ML Lab — Applied Machine Learning

01 What the ML Lab is

A division, not a side project

The ML Lab is Braingency's dedicated machine learning division. It exists because the models that create real business value are rarely general-purpose — they are trained on a specific company's data to solve a specific decision problem. The Lab builds those models, integrates them into production systems, and conducts our own research in machine learning and AI.

02 What we do

01

Models inside our own products

Our financial ERP platform includes a scoring module where we train machine learning models on real user data from the lender's own portfolio. The resulting model scores incoming credit applications directly within the origination workflow — the model is not a separate analytics product, it is part of how the loan gets approved.

Vertio, our real estate valuation platform, runs on a model built entirely in-house and trained on Latvian property market data. It produces price forecasts used for mortgage collateral assessment and credit scoring inputs.

02

Custom models for clients

We build machine learning models on commission for companies across different industries. The typical engagement starts with the client's existing operational data and a decision they want to make better or faster — risk assessment, demand forecasting, classification, pricing, anomaly detection. We handle data preparation, model development, validation and production integration.

Because our engineering team builds the systems the models run inside, the handover problem that kills most ML projects does not arise: there is no gap between the team that trains the model and the team that has to deploy it.

03

Research

The Lab conducts ongoing research in machine learning and AI, focused on the domains we work in — credit risk modelling, valuation, and applied AI in regulated environments.

The ML Lab operates within the same engineering organisation that builds our production systems. Machine learning capability is embedded across the founding team.

03 Case examples

Two models in production

Case 01

Credit scoring

A lending client's underwriting process relied on static rule-based criteria. We trained a scoring model on their historical application and repayment data and integrated it into the origination workflow, allowing applications to be assessed against patterns in their actual portfolio performance.

Case 02

Property valuation

Vertio: an ML model trained on Latvian real estate market data that forecasts property values, applied to mortgage collateral assessment and scoring.

Braingency ML Lab