AI, deployed and operational.
We connect AI to the systems your business already runs on. Cloud deployment, API integrations, model fine-tuning, security, and ongoing support — we handle the operational layer so your AI doesn't just exist, it works.
The operational layer that keeps AI running.
There is a wide gap between a model that works in a notebook and a system the business can depend on. Closing it is its own discipline — deployment, integration, security, and observability that let AI run inside your real environment, at real load, against real consequences. That operational layer is what we build, so your AI keeps working long after the demo.
Where we go deep
- Cloud & infrastructure deployment — AWS, GCP, or Azure, containerised and autoscaling, provisioned as code and built to fail gracefully.
- API & systems integration — wiring AI into your CRM, ERP, data warehouse, and internal apps so it lives where the work already happens.
- Model fine-tuning & evaluation — tuning and rigorously evaluating models against your data and your quality bar, not generic benchmarks.
- Security & governance — auth, secrets management, PII handling, OWASP hardening, and audit trails baked in from day one.
- Observability & monitoring — cost, latency, and quality evals with alerting, so you see problems before your users do.
- Ongoing support & SLAs — managed operations, updates, and response commitments that keep the system healthy over time.
Operated like production infrastructure, because it is.
We work in tight, senior teams alongside yours, in a four-step rhythm: Discover your systems, constraints, and risk profile, Design an integration and deployment architecture that fits, Build the pipelines, security, and observability that earn production, and Sustain it with monitoring, support, and iteration. Infrastructure as code, tested, documented as we go.
Frontier models, production infrastructure.
We work daily with frontier large language models — Anthropic's Claude, OpenAI's GPT, and open-source models — alongside the cloud, API, vector, and data infrastructure needed to run them. Every choice is weighed for reliability, security, and cost control, so the system holds up under real load and stays affordable to run.
Employee super-app for a nationwide restaurant chain
Six integrated codebases, 300+ API endpoints, serving thousands of crew daily — modernised and hardened with multi-agent refactoring and AI security audits.
Read the case ›Production Monitor — when to cook, computed in real time
An offline-first PWA with a walk-back batch scheduler — spec to pilot-ready build in days, with 46 unit tests on the core logic.
Read the case ›Already running AI? Let's make it reliable.
Tell us where AI needs to be deployed, integrated, or supported. We reply within two working days.