We test what AI can reliably do before your business depends on it
The Lab turns focused prototypes into observable and evaluated systems. We design agents, knowledge tools and automations around real operational work.
Applied AI that goes beyond a chat box
We focus on AI systems that teams can inspect, measure and improve. Each prototype starts with a real workflow and a clear definition of success.
Agentic workflows
Specialised agents coordinate research, execution and quality review while explicit state and approval gates keep the process observable.
Knowledge assistants
RAG systems retrieve from governed company material, cite evidence and respect permissions instead of improvising from generic knowledge.
Document intelligence
Extraction pipelines turn contracts, forms, reports and attachments into validated structured data ready for operational workflows.
Human-in-the-loop automation
AI handles repetitive interpretation and drafting while people retain control over sensitive decisions, exceptions and external actions.
Evaluation and guardrails
Regression datasets, structured outputs, policy checks and tracing make model behaviour measurable before and after deployment.
Model and cost strategy
Tasks route to the smallest capable model, with caching and fallbacks designed around latency, privacy, quality and operating cost.
Useful autonomy needs visible control points
Context enters, an orchestrator selects bounded capabilities, evaluation checks the result and people approve consequential actions.
The model is replaceable. Durable value lives in your context, tools, evaluation cases, permissions, audit trail and workflow.
Experiments grounded in real operating problems
Capability previews that we adapt to each client's data, controls and success criteria.
Multi-agent delivery desk
A manager routes engineering tasks to stack specialists, records lifecycle state and sends every result through evidence-based QA.
Evidence-first knowledge assistant
A permission-aware assistant answers operational questions from private documents and exposes the passages used for every response.
Document intake operator
An extraction workflow classifies incoming files, validates required fields and sends uncertain records to a focused review queue.
Prototype quickly. Prove quality. Introduce autonomy carefully.
We begin with a narrow decision or workflow, assemble representative examples and establish a baseline before building.
- 1Define the outcome and unacceptable failures
- 2Prototype against representative private data
- 3Create evaluation cases for quality and safety
- 4Integrate tools with least-privilege permissions
- 5Pilot with real users, measure, then expand
Have a workflow worth testing?
Bring us the process, constraints and examples. We will help you find a responsible first experiment.
Scope an AI prototype