Custom ML models, private LLMs, and RAG for business

Custom ML Models & Private LLMs

Custom ML models, private LLMs, and RAG for business

Product recommendations, sales forecasting, risk scoring, and RAG on your data - everything designed to generate real money: revenue, margin, risk, churn. Deployment in EU or on-prem with full GDPR compliance.
4-12 weeks deployment
Possible EU/on-prem deployment. Full GDPR compliance

Classic reports are no longer enough. Data grows, decisions are still by feel.

You have data in CRM, ERP, data warehouses, billing systems, and application logs, but key decisions are still made in Excel, after lengthy analyses, or by intuition. Ready-made, off-the-shelf scoring models don't account for your margin, seasonality, industry, and risk appetite. A custom ML model, private LLM, or RAG system has one goal: turn your data into concrete business decisions that can be measured in revenue, savings, or reduced risk - with full GDPR compliance and full control on your side.

Results

Results from companies in finance, e-commerce, and B2B

Real projects for sales, risk, marketing, and operations teams.

Fintech / Leasing

-18% fewer bad applications at the same volume.

ML scoring model filtered high-risk applications at pre-approval stage, shortening decision time and reducing collection costs.

E-commerce

+22% cart value thanks to recommendations.

Recommendation engine buy together and you may also like increased average cart value and CTR in remarketing campaigns.

B2B SaaS

-35% churn in key segments.

Customer churn prediction model based on application logs and billing, combined with automated playbooks for Customer Success.

Who It's For

Who benefits from custom ML models and private LLMs - ROI in 3-6 months

You'll see the biggest impact if:

  • You have at least tens of thousands of records (customers, transactions, products, documents, application logs).

  • Data is scattered across CRM, ERP, billing systems, data warehouses, or data lakes, and you want to build an ML model or private AI model on top of it.

  • You regularly make decisions like: approve / reject, recommend / don't recommend, contact / don't contact, flag / reject - ideal for scoring, recommendations, and churn prediction.

  • You need GDPR-compliant AI: DPA, logs, full control over where data is processed - we don't use your data to train models by default.

  • Excel and dashboards already exist, but there's no automatic decision - people look at reports and click approve/reject.

  • You know the process is perfect for a model, but there's no one to build, deploy, and maintain it (MLOps).

Features

What custom ML models and private LLMs can do

Six types of solutions that turn data into concrete decisions.

Recommendation systems

Product/offer recommendations in e-commerce and B2B, cross-sell/upsell based on purchase history, content and campaign personalization.

Prediction and forecasting

Demand, inventory, occupancy forecasts, revenue or team workload prediction, dynamic campaign and resource planning.

Scoring and classification (risk, lead, churn)

Risk scoring (credit, leasing, payments), sales lead scoring, churn prediction and identification of at-risk customers.

Document analysis and text classification

Automatic document categorization (contracts, invoices, applications), detection of gaps or errors in documentation, extraction of key fields (amounts, dates, contractors).

Private LLMs and RAG on your data (EU / on-prem)

Private LLM (e.g., Llama 3, Mistral) deployed in your cloud or on-prem. RAG (Retrieval-Augmented Generation) on documents, emails, and knowledge bases. Internal chatbots and AI agents for employees (customer service, sales, operations) working exclusively on your data. Integration capability via Model Context Protocol (MCP) - one model, many tools (CRM, ERP, ticketing, knowledge bases).

Anomaly and abuse detection (fraud detection, anomaly detection)

Detection of unusual transactions and user behaviors. Anomaly monitoring in system logs and operational data. Alerts for risk, compliance, and security teams - before the problem becomes a real loss.

Process

What custom ML model deployment looks like

  1. 1
    Week 0-1

    Data analysis and business case

    You show us data and processes you want to improve.

    We check data quality, select model type, and calculate ROI.

    Result:

    Concrete KPIs, data to use, ROI estimate, and project scope.

  2. 2
    Week 2-4

    Experiments and first model (PoC)

    We build first models on your data: baseline + ML/LLM models.

    We compare results with how decisions are made today.

