Everyone talks about AI. Almost nobody uses it reliably.
The gap between the demo and production is where most AI projects die. A chatbot that hallucinates is a liability. A classification model embedded in a validated pipeline is an asset. I design AI components that work inside your existing processes — with guardrails, not hope.
The problem
Most AI initiatives start with the technology: "We should use AI." The result is a pilot that impresses in the demo but fails in production — because nobody designed the process around it. No validation, no fallback, no way to know when it's wrong.
The approach
Start with the process, not the model. Identify where human judgement is the bottleneck — classification, extraction, summarisation, triage. Then embed AI at that exact point, wrapped in deterministic code that validates, routes and stores.
The result
AI components that are auditable, testable and replaceable. Your team trusts the output because the process guarantees quality — not the model alone.
Where AI adds real value
- Classification — Incoming tickets, documents, emails sorted by intent and urgency
- Extraction — Key data from contracts, invoices, reports pulled into structured fields
- Summarisation — Meeting notes, lengthy documents, knowledge bases condensed to essentials
- Triage — First-pass prioritisation so humans focus on what matters
- Translation & adaptation — Multilingual content at scale, with terminology control
Process
- Map — Document your current process, identify bottlenecks and manual steps
- Identify — Select AI candidates: high volume, fuzzy input, structured output
- Design — Specify the AI component with input/output contracts, validation rules and fallbacks
- Pilot — Build, test and measure against your real data — not a demo dataset