FarEdge Labs

Services

Contract development. The five below are the usual shapes a request takes, and they combine freely. Why no client is named is explained on the work page.

What you can ask for

  • Building something new

    Requirements through to production release — front end, back end, runtime platform, CI/CD and security design, all of it.

  • Rearchitecting an existing system, and cutting what it costs

    Revisiting architecture built around always-on servers. Usually that means moving toward serverless, without forcing anything that genuinely has to stay running. How much there is to save depends on the system, so the first thing delivered is a measurement and an estimate.

  • Getting machine learning into production

    The strength is less in training a model than in landing one somewhere it actually runs: ONNX conversion, INT8 quantisation, feature design, and measured inference performance.

  • Technical validation and proofs of concept

    Producing what you need in order to decide. Measured numbers from a real workload rather than a datasheet, shaped so the adoption question and the running-cost question can both be answered.

  • Making a codebase an agent can work in

    Getting a codebase into a state where an agent can read it and be useful: conventions and documentation designed for that, and AI review wired into CI. The point is that your team can keep working the same way after handover.

  • Joining your team

    Either holding one area as a member or leading the whole application as tech lead. Implementing tickets is not where it stops — design improvements get proposed actively, and when one is accepted the implementation comes with it.

Layers covered

Web front end
React + Vite / Next.js / TypeScript
Native applications
C++ / JUCE, real-time signal processing
Back end
Python, Node.js, Go, Java
Runtime platform
Serverless by preference, without forcing what has to stay running. AWS and GCP, provisioned as code, including platforms for large-scale distributed processing
Machine learning
Inference optimisation and quantisation, feature design, model development
AI features
RAG and knowledge bases, agents and tool calling, realtime voice

Languages

Python, TypeScript, Go, Java, C++, Dart