AI that works in
production,
not just in demos
We engineer custom AI systems built around your data, your processes, and your measurable goals - not off-the-shelf wrappers. From LLM fine-tuning and RAG pipelines to intelligent agents and computer vision, we own the full build through to live deployment.
Six AI disciplines.
One accountable team.
We don't hand off to sub-contractors. The team that scopes your system builds it, tests it, and monitors it after go-live.
Large Language Models & RAG Systems
Fine-tuned LLMs and retrieval-augmented generation pipelines built on your internal knowledge base - not hallucination-prone generic models. We design the full data ingestion, chunking, embedding, retrieval, and evaluation loop so the system gives accurate, citable answers from day one.
Intelligent Agents & Automation
Multi-step AI agents that connect to your APIs, databases, and third-party tools - automating workflows that previously needed human judgment. We build with defined guardrails, audit trails, and human-in-the-loop checkpoints where stakes are high.
Computer Vision
Custom-trained vision models for defect detection, quality control, document OCR, and real-time scene understanding - tuned on your production images, not stock datasets.
Predictive Analytics & Forecasting
Demand forecasting, anomaly detection, churn prediction, and risk scoring models that feed into your existing dashboards or APIs - with full explainability documentation for regulated industries.
MLOps & Production Infrastructure
A model that performs well in a notebook is worthless without robust serving infrastructure. We build the CI/CD pipelines, feature stores, model registries, drift detection, and retraining triggers that keep your AI system accurate at scale - not just at launch.
AI Strategy & Discovery
Not sure where AI creates genuine leverage in your business? A focused two-week discovery sprint identifies the three to five highest-ROI use cases, stress-tests feasibility, and delivers a build roadmap - before any commitment to a full project.
From problem statement
to production model
Discovery & Data Audit
Business objective definition, data inventory, quality assessment, and feasibility scoring across candidate use cases.
Model Development
Baseline model, feature engineering, fine-tuning or RAG pipeline construction, and initial evaluation against defined benchmarks.
Integration & Testing
API development, integration with existing systems, performance testing under load, and adversarial prompt / edge-case validation.
Production & MLOps
Live deployment, monitoring setup, drift detection, retraining pipeline, and 90-day hypercare support with weekly performance review.
Where our AI systems run today
Live results from production deployments - not projections from a whitepaper.
Clinical Document Intelligence
A RAG-based system trained on clinical guidelines, patient records, and drug interaction databases - used by care teams to surface relevant protocols during consultations.
Real-Time Credit Risk Scoring
A gradient-boosted ensemble model replacing a legacy rules engine - assessing 200+ features in under 80ms per request, with full SHAP explainability for regulatory review.
Visual Quality Control
Computer vision system inspecting 48 frames per second on the production line - detecting surface defects that manual inspection missed at a rate of 99.3% precision.
Demand Forecasting & Route Optimisation
A hybrid LSTM-Prophet pipeline generating 14-day demand forecasts that feed into a route optimisation engine, reducing fleet idle time and overstocking across 60+ depots.
Contract Review Agent
A multi-step agent that reads, classifies, and flags non-standard clauses in commercial contracts - cutting first-pass review time from three hours to under twelve minutes.
Personalised Recommendation Engine
A real-time recommender system processing live session data and purchase history to serve contextually relevant suggestions - outperforming the previous collaborative filter by 2.3×.
What separates a working AI system from a costly pilot that never ships
Most AI projects stall between prototype and production. The reasons are consistent: poor data foundations, no MLOps plan, or a team that hands off the moment the notebook runs. We're structured to avoid all three.
Production-first engineering from day one
We design for deployment constraints - latency budgets, infrastructure costs, update frequency - during model development, not after. Systems that look impressive in isolation but can't scale are a waste of everyone's time.
We optimise inference costs, containerise from the start, and test against production traffic volumes before go-live.
Evaluation-driven development
We don't ship a model because it "seems to work." Every build includes a defined evaluation framework - task-specific metrics, human evaluation protocols, and regression test suites that catch capability drift before it reaches users.
Hallucination rates, retrieval precision, F1 scores, and business KPIs are tracked from day one and included in every sprint review.
Data quality over model sophistication
The most common failure point in enterprise AI is dirty, incomplete, or poorly labelled data - not the choice of model architecture. Our discovery process includes a rigorous data audit before any code is written.
We establish data pipelines, labelling workflows, and ongoing quality checks that keep model performance stable as your data evolves.
Full-cycle ownership, not a hand-off
Once we deploy, we don't disappear. Every engagement includes 90 days of post-launch support, monitoring infrastructure, and a defined retraining cadence. Your model improves over time - it doesn't degrade silently.
On-call incident support, weekly performance review, and a model registry with version history included in every production engagement.
Model-agnostic. Best tool
for every problem.
We select and combine the right frameworks based on your data, latency needs, and infrastructure - not based on what we already know.
The questions that come up before every AI project
Honest answers on data requirements, timelines, costs, and what separates a working system from a demo. Anything else? Just ask us directly.
Let's scope your AI system properly
Book a free 45-minute call with one of our AI engineers. We'll look at your use case, your data, and your infrastructure - then tell you honestly what's feasible, what it will cost, and how long it will take. No pitch decks.