A focused first engagement
Choose a specific workflow, identify its owner and document the current baseline. A discovery output should make the proposed scope, dependencies and acceptance criteria explicit.
Services
Applied AI consulting and custom software, built around your operations.
We work with your operations team to scope, build and evaluate AI systems around your data and workflows. Each engagement defines what will be delivered, how it will be tested and who will operate it.
The four phases, and what each one leaves behind
01
Embed & Map
Two weeks inside your operation, not on a call about it. We sit with the people doing the work and write down what they actually do: which spreadsheet, which exception, which thing they know that is written nowhere. That document is the specification, and it is usually the first time the process exists on paper.
You get: A written process map, a ranked list of automation targets, and an honest note on which ones are not worth building.
02
Architect
We design the system around your data and your constraints. Model choice comes last, because it is the part most likely to change. Retrieval strategy, evaluation, guardrails, failure handling and where a human stays in the loop come first.
You get: A system architecture, the data contracts it depends on, and the evaluation set we will judge it against before anyone writes production code.
03
Build & Ship
Build a narrow workflow end to end, test it with representative examples and expand after agreed acceptance checks. Rollout depends on data, access and integration readiness.
You get: A running system your team uses daily, with tests, monitoring and a rollback path.
04
Compound
Review corrections and exceptions with the workflow owner. Evaluate proposed changes before adopting them; feedback does not automatically make a system more accurate.
You get: A documented improvement process with evaluation results and agreed operating responsibilities.
What we build
AI strategy and system design
Where AI belongs in your operation and, more usefully, where it does not. Delivered as an architecture and a build sequence, not a slide deck.
Retrieval and RAG systems
Hybrid retrieval, reranking, chunking strategy and evaluation pipelines over your own documents and data. The part that decides whether the answer is right.
Multi-agent orchestration
Specialised agents coordinating on a real workflow, with fallback handling and human escalation, rather than one prompt doing everything badly.
Integration with the systems you already run
ERPs, CRMs, dialers, warehouse systems, government registries. The integration is usually the project; the model is rarely the hard part.
Data analytics and forecasting
Demand forecasting, anomaly detection and revenue leakage analysis built on your operational data, feeding decisions rather than dashboards.
Team enablement
Your engineers work alongside ours. When we leave, the system is yours to run and extend, and we document it on that assumption.
Where this fits, and where it does not
We are not
- A SaaS platform you log into
- A ChatGPT wrapper or AI chatbot vendor
- A consulting firm that delivers slide decks
- A one-size-fits-all product for any industry
- A freight forwarder, insurer, or energy company
We are
- Your embedded AI engineering team
- Builders of proprietary, production-grade AI systems
- Engineers who evaluate and operate working software
- Custom-built around your exact operations
- A team that connects software to operational outcomes
Before we build
A clear scope, from first workflow to handover.
Data and access
Agree which representative examples, systems and permissions are needed. Establish data handling and retention requirements before using production information.
Evaluation and rollout
Define how quality, latency and cost will be measured. Set human-review boundaries, fallback behavior and rollout checkpoints appropriate to the workflow.
Ownership and ongoing support
Document deliverables, code and infrastructure ownership, access transfer, operating instructions and support responsibilities in the agreed scope.
A good fit is a repeatable operational problem with a responsible owner and accessible examples. If standard software already meets the need, discovery should make that clear too. Timing and budget follow the agreed scope.
Discuss your project →