What we build
Recommendation & personalization engines
Product, content, and next-best-action recommendations tuned to your catalog and behavior data — so the right thing surfaces at the right moment and average order value climbs.
Demand & revenue forecasting
Models that predict sales, demand, inventory, and cash flow weeks ahead — so you stock, staff, and plan against what's coming instead of what already happened.
Anomaly & fraud detection
Systems that flag the fraudulent transaction, the failing machine, and the outlier in your data before it costs you — catching what static rules and dashboards miss.
Churn & propensity models
Scores that tell you which customers are about to leave and which are ready to buy — so retention and sales spend goes where it actually moves the number.
Predictive analytics & scoring
Lead scoring, credit and risk models, pricing, and lifetime-value prediction — turning the data you already collect into decisions your team can act on today.
Computer vision & document intelligence
Models that read images, scans, and documents — classifying defects, extracting fields, and grading quality at a speed and consistency a manual review can't match.
Not sure machine learning is the right move?
A free 30-minute call with our co-founders. No pitch, no SDR — just an honest read on whether ML will pay back on your data, and which model to build first.
- 10+ years building production ML and AI systems
- Forecasting, recommendation, and anomaly-detection models shipped to production
- $20M+ in client revenue generated and costs saved
What machine learning does for you
Predicts what's about to happen
Forecasting turns your history into a view of the next quarter — demand, churn, cash flow — so you act early instead of reacting late.
Measured against your real KPIs
We tie every model to a number you already report — conversion, retention, margin, cost per case — and prove the lift on your data before anything ships.
Catches what rules and dashboards miss
Anomaly detection surfaces the fraud, the fault, and the outlier hiding in millions of rows — the patterns a person scrolling a report will never see.
Built on your data, not benchmarks
Models are trained and validated on your actual data and edge cases, so accuracy holds up in production — not just on a tidy academic dataset.
Gets sharper as your data grows
With retraining and drift monitoring in place, accuracy is maintained as behavior shifts, instead of quietly decaying the month after launch.
You own the models outright
Code, pipelines, trained models, and documentation are yours — no per-prediction API bill, no black box, no vendor lock-in.
What you get
- A production ML pipeline — data ingestion, training, serving, and monitoring — not a notebook that only runs on someone's laptop.
- Measured lift on the KPI we agree at kickoff, validated on your real data before a full build.
- Retraining and drift monitoring so accuracy holds as your data and customers change.
- Full ownership — code, pipelines, models, and documentation are yours. No per-seat or per-prediction subscription, no vendor lock-in.
- Numbers you can show the board — monitoring, a /status page, and the metric we set at kickoff moving in the right direction.
Industries we've shipped in
Let's scope your machine learning project
Book a 30-minute call. We'll look at your data and use case, and tell you honestly whether a machine learning model moves your numbers — and which one to build first.
Frequently asked questions
Machine learning development is building software that learns patterns from your data to make predictions or decisions — forecasting demand, recommending products, scoring risk, or flagging anomalies. Unlike hand-written rules, an ML model improves as it sees more data. Done properly it's a full pipeline: data preparation, model training and evaluation, deployment to production, and ongoing monitoring and retraining — not a one-off experiment in a notebook.
A machine learning development company takes a business problem — churn, forecasting, fraud, personalization — and ships a model that moves the KPI behind it. That means scoping the use case, preparing and labeling your data, training and validating models against a real benchmark, deploying them into your stack, and maintaining accuracy with monitoring and retraining. We break down where predictive ML pays back fastest in Predictive Machine Learning for Business.
It depends on scope and how clean your data is. A validated proof of concept on your own data typically runs $15k–35k over 4–8 weeks; a full production build is quoted per scope after that. Either way you see results on your data early, before committing to a full build. We break down where the money goes in our cost guide.
Machine learning consulting answers 'should we, and how?' — it audits your data, scopes feasible use cases, and hands you a roadmap and ROI case. Machine learning development actually builds and ships the model into production. We do both: most engagements start with a short consulting-style discovery to pick the highest-return use case, then move into development once there's a clear KPI to hit. You're never paying to build something we haven't first validated is worth building.
The ones that pay back fastest are demand and revenue forecasting, churn prediction, recommendation and personalization, fraud and anomaly detection, and lead or risk scoring. The common thread is a decision you make repeatedly and a KPI you already track. Pick the workflow with the clearest metric — inventory accuracy, retention rate, fraud loss, conversion — and start there, rather than trying to 'do ML' across the whole business at once.
Usually more than you think — and part of our job is telling you honestly if you don't. Most companies already sit on enough transactional, behavioral, or operational data for a first useful model; the work is cleaning and structuring it, not collecting more. In discovery we assess your data volume, quality, and labeling, and if the data isn't there yet we'll tell you what to capture first instead of billing you for a model that can't perform.
You see results on your own data within the first weeks. A validated proof of concept takes 4–8 weeks, and most production rollouts follow within a few two-week sprints, depending on data readiness and how many systems the model needs to plug into. We deploy with monitoring and a retraining plan from day one, so accuracy is maintained rather than left to decay after launch.