SEROKELL

Since 2015 we've worked where failure is expensive: inference infrastructure under live traffic, protocols moving billions, trading and settlement backends, lab instruments, drones. 60+ engineers, many with graduate degrees in maths and computer science. 90%+ client retention.

Most teams can ship a prototype in days now. What they can't do cheaply is build a system that holds up on year three - under load, under audit, after the original authors have left. That's our part.

WHAT WE DO

Audit and risk assessment. Architecture, security, performance, smart contracts, and AI-generated codebases. Teams generate a system fast, run it past AI reviewers, and still need someone to reconstruct what it's meant to do. Two independent auditors, threat modelling, findings with severity and repro steps, re-audit after your fixes.

Reliable AI systems. Inference infrastructure, retrieval pipelines, evals and regression testing for model behaviour, cost-per-token work - plus the unglamorous half: making features fail safely when the model is wrong.

Stabilise and harden. Systems that run but cost too much, scale badly, or break under load. Performance, SRE, incident readiness, migration off code nobody can maintain.

Build critical components. Settlement and trading backends, consensus and protocol work, hardware and scientific software. Rust, Nix, Python, TypeScript, Haskell; formal methods where risk justifies them.

HOW WE ENGAGE

Risk audit - fixed price and scope, prioritised roadmap of what will break.Hardening milestone - tied to a measurable target (latency, uptime, inference cost), not logged hours.Embedded experts - a small senior team owning closed scopes alongside yours. A C-level engineer owns every engagement and doesn't rotate off.