Practical AI, shipped inside real systems.
We apply AI where it removes measurable work, not as a feature badge. Each prototype is evaluated against a baseline, and every consequential action keeps a person in control.
- Focus areas
- 5
- Approach
- Evaluate before deploy
- Output
- Working prototypes
Practical AI, shipped inside real systems
We apply AI where it removes measurable work — not as a feature badge. Every prototype is evaluated against a baseline before it touches production data.
Ask the lab assistant a question
This is a simulated interface with fixed responses. Pick a prompt below to see how an enterprise automation conversation is structured.
Five places the return is measurable
Each capability area is chosen because it removes volume, latency or error from work that already happens. The examples are the kinds of systems we build.
- 4 examples01
AI Agents
Goal-driven agents that query your systems, take bounded actions and hand control back to a person when confidence drops.
- Support ticket triage
- Internal knowledge assistant
- Order status investigation
- Supplier follow-up drafting
- 4 examples02
Business Automation
Event-driven workflows that connect existing tools, remove repetitive handling and keep every step logged for later review.
- Invoice approval routing
- Lead qualification and assignment
- Employee onboarding steps
- Inventory reorder triggers
- 4 examples03
Document Intelligence
Extraction pipelines that read invoices, contracts and forms, returning structured fields with confidence scores and a queue for review.
- Invoice field extraction
- Contract clause detection
- Purchase order matching
- Identity document parsing
- 4 examples04
Recommendation Systems
Ranking and suggestion services tuned to your catalogue and behaviour data, improving relevance without exposing individual customer records.
- Product recommendations
- Content personalisation
- Next-best-action suggestions
- Search result ranking
- 4 examples05
Predictive Analytics
Forecasting models that turn historical operations data into demand, capacity and maintenance signals your planners can act on weekly.
- Demand forecasting
- Churn risk scoring
- Inventory optimisation
- Predictive maintenance
How we evaluate a model or feature
Four gates between an idea and production. A feature that fails a gate is reworked or stopped, not shipped with a caveat.
Baseline measurement
We measure how the process performs today, including its error rate and the cost per item, so any improvement can be attributed rather than assumed.
Offline evaluation on a labelled set
A representative labelled set is scored against the baseline. Accuracy, failure modes and confidence thresholds are agreed before any production access.
Shadow mode in production
The feature runs alongside the live process, producing predictions without acting on them. Disagreements are reviewed while nothing is at risk.
Guarded rollout with human approval
The feature is enabled behind a flag for a limited scope, with a person approving every consequential action and a rollback path ready.
Controls that ship with the feature
Guardrails are part of the deliverable, documented and tested alongside the model integration rather than described in a slide.
PII redaction
Personal data is detected and redacted before it reaches a model, and the redaction step is tested like any other component.
Prompt injection defence
Untrusted content is treated as data, never instruction. Tool access is allow-listed and outputs are validated against a schema before use.
Human-in-the-loop approval
Anything with financial, safety or employment consequence requires a named person to approve the action, with the model's reasoning visible.
Full audit trail
Every prompt, response, tool call and approval is logged, so a decision can be reconstructed after the fact for review or compliance.
Cost and latency budgets
Each feature has explicit cost and response-time budgets, monitored in production, with graceful degradation when a model is slow or unavailable.
Documented
Every guardrail above is written into the handover documentation, with the tests that demonstrate it and the owner responsible for it.
How we handle securityWhat we will not do
There are places where automation should not remove the person from the decision. We are direct about where those are.
AI is well suited to reading, ranking, summarising, forecasting and routing. It is not well suited to carrying responsibility. We will not build systems that make autonomous decisions over money, safety or employment without a person signing off on the outcome.
- No unattended movement of money or credit decisions.
- No autonomous action affecting physical safety or medical care.
- No automated hiring, promotion or dismissal decisions.
- No silent changes to a record that a person is accountable for.
Have a process worth automating?
Describe the task, the volume and the error that hurts most. We will tell you whether AI is the right tool and how we would measure the result.