AI Solutions, engineered to ship
Predictive models, computer-vision systems, RAG pipelines, AI agents, and the MLOps to keep them running. Designed, built, and deployed end-to-end.
- CTOs & Heads of EngineeringNeed an AI system in production - not another notebook or PoC that stalls.
- Product Leaders & Heads of ProductShipping an AI feature customers asked for, with the rigour the rest of the product gets.
- Heads of Data Science & MLHave models that work in the lab and need the platform to make them live.
- Heads of Innovation & COOsRunning operational pilots that now need to scale beyond a single team.
- CISOs & Heads of InfrastructureMoving AI workloads on-prem, to sovereign cloud, or into air-gapped environments.
- “Your prototype works on demo data but hallucinates in production.”
- “A model lives in someone's notebook and has never seen real traffic.”
- “Customers are asking for an AI feature and you don't have an ML team yet.”
- “You've outgrown rule-based systems but don't know where ML actually fits.”
- “Your GenAI assistant works for the demo and breaks for the lawyer.”
- “You need to move on-prem or to a sovereign cloud and don't know how.”
- “Inference costs are climbing and no one can tell you which prompt is to blame.”
- “A model in production has started drifting and the alerting was never wired up.”
- “Your risk committee wants an evaluation harness before they sign off on launch.”
Four tracks, one delivery discipline
Pick the track that fits the problem. Every one ships under the same engineering standards: real evaluation, real monitoring, real handover.
Predictive AI
Forecasts, scores, and classifications you can act on.
Classical machine learning is still where most enterprise AI value lives - and it's still where most enterprise AI projects fail. We build production-grade predictive systems that hold up under the scrutiny of risk, compliance, and operations teams.
- Customer churn prediction with intervention scoring
- Demand & sales forecasting (multi-seasonal, multi-SKU)
- Fraud and anomaly detection in transactional data
- Recommendation engines (collaborative, content-based, hybrid)
- Credit and propensity scoring
- Dynamic pricing models
- Predictive maintenance for industrial assets
- Customer segmentation and lifetime-value modelling
- Routing and constraint optimisation
Eight artefacts your team owns after handover
Code, models, infrastructure, documentation - all yours. No proprietary runtime, no per-call fees.
Production AI system
Live in your environment - cloud, on-prem, hybrid, or air-gapped.
API + SDK
REST or gRPC endpoints, client SDKs in your stack's primary language.
Evaluation harness
Reproducible test suite + metrics dashboard - so model regressions are caught before users see them.
CI/CD for ML
Automated training, testing, and deployment pipelines wired to your existing dev workflow.
Source code + IP
You own everything. No platform lock-in, no per-call fees to us.
Docs + runbooks
Architecture diagrams, operational runbooks, on-call playbooks, written for your engineers.
Monitoring + drift
Production observability with alerting on accuracy degradation and data drift.
Handover sessions
Live walkthroughs with your team until they own it. Recorded for the next hires.
From frame to operate, in five sequenced steps
Weekly demos, milestone billing, code in your repo from day one.
Frame
Problem definition, success metrics, evaluation criteria. We refuse to start building until "done" is measurable.
Prepare
Data cleaning, exploratory analysis, feature engineering, baseline establishment. The least glamorous phase, the most predictive of success.
Prototype
Model selection and experimentation against the evaluation criteria. Multiple approaches in parallel, weekly demos with your team.
Productionise
Deployment, monitoring, guardrails, CI/CD, observability. The moment a model leaves a notebook is the moment its real life begins.
Operate
Knowledge transfer, runbooks, optional managed-service retainer. Your team owns it; we're a phone call away.
Four ways to engage us on a build
PoC Sprint
Working prototype on your real data. Ends in a go / no-go demo and a production plan.
Production Implementation
Full delivery from frame to operate. Milestone-based, weekly demos, code in your repo from day one.
AI Co-development Partnership
Embedded with your product team for a quarter or more. Best when AI is becoming a core competence.
AI Audit / Validation
Independent review of an existing AI system: performance, safety, drift, fairness, cost.
Industries we serve
The case studies and articles on this site are a slice; we've shipped AI across many more sectors than those examples.
Tech & frameworks we ship with
A pragmatic mix of best-in-class open source and managed services. Always picked to fit the team that will own it after handover.
- We don't ship demo-ware. If it can't pass evaluation under real traffic, we don't call it done.
- We don't lock you in. No per-inference fees, no proprietary runtime.
- We don't take the brief at face value when the data tells us a better question to ask.
Frequently asked
All three. We've shipped to AWS, Azure, GCP, OVH Sovereign Cloud, and on-prem clusters including air-gapped environments for defence and regulated finance.
Got a build in mind? Let's scope it.
Tell us where you are. We'll tell you whether it's a 4-week PoC, a 4-month implementation, or a partnership.
