braingency
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Business infrastructure for financial services

We build the ERP, CRM and machine learning systems that lending companies run on — from application intake to portfolio management.

Talk to us What we build

Braingency is a product-driven software development company based in Riga, Latvia. We design and build production systems for consumer lending, auto finance, leasing and mortgage businesses, and we develop custom machine learning models that turn operational data into better decisions.

  • 01 Riga · Latvia
  • 02 Spain
  • 03 United Arab Emirates
  • 04 25+ years in fintech
  • 05 In-house ML Lab
01 What we do

Three things, built to work together

01

Financial systems

End-to-end ERP and CRM platforms for lenders: application intake, underwriting and scoring, AML and KYC, contract generation, portfolio accounting and debt collection — in one system.

02

Machine learning

Our ML Lab builds models trained on your own operational data — credit scoring, risk assessment, valuation and forecasting — and integrates them directly into the systems that use them.

03

Platforms and products

Learning management platforms, AI assistants, market analytics tools. We take products from concept through to launch and scale, and we run them in production.

02 The lending cycle

One platform, not five tools

Rather than stitching together separate tools for origination, compliance and collections, the platform handles the entire loan lifecycle in a single, coherent system — which is also what makes the data usable for machine learning.

  1. 01

    Application intake

  2. 02

    Processing & scoring

  3. 03

    AML & compliance

  4. 04

    Contract generation

  5. 05

    Portfolio management

  6. 06

    Collections

fig. 01 — the lending cycle

ML layer

ML models trained on the full data stream

03 See it run

One application, end to end

An application moving through the platform: verified, screened, scored by the model and booked — in about the time it takes to read this paragraph.

An animated illustration, using sample data, of a loan application passing through intake, bureau and collateral checks, model scoring, KYC and AML screening, a decision, contract generation and portfolio booking.

fig. 02 — origination run
04 Why companies work with us

Four reasons this works

01

Built for regulated environments

Our financial systems are designed around compliance requirements from the start — AML screening, KYC/KYB integrations, audit trails and data integrity are part of the architecture, not an afterthought.

02

Full cycle, one team

We build from the initial idea through architecture, development, launch and scaling. You are not handing your product between agencies at each stage.

03

Machine learning that actually ships

We do not deliver notebooks and slides. Our models run inside live lending systems, scoring real applications every day.

04

Founders who write code

Both founders are hands-on engineers with a combined 25+ years of experience building fintech platforms in Ruby on Rails.

From the codebase

The model is one step of the workflow

Scoring is not a report someone reads later. It runs inside the transaction that decides the loan, is written to the audit trail, and is versioned like the rest of the code.

score_application.rb
Illustrative excerpt 
module Origination
  class ScoreApplication
    def call(application)
      features = Features::Builder.new(application).to_h
      model    = Scoring::Model.current

      application.transaction do
        score = model.predict(features)
        application.scores.create!(value: score, model: model.version)
        Audit.record(application, :scored, score:)

        if score >= policy.approve_above
          Decisions::Approve.call(application)
        else
          Decisions::Refer.call(application, queue: :underwriting)
        end
      end
    end
  end
end
ruby 3.3 · rails 7.2
fig. 03 — scoring inside origination
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