Explore ideas, technology, and real-world solutions we're building and learning from.
How organisations move from disconnected data to connected systems that support better decisions, automation and intelligent workflows.
What separates an AI demo from a production system: grounding, evaluation, guardrails, monitoring and integration with the systems that hold the real record.
Conflicting numbers are rarely a reporting bug. They are usually a definition problem, and the fix is governance, not another dashboard.
How a critical process moves off a shared workbook and into a real system without stopping the business that depends on it.
Sometimes the right recommendation is a process change or a short script. How to tell when AI is the wrong tool before spending a budget finding out.
Most performance problems are data-model problems in disguise. How to design for the questions that will actually be asked.
Retrieval is easy to demo and hard to trust. What it takes to make a grounded assistant hold up under real questions from real users.
Notes from building workforce management for hospitality, where the edge cases are the product and the constraints change hourly.
Strangler patterns, seams and shipping the whole way through, migration as a series of safe steps rather than one brave leap.
What changes when a reporting pipeline is versioned, tested and monitored like software rather than assembled by hand each month.
Bringing WhatsApp, Messenger, Instagram and live chat into one thread model, and what that meant for search and team handover.
Public benchmarks tell you about public benchmarks. Building an evaluation set from real cases is what tells you whether to ship.
Automating one step of a broken process makes it faster at being broken. How to map a workflow before deciding what to automate.
Structure, permissions, freshness and documentation: the prerequisites that decide whether an AI project ships or stalls.
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