Machine learning consulting services
A model that's measured, not promised.
Forecasting, scoring, pricing and anomaly detection built on your own data, piloted against a real baseline before anyone commits to production — with classical, explainable methods used wherever they'll beat a fashionable one.
- ML pilot $4,900
- Measured in 2–4 weeks
- Yours to keep — code, weights, pipeline
Three honest stages, not one big number.
One model, your real data, measured against a baseline.
- One model, one use case
- Measured against a baseline you agree upfront
- 2–4 weeks
Typically to $45,000.
- Deployed and monitored
- Retraining pipeline included
- 8–14 weeks
Monitoring, drift checks and retraining once it's live.
- Performance and drift monitored
- Scheduled retraining
- Cancel with notice
Six predictions worth automating.
Demand & sales forecasting
Knowing what you'll sell next month, by product or by branch, instead of ordering by gut feel.
Lead scoring
Ranking new leads by how likely they are to close, so your best reps spend time on the right ones.
Churn prediction
Flagging the customers quietly about to leave, while there's still time to call them.
Pricing suggestions
A recommended price band from your margins, competitors and history — a suggestion, not an auto-pilot.
Anomaly detection
Catching the order, transaction or reading that doesn't look like the others, before it costs you.
Document classification
Sorting invoices, applications or support tickets into the right category automatically.
Most prediction jobs don't need an LLM.
Gradient boosting on your spreadsheet-shaped data — sales history, customer records, transaction logs — beats a large language model on most forecasting and scoring jobs. It's cheaper to run, faster to answer, and you can actually explain why it made a call. We recommend the boring, explainable model first, and only reach for something bigger when the problem genuinely needs it.
Need something that reads documents and drafts text instead of predicting a number? That’s Generative AI, not this page.
We check your data before you commit to a model.
The pilot starts by looking at what you actually have — how much history, how consistent it is, what's missing. If your data can't support a reliable model yet, we'll say so at pilot price, not after a production invoice.
- How much history you have, and whether it's enough
- How consistent the labels and records actually are
- What's missing, and whether it's worth collecting before we build
- A plain answer: build now, collect more data first, or a simpler rule beats a model here
A model works harder connected to everything else.
Get a free proposal for your ML pilot.
Send a few details and a senior engineer — not a salesperson — will come back within one business day with questions, a recommendation and a price.