Applied Artificial Intelligence
Model research, evaluation methodology and practical integration of intelligent behaviour into software products. We focus on reliability and evaluation, not on chasing benchmarks.
TECHNOLOGY & CAPABILITIES
Engineering across the full stack.
Our capabilities span applied artificial intelligence, software architecture, performance engineering and digital experience. This page describes how we think about technology — not a feature list.
Engineering disciplines
Four areas of capability, each informing the others.
Software Architecture
Robust systems designed for maintainability. We prefer clear, boring solutions over clever ones that will not age well.
Performance Engineering
Latency, throughput and resource efficiency treated as first-class product requirements. Measurement is the minimum standard for any claim.
Digital Experience
Interface design and interaction craft — the part of the system a person actually meets. Accessibility and clarity are not optional.
Engineering practices
Our working habits are deliberately conservative. We would rather be slow and correct than fast and fragile.
Measure before claiming. Any performance, reliability or quality claim we make is backed by measurement we can reproduce. Benchmarks we cannot reproduce do not count.
Design for the maintainer. The next person to touch a system should be able to read it without asking us. That means clear structure, honest naming and documentation of the non-obvious.
Prefer boring technology. We do not adopt new tools because they are interesting. We adopt them when they solve a problem we can articulate and measure.
Test the failure modes. How a system behaves when things go wrong is at least as important as how it behaves when things go right.
Respect the runtime. Whether a system is a browser tab, a mobile app or a backend service, we treat its runtime characteristics — memory, latency, error handling — as part of the design, not something to fix afterwards.
Tooling and stack
Tooling is a means, not an identity. We work across a broad range of technologies and pick per problem.
Languages
Python, TypeScript, JavaScript, Rust, C/C++, Go — chosen per problem rather than by convention.
Web
Modern standards-based frontends, WebGL, WebAssembly, performant DOM, progressive enhancement.
AI & ML
Model integration, retrieval architecture, evaluation pipelines, prompt design, cost and latency measurement.
Systems
Real-time networking, low-level performance work, profiling, benchmarking.
Cloud & DevOps
Reproducible deployments, observability, cost-aware infrastructure choices.
Design
Editorial typography, minimal futuristic interfaces, accessible interaction patterns.
What we will not do
Just as important as our capabilities is what we decline to build. We do not build systems whose primary purpose is manipulation, surveillance, or the removal of meaningful human oversight. We do not ship performance or quality claims we cannot reproduce. We do not treat accessibility as a phase-two concern.
If a project is a poor fit for these standards, we will say so before work begins rather than after.
Work with us on a serious problem
If you have an engineering problem worth solving carefully, we would like to hear about it.