AI & software engineering · since 2016
Most AI pilots
never reach
production.
That's rarely a model problem. It's architecture, data plumbing, evaluation, latency, and cost — the unglamorous engineering that decides whether a demo becomes a system. We've been doing that work for 10 years.
The problem
The distance between a working demo and a working system
01 — Demos are cheap now.
A convincing prototype takes an afternoon. That's precisely why a prototype no longer proves anything about whether you can ship.
02 — Production is where the cost lives.
Evaluation harnesses. Token budgets that don't blow up at scale. Latency under real concurrency. Fallback behaviour when the model is wrong. Audit trails. Access control. None of it appears in a demo.
03 — This is ordinary engineering.
Hard, but not mysterious. The same discipline as any other production system, applied to a component that happens to be probabilistic. We shipped production systems before the models existed.
What we build
Five practices, one engineering team
AI & Agentic Systems
LLM integration, retrieval, agent workflows, evaluation, and the cost and latency work that keeps them viable.
Custom Development
Business systems built to your actual processes, from concept through deployment and the years after it.
Cloud & Platform
AWS, Azure, and GCP. Migration, cloud-native builds, Kubernetes, and infrastructure that scales without surprises on the invoice.
Mobile & App
Native iOS and Android, or React Native and Flutter when cross-platform is the right call. Offline, push, store deployment, maintenance.
Quality Engineering
Automated suites, performance testing, security validation, and CI/CD. The reason the other four hold together.
Track record
Systems in production
Engagement
Four phases, no mystery
01 · Discovery
Requirements and constraints, technical feasibility, architecture, timeline and resourcing. You get a written technical assessment whether or not you proceed.
02 · Design
Architecture blueprints, security parameters, scaling strategy, monitoring. Decisions get documented with their trade-offs, so the reasoning survives staff changes.
03 · Build
Agile sprints with working software at the end of each. Continuous integration and deployment from day one. You see progress weekly, not at the end.
04 · Run
Deployment, migration, performance monitoring, ongoing maintenance. Most of a system's life happens after launch, and most agencies stop caring at exactly that point.
Under the hood
The stack, stated plainly
Solution Architecture
Microservices and cloud-native systems designed to scale predictably. We size for the load you'll have in two years, not the one you have today.
Web Development
React, Angular, and Vue on the front end. Node.js, Python, and Java behind it. Payment gateways, third-party integration, database performance tuning, SSL and API authentication, and PWAs where a native app isn't warranted.
Mobile Development
Native iOS in Swift with SwiftUI and UIKit. Android in Kotlin with Jetpack Compose. React Native and Flutter for cross-platform. Offline functionality, push notifications, location services, store deployment, and the maintenance that follows.
Technology Stack
React with Next.js where SEO matters, Angular for enterprise applications, Vue for dynamic interfaces. Node.js for scalable APIs, Python for AI and ML workloads, Java for enterprise systems. AWS, Azure, and GCP with serverless, containers, and microservices. MongoDB for flexibility, PostgreSQL for complex transactions, Redis for caching.
Quality Engineering
Jest, Selenium, and Cypress for automated coverage. Performance testing that finds bottlenecks before users do. Penetration testing, vulnerability scanning, and compliance verification. CI/CD that makes deployment boring.
Tell us what you're building.
Send the problem, not a spec. First conversation is with an engineer, not a salesperson, and you'll get a straight answer about whether we're the right fit.
Start a project