Five service lines. One engineering standard.
Every engagement — from a trading platform to a predictive maintenance dashboard — gets the same discipline: production-grade code, real test coverage, and AI used where it earns its place.
01
AI-Native Web Applications
We design and build web applications on modern, typed foundations — React, Next.js, GraphQL, and event-driven backends — with AI capability treated as a core requirement, not a feature flag. That means real-time data pipelines, AI-assisted internal tooling, and automation woven into the architecture from the first sprint.
Real-time data pipelines and WebSocket streaming for live pricing, monitoring, and analytics
AI copilots and automation embedded in internal tools and customer-facing UX
GraphQL and REST APIs designed for reliability under regulated or high-stakes workloads
Cloud-native deployment on AWS or Azure with CI/CD and automated testing from day one
02
Mobile & Cross-Platform Apps
When a product needs to live on a phone as well as a browser, we build it once with shared business logic and platform-native polish — so mobile isn't a second, slower project trailing behind the web release.
React Native apps sharing core logic and data layer with the web platform
Native-feeling UI and performance on iOS and Android from a single codebase
Push, offline, and device-integration patterns handled from the architecture stage, not retrofitted
03
Complex, Mission-Critical Systems
Some systems can't afford to be wrong. We bring defense, finance, and healthcare-grade engineering discipline — TDD/BDD, security clearance experience, and audited delivery processes — to platforms where a bug isn't an inconvenience, it's an incident.
Real-time trading, pricing, and portfolio systems built on event-driven architecture
Geospatial and mission-critical platforms engineered for accuracy at scale (ArcGIS, spatial indexing, large multi-source datasets)
TDD/BDD test discipline (JUnit, Cucumber) validating every business-critical workflow
Delivery experience inside government and defense environments, including UK SC-cleared work
04
Legacy Modernization
Legacy systems rarely fail because the business logic is wrong — they fail because the platform underneath can't keep up. We modernize incrementally: strangler patterns, test coverage before refactors, and cloud migration paths that don't require betting the business on a big-bang rewrite.
Incremental migration paths from legacy stacks (Angular.js, jQuery, on-prem systems) to modern architecture
Test coverage established before refactors begin, so behavior stays provably correct
Cloud migration to AWS or Azure with CI/CD pipelines replacing manual deployment
05
AI Automation & Applied ML
We integrate large language models and applied machine learning where they actually move a business metric — document processing, financial analysis, predictive maintenance, diagnostic support — selecting the model (Claude, GPT, or another frontier model) that fits the task, cost, and compliance requirements rather than defaulting to one vendor.
LLM integration for document processing, financial analysis, and internal automation
Applied ML and predictive models (Amazon SageMaker and equivalents) for maintenance, forecasting, and pattern recognition
AI-assisted diagnostics and image analysis in regulated healthcare and industrial contexts
Vendor-neutral model selection based on task fit, latency, cost, and compliance — not a single-provider lock-in