What we build
Data to decision, engineered end to end.
Four capabilities, one path, from the data foundation to the decision your business runs on. We build whichever part you need, and we build it to reach production, not a demo.
Data · ML · Agents · Apps
Case studies
Where we’ve gone deep, you start ahead.
Each engagement leaves a reusable foundation. In the domains we know cold, the pattern is already built, so your first system is a head start, not a blank sheet.
Client work
Mining & metals
Copper Recovery, Mine to Concentrate
A production data and ML system across the full circuit: 31 models from feed characterisation to final concentrate, a plant-wide flotation optimiser, and retraining under human sign-off.
Customs Broker Workbench
Retail B2B Agent Foundation
Software quality
Agentic Quality Engineering
Defects fixed in minutes, inside a boundary the fix can’t cross. Root cause in under 2 minutes, a reviewed, tested fix in under 15, and nothing ships until a human approves.
Built on ourselves · Internal
We run it on ourselves first.
Running on our own support, delivery, hiring and onboarding work. Then we build it into yours.
Internal · Support and delivery
LeapOps
AI drafts, a human approves, nothing else writes. Customer email becomes reviewed tickets, follow-ups link instead of duplicating, and bugs become stories that cite real code.
Internal · People operations
Agentic HR Operations
One agent for the employee lifecycle. Every application screened against our own bar, onboarding finished in a chat, and sensitive data masked before the model sees it.
One path, not four purchases
The parts are capabilities.
The whole is a system.
01 · The foundation
Data Engineering
No decision is better than the data underneath it. We make your data usable, current, and trustworthy, and wire it into the systems you already run.
Batch and streaming pipelines
Warehouses and lakehouses
Data quality, lineage, and observability
Integration with your ERP, CRM, and legacy systems
Everything above inherits whatever the data gets wrong, so we start here.
02 · The intelligence
AI & ML
Data becomes intelligence, models tuned to your domain and your data, and the evaluation that proves they hold up before you trust them.
Domain-specific models, not off-the-shelf guesses
Prediction and optimisation systems
Self-monitoring and retraining loops that adapt as reality drifts
Evaluation harnesses, measure before you rely on it
Intelligence that survives contact with production, not a notebook that looked good once.
03 · The action
Agents
Agentic systems that act, running real workflows across your tools and data, with the accountable step kept human where the stakes are high.
Agents that act inside the tools you already use
A person approves where the stakes are high
Every action traced to its data and authority
Read-only first, trusted to act once it has earned it
Agents that do the work, with your people still holding the decision.
04 · The product
AI-Native Apps
The interface your team actually works in, designed with product care, decision-first where it counts, so the “why” is visible and the human stays in control.
The interface your team lives in, not another dashboard
Decision-first where the stakes are high
Built into your data and agents, not bolted on
Shaped by product engineers who care whether people use it
Software people want to use, the difference between a system that ships and one that sticks.
Built to be trusted, and to be yours
Decision-first where it counts.
On the systems that matter, we build so every decision is traceable to the data and the authority behind. The system is trustable enough to actually run and delegate to.
Frontier models will keep changing hands; what we build on top, and the judgment it captures, stays yours.
Model-agnostic by design, never locked in