Faster, better-informed decisions from the data your operations already produce - applied AI, not hype.
Most operational businesses are sitting on years of data - collections, jobs, routes, orders - and using almost none of it. Meanwhile the AI pitch they hear is generic: a chatbot, a dashboard, a promise.
We start from the operational question instead. Which routes waste the most time? What will next month's demand look like? Which of these thousand cases actually needs a person's attention? Then we build the answer into the systems your team already uses - grounded in your real data, not a demo dataset.
Often the first job is plumbing, not intelligence: getting the data flowing cleanly through proper integrations so there's something solid to build on.
The applications with a measurable payback in operational businesses.
Getting more from the vehicles, crews and plant you already have - schedules and routes driven by your actual demand data rather than last year's habits.
Seeing next month before it arrives - forecasts built from your own history, so buying, staffing and capacity decisions rest on numbers instead of gut feel.
Letting software handle the thousand routine cases so your people handle the ten hard ones - document processing, classification and checks that run without a queue.
AI is only as good as the data underneath it. We do the unglamorous work first - pipelines, cleaning, one source of truth - which is why our AI work sits alongside our integration and platform work rather than apart from it. A recent build includes real-time capture from AI detection equipment on refuse trucks and a weekly automated export of over 1.1 million records.
Probably better than you think - and finding out is part of the work. We assess what you have, fix the gaps that matter and are honest about what the data can and can't support before anything is built.
No. You need one operational problem where a better decision is worth real money. Start there, prove the value, then let the strategy follow the evidence.
Less than the hype suggests when it's scoped to a real problem. Most of our AI and data work runs as part of a normal cloud platform - we design for running cost the same way we do for any cloud system.