AI built for your real data. Not a benchmark.
We build AI systems that process documents, predict outcomes, route decisions, and automate the work your team shouldn’t be doing. Each system is built around your actual data, your edge cases, and your accuracy requirements.
Most AI projects never leave the demo
The gap between an impressive ChatGPT demo and a production system that reliably processes ten thousand invoices a day is enormous. Most AI initiatives stall because they start with the technology instead of the problem. Teams build a proof-of-concept, show it in a meeting, get applause, then discover it breaks on real data, can’t handle edge cases, and has no path to production. The other common failure mode: buying an off-the-shelf AI product that does 80% of what you need, then spending more time working around the missing 20% than the tool saves. Applied AI works when it’s built around your specific data, your specific workflows, and your specific accuracy requirements.
Your team is manually processing documents that follow predictable patterns
Decisions are being made on gut feel when historical data could inform them
You’ve tried an AI product that works for demos but fails on your real data
Customer support spends hours answering questions that are already documented
You need to classify, route, or prioritise items faster than humans can manage
What we deliver
Every AI engagement produces a production system with monitoring, fallback handling, and clear performance metrics. Not a Jupyter notebook and a slide deck.
AI engineering capabilities
Document intelligence
OCR, layout analysis, and structured data extraction from invoices, contracts, medical records, and compliance documents. We build pipelines that handle messy real-world PDFs, not clean test files.
LLM orchestration
Multi-step AI workflows using GPT-4, Claude, and open-source models. Prompt engineering, chain-of-thought reasoning, tool use, and output validation with guardrails and cost controls.
RAG systems
Retrieval-augmented generation over your proprietary data. Vector databases, embedding strategies, chunk optimisation, and hybrid search for accurate, grounded AI responses.
Prediction & classification
Supervised and unsupervised models for demand forecasting, churn prediction, lead scoring, and ticket classification. Trained on your historical data with explainable outputs.
Computer vision
Object detection, image classification, quality inspection, and visual search. From retail product recognition to construction site monitoring.
Conversational AI
Customer-facing chatbots and internal assistants that answer questions from your knowledge base, escalate when uncertain, and improve over time with human feedback loops.
Model monitoring & drift detection
Production monitoring for accuracy degradation, data drift, and cost tracking. Automated retraining triggers and A/B testing frameworks for continuous improvement.
Data pipeline engineering
ETL pipelines, feature stores, and training data management. Clean data in, reliable predictions out: the infrastructure that makes AI systems actually work.
How we build AI systems
Problem definition & data audit
We identify the specific business problem, audit your available data, define success metrics, and determine whether AI is the right approach. Sometimes a well-designed rule engine is the better answer.
Prototype & validation
A working prototype tested on your real data, not synthetic examples. We measure accuracy, latency, and cost against your requirements before committing to a production build.
Production deployment
Hardened pipelines with error handling, monitoring, fallback logic, and human-in-the-loop workflows where confidence is low. Deployed with the same CI/CD rigour as any software system.
AI systems we’ve built
Invoice processing automation
Extracts line items, totals, and supplier details from thousands of invoice formats. Handles handwriting, poor scans, and non-standard layouts with 97%+ accuracy.
Internal knowledge assistant
RAG-powered chatbot that answers employee questions from policy documents, SOPs, and technical manuals. Cites sources and escalates when uncertain.
Predictive maintenance alerts
Sensor data analysis for equipment failure prediction. Alerts maintenance teams before breakdowns occur, reducing unplanned downtime by 40%.
Customer ticket classification
Automatically categorises, prioritises, and routes support tickets based on content analysis. Reduces first-response time from hours to minutes.
Contract review assistant
Highlights non-standard clauses, missing terms, and compliance risks in legal documents. Reduces manual review time by 60% while flagging items for human decision.
AI technology stack
LLM & NLP
ML frameworks
Vector & search
Data processing
Cloud ML
Monitoring
Business outcomes
Hours of manual work eliminated daily
Document processing, data entry, classification, and routing that used to require full-time staff now happens automatically.
Decisions informed by data, not intuition
Predictive models that surface patterns humans can’t see in historical data, with explainable reasoning your team can trust.
AI that works on your data, not demo data
Systems trained and validated on your specific formats, edge cases, and accuracy requirements, not generic benchmarks.
Production reliability, not prototype fragility
Monitoring, fallbacks, and human-in-the-loop workflows that handle the cases where AI confidence is low.
See how we've delivered for other clients.
Real projects with measurable outcomes, not hypothetical scenarios.
Have a problem AI could solve?
We’ll help you determine whether AI is the right approach, audit your data, and build a system that works in production, not just in demos.
Discuss your AI project