Validate: A demo on your data.
We fix the task and success metric, build a prototype, and show its real quality.
AI-Powered Decisions for Every Business
From raw data to a deployed model. No ML team required.
We help businesses run on data. The full cycle — from validating a hypothesis to a working model.
Deliver: A model in your processes.
We integrate the solution into your processes and infrastructure and take it to production.
Support: The result lasts.
We monitor quality, update the model, and support it after launch.
Impact across business functions
Select a business scenario to see how Modeller improves measurable KPIs, reduces delivery time, and turns data into financial impact.
Predictive LTV for Marketing Optimization
Enterprise e-commerce · 3M+ customers
+18% retention ROI — Marketing spend shifted to high-value customers.
Risk Scoring for Digital Lending
MFI · 5M+ customer base
−14% credit losses — Manual rules replaced by predictive risk ranking.
Lead Scoring for Sales Efficiency
B2B SaaS company · 50K+ leads per month
+23% sales conversion — Sales prioritized by conversion probability, not order of arrival.
Demand & Revenue Forecasting
Retail network · 200+ stores
−21% stockouts — Reliable planning signal across stores, categories, regions.
Contact Center Optimization
Bank / fintech company · 10M+ customers
+17% contact rate — Right customer, right time, fewer wasted calls.
Results first. Big project later.
Instead of a big project with an unpredictable outcome — a scoped demo on your data. We fix the task, success metric, timeline, and cost, build a prototype, and show its real quality. You decide on delivery based on numbers, not promises.
Validation risk is on us. The demo is a self-contained stage with a clear budget and outcome. No obligation to continue if the hypothesis doesn't hold.
How the demo ends
Hypothesis confirmed: We deliver
- Prototype with measured quality
- Estimated business impact
- Integration plan, timeline, and cost
- Support terms and SLA
Hypothesis not confirmed: We help find the path
- We show exactly what is missing for the result
- Which data to collect and how to label it
- What to change so the model can work
- When it makes sense to revisit the task
From task to a working model.
01. Understanding the task
We define the business goal, the data, and the success criterion.
02. Training a model on your data
We build a prototype and measure its real quality.
03. Making the call
If the economics work — we plan delivery. If not — we show why.
04. Delivering & supporting
We integrate, monitor quality, and support the model after launch.
Need ML?
No spec, no prep. Thirty minutes on your task and data — and you get an honest answer on where ML pays off, plus a concrete demo proposal with timeline and cost.