    Result:

    First model version with metrics (e.g., AUC, precision/recall, uplift) and recommendation: scale/improve/reject.

  3. 3
    Week 4-8

    Production model and integration

    Fine-tuning, feature engineering, validation on historical data.

    We integrate the model with your systems (API, batch, events) and prepare monitoring.

    Result:

    Model ready for production + endpoint/API or batch pipeline, connected to CRM/ERP, applications, or data warehouse.

  4. 4
    Week 8-12

    Launch, A/B testing, and handoff

    We launch the model in real process (e.g., part of traffic).

    We monitor metrics, improve, and hand over documentation and knowledge to your team.

    Result:

    Working ML/LLM model with documented impact on results (revenue, savings, risk).

Technology

Technology that works in the background - without friction and without lock-in

We select the stack for your case - with full control on your side.

Infrastructure and environments

Cloud (possible EU deployment): AWS, GCP, Azure - Frankfurt, Warsaw, Amsterdam regions.

On-prem / private cloud: Kubernetes / Docker in your data center.

Data is processed in compliance with GDPR - we don't use your data to train models. Infrastructure can run in EU or globally, depending on configuration.

ML engines, private LLMs, and RAG

Classic ML models: scikit-learn, XGBoost, LightGBM - scoring, classification, regression. Deep Learning: PyTorch, TensorFlow - sequences, time series, signals, text. Private LLMs: Llama 3, Mistral, and other open-source models deployed in your cloud or on-prem (without sending data to public APIs). Integrations with OpenAI/Anthropic/Google where data allows (e.g., less sensitive use cases). RAG architectures for semantic search, chatbots, and AI agents working on your documents.

Data and RAG

Data warehouses and lakes: BigQuery, Snowflake, Redshift, PostgreSQL, MS SQL, S3/GCS/Blob Storage.

Vector databases: pgvector, Weaviate, OpenSearch.

RAG for working with documents, emails, knowledge bases.

Integrations, orchestration, and Model Context Protocol (MCP)

Integrations via API, webhooks, queues (Kafka, Pub/Sub, SQS), and existing ESB. Workflow orchestration: Airflow, Prefect, Dagster, or client tools. Integration layer can be based on Model Context Protocol (MCP) - enabling AI agents and private LLMs to use multiple tools (CRM, ERP, databases, file systems) in one, consistent context. This is the foundation for modern agent-based AI systems 2025: models not only predict, but also execute actions in your systems.

MLOps and monitoring

CI/CD for models (automatic deployments).

Data quality and drift monitoring.

Logging predictions and decisions for audit purposes.

Security and GDPR

GDPR-compliant data processing - we don't use your data to train models.

Data encryption at rest and in transit (TLS 1.3, AES-256).

Signed DPA, audit logs, backups.

Transparent Pricing

Custom ML Model Pricing - Fixed Setup + Monthly (excl. VAT)

Transparent hybrid model: one-time setup fee (architecture, model training, deployment) + fixed monthly fee (hosting, monitoring, retraining).

No surprises, no hidden fees. You know the full cost upfront.

ML ASSESSMENT

For companies that want to verify if ML makes sense before committing to full implementation.

  • Data quality audit (what you have, what's missing)
  • ML opportunity analysis (which processes to automate)
  • ROI estimation (potential savings/gains)
  • Architecture and scope recommendation
  • Written report + 1h consultation
EUR
€2,199 one-time
1 week
no minimum term
Get a quote
Most Popular
ML STARTER

Perfect if you want to test how ML works on your data in one specific case.

  • 1 business process (e.g., lead scoring, churn, data errors)
  • Data analysis + dataset preparation
  • ML model / simple deep learning model
  • Validation, metrics, business recommendations
  • API or batch export
  • Basic monitoring
  • DPA / GDPR
  • 30 days support
EUR
from €4,999
4-6 weeks
no minimum term
Get a quote
ML GROWTH

For companies that want to deploy a model that actually works in their systems.

