Data & Analytics
Most of our data work sits inside a larger system: the pipeline behind an assistant, the telemetry behind a connected device, the scoring behind a coaching tool. We design the data model, the flow and the measure of success together.
What we build
01
Connectors that pull from documents, tickets and chat history on their own schedules.
02
Storage and aggregation for sensor readings, with a command channel back to the device.
03
Your own criteria turned into structured scores, each tied to its evidence.
04
Dashboards that show performance against the measure agreed at the start.
How we approach it
A target everyone can test beats a promise nobody can.
A score without the quote, source or reading behind it is an opinion.
Content is tagged with where it came from and when, so verified material outranks stale material.
Our work here
Manufacturing
We designed an enterprise knowledge assistant that answers technical support questions in Slack from scattered documentation and undocumented expertise, with citations and escalation instead of guesses.
5Layers of accuracy safeguards
SaaS
We designed an AI coaching engine that scores sales call transcripts against a company's own sales methodology and tells reps what to do next to win the deal.
7Coaching outputs from every call
Energy & IoT
We designed the backend, AI optimisation layer, web and mobile applications for an internet-connected thermal management system, and authored the technical case for a government innovation grant.
3Energy sources coordinated by one engine
Questions
A language model can give a reasoned estimate and show its reasoning, which helps with prioritisation. A trained predictive model needs a large volume of historical outcome data. We say plainly which one we are delivering.