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.
What our platform sees in open data
Public research built with the same pipeline we run on client data.
Рынок труда России по регионам и профессиям
Аналитика 194 067 вакансий портала «Работа в России» за июль–сентябрь 2026: медианные зарплаты по 84 регионам, 8 округам и 200 профессиям, дефицит кадров и условия найма.
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.