Scaling work
Going from a hundred users to ten thousand isn't a hosting problem. It's a re-architecture problem. AI inference costs, native app performance, API latency, database hot spots, queue backlogs, eval pipelines that haven't kept up with reality. We've taken a fair few systems through that gap and we know where the bodies are buried.
Working with UK teams across fintech, retail, SaaS and the public sector
Tune the path users actually hit
Frontend, API, database, queue, model, and region strategy get measured together.
A few teams we've shipped for
Fine in staging, dies in production. The actual cause is almost always one of three things and almost never the one the team thinks. We instrument the real request path (frontend, API, database, model), find the real bottleneck, ship the fix.
Every signup is another LLM call, another video transcode, another cluster node. The maths only works if you charge a fortune. Caching, batching, smaller models, queue smoothing, right-sized instances. Cost flattens out, growth becomes affordable again.
AI accuracy drifts as production data shifts. API error rates climb during peaks. Crash-free sessions drop after OS releases. Reports slow down as data grows. Nobody notices until users do. We set up real evals and real SLOs on live traffic, automate alerts, and make degradation visible before customers shout.
Sydney users get 800ms cold starts because everything lives in us-east-1. Multi-region deploys, edge inference, on-device, CDN strategy, whatever the latency maths actually wants. We pick by the data, not the trend.
Instrument what's running, capture real traffic, find the three changes that matter most. Not opinions, numbers. You get a written brief at the end of the week.
Caching, batching, model routing, fine-tunes, region split, queue smoothing, DB read replicas, whichever the data points to. Each change ships behind a flag with eval gates and an instant rollback. No flying blind.
Quarterly cost and quality review. A lot of teams keep us on a fractional retainer once the system is stable, basically a senior engineer on speed dial without having to hire one full-time.
10x
typical throughput gain after rework
Under 200ms
P95 target on tuned APIs and models
200+
AI and software projects shipped since 2019
If your team is shipping and hitting your targets, don't. If they've been stuck on the same three things for a quarter, an outside engineer often unblocks in weeks. We work alongside, not instead.
Full stack. We scale PostgreSQL and Redis as often as we scale LLM pipelines. React and Next.js performance, native Swift and Kotlin app performance, Node.js and NestJS APIs, Kubernetes on AWS, queues, caches, CDNs. Whatever the system needs.
All three, because at scale they aren't really separable. The model is rarely the bottleneck. It's usually the serving layer, the cache, the eval pipeline, the database, or the data plumbing.
Yes. We've shipped cloud, edge, and self-hosted AI systems where latency, privacy, or cost made the default cloud API path a bad fit. ONNX Runtime, custom quantisation, model routing, region strategy, and the infrastructure around it.
Three buckets: route easy work to cheap paths (small models, cached responses, read replicas), batch what can be batched, and right-size everything else. 40 to 70 percent reductions are normal once we look properly.
Designed in, not retrofitted. UK GDPR, Australian Privacy Act, SOC 2. EU and UK data stays in-region when needed, AU data in AU. PII redaction, audit trails and retention policies are part of the build.
8 to 12 weeks with a 2 to 4 person team is typical, then a fractional retainer if you want one. We also run fixed-fee 1-week assessments if you just want a second opinion before committing to anything bigger.
An actual engineer reads it the same day. You'll hear back inside one business day during UK business hours.
