See what’s coming, from your own data
Not all useful AI is a chatbot. We build custom models that forecast demand, flag equipment problems before they turn into breakdowns, and score which leads, claims, or accounts deserve attention, with plain-language explanations your team can act on.
What we build
Demand and sales forecasting
Plan staffing, inventory, and purchasing around forecasts that learn from your history, seasons, and local events.
Predictive maintenance
Spot early signs of wear in equipment and vehicle data, and schedule repairs before a breakdown stops the work.
Risk and lead scoring
Rank applications, accounts, or leads by what matters to you, with reasons behind every score.
Yield and production forecasts
Blend field, weather, and production history to project output and plan inputs, storage, and crews earlier.
Anomaly detection
Catch unusual transactions, readings, or orders as they happen, with an explanation of what changed.
Predictions where you work
Results land in the spreadsheets, dashboards, or software your team already uses, with a plain-language summary.
How it works
- 01
Define the decision
We start from the decision a prediction should improve, like how much to order or which machine to service first.
- 02
Check the data
We review what you have, fix what we can, and tell you honestly whether it’s enough to predict well.
- 03
Build and validate
We train models on your history and test them on periods they haven’t seen, so you know how accurate they are before you rely on them.
- 04
Put it to work
Predictions flow into your tools, and we watch accuracy and retrain as your business changes.
A good fit if…
- You keep a few years of sales, operations, or equipment records
- Ordering too much or too little costs you real money
- Unplanned downtime throws off your schedule
- Key decisions rest on gut feel that’s hard to pass on
Questions we hear
How much data do we need?
Often less than people think. A couple of years of sales or operations history can be enough for a useful forecast. We check your data first and tell you what accuracy to expect before you commit to a build.
How is this different from generative AI?
Generative AI writes and summarizes; predictive models estimate and score from your numbers. They work well together: a model forecasts next month’s demand, and a generative assistant explains the forecast in plain language.
Can we trust the predictions?
Every model is tested on data it hasn’t seen, and we show you its track record in plain terms. Once it’s live we watch its accuracy and retrain it when it starts to drift.
Do we need a data team?
No. We build and maintain the data pipelines and models, and deliver the results in tools your team already uses.
More ways we help
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Let’s see if it’s a fit
Tell us what you’re working on, and we’ll give you a straight answer on whether this makes sense for your business and what it would take.