Data & AI
Dashboards that answer the question that was asked, and AI that does real work inside the business.
Most data projects stall between the warehouse and the meeting where the decision is made. We start from the decisions, agree what each number means, then build the pipelines and dashboards that answer them reliably.
Where AI fits, we put it to work on specific tasks: assistants that answer from your own documents, and automations that take repetitive work off your team, each with guardrails and a person to hand over to.
What’s included
Analytics
Tracking and metric definitions agreed with the people who use them, so every report counts the same way.
Dashboards
Built around the decisions they inform, with alerts when a number moves.
Automation
Repetitive work taken off your team, from data entry to reports to routing requests.
AI assistants
Answers from your own content, tested before launch, with a handover to a person.
How it works
Questions first
We list the decisions the data has to support, and the metrics that answer them.
Data audit
We check what you collect, where it lives, and how far it can be trusted.
Build
Pipelines, dashboards and models built in stages, each checked against real data.
Handover
Your team is trained to run it, with documentation and monitoring in place.
Questions
Assistants answer only from the content you choose, run under your own accounts where possible, and are tested for what they shouldn't say before launch.
Not always. After the audit we'll tell you whether your current tools are enough, or whether a warehouse would pay for itself.
The one that fits the task, its cost and your data rules. We compare candidates on your own examples before choosing.
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Design
