against that definition, not against a single expected output.
Ready to scope an engineering engagement?
Book a call and we'll review your system context, team setup, and the specific challenge — then outline what an engagement would look like and whether the fit makes sense.