  • Everything from STARTER
  • Full ML pipeline: feature engineering, tuning, retraining
  • Integrations with CRM / ERP / data warehouse
  • Advanced metrics + drift monitoring
  • Panel with results and alerts
  • Automatic model refresh
  • 45 days support + monitoring
EUR
from €9,599
6-10 weeks
minimum 3 months
Get a quote
ML ENTERPRISE

For companies that need a set of models or a private LLM for sensitive data.

  • Everything from GROWTH
  • Multiple models simultaneously (scoring, recommendations, segmentation…)
  • Private LLM in EU / on-prem (Mistral, Llama 3, without training on client data) - optional
  • Full MLOps: drift monitoring, retraining, versioning
  • Integrations with large systems (ERP, billing, legacy)
  • Security audit + advanced encryption
  • SLA + 60 days premium support
EUR
Let's talk
8-16 weeks
minimum 6 months
Get a quote

In all packages you receive:

  • Data analysis and business case
  • Solution and ML architecture design
  • Model training + validation + metrics
  • Deployment (API, batch, integrations)
  • Full technical and business documentation
  • DPA / GDPR
  • 30-60 days support (depending on package)
  • Backend infrastructure in EU (min. 3 months included)
  • Full documentation and team training

What's included in the monthly fee

The monthly fee covers everything needed for your ML model to operate:

• EU cloud hosting (AWS/GCP)
• Model monitoring and drift detection
• Periodic retraining (frequency per tier)
• Technical support
• Security updates and patches
• Backup and disaster recovery

Why is this cheaper than DataRobot or Big4?

  • We're a startup, not a consulting firm with 500 people on the bench. No 'project managers', 'engagement leads', or 'discovery workshops' - just 2 engineers building your solution.

  • DataRobot costs $50,000-$200,000/year for the platform alone. We build a custom solution that you own 100% - no annual licensing.

  • Big4 (Deloitte, McKinsey, Accenture) charge €1,500-€3,000/day. A similar project would cost €150,000-€500,000 and take 6-12 months.

  • We use open-source ML (scikit-learn, XGBoost, PyTorch) and cloud infrastructure (AWS, GCP) - no expensive proprietary tools.

Included in setup price

The setup fee covers the full implementation from analysis to production:

Data quality analysis and business case

Solution and ML architecture design

Model training + validation + metrics

Deployment (API, batch, integrations)

Full technical and business documentation

DPA / GDPR compliance

30-60 days support (depending on tier)

Full documentation and training

FAQ

FAQ - honest answers about ML, data, and ROI

Do I need "perfect" data to deploy an ML model?

No. We check data quality at the start and advise what needs improvement. Often we can build the first model on what you already have, while cleaning up data in parallel.

What data is needed for the project to make sense?

Most often: decision history (approval/rejection, purchase/no purchase), customer events, transactions, system logs, documents, or labels (e.g., categories). The more good history, the better the model.

Will my data be sent to external models (OpenAI/Anthropic/Google)?

We use provider APIs (OpenAI, Anthropic, Google) in zero-retention mode - your data is NEVER used for model training. Queries are processed and immediately deleted. For sensitive data, we offer private LLMs (Mistral, Llama) in EU or on-prem - then your data never leaves your infrastructure.

What if the model makes a "mistake"? Who is responsible for the decision?

We clearly establish whether the model makes decisions automatically or acts as a recommendation system for humans. We always log predictions and decision basis for audit purposes.

How do you measure ROI from an ML model?

At the start, we define what is a "win": higher revenue, lower risk, less manual work. Then we compare results with the "before model" period (e.g., A/B test, control group) and convert to euros.

Can I maintain the model independently after deployment?

Yes. We can hand over code, pipelines, and documentation to your team, or offer maintenance on our side. Code ownership and handover options are available.

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Check in 30 minutes if a custom ML model makes sense for your case

Book a 30-minute consultation.

We'll review your data, show examples of models from similar companies, and preliminarily calculate whether the project can pay for itself in 3-6 months.

Calculate how much you can gain with a custom ML model

We'll respond within 24 hours - concretely, without sales slides.