# Algolytics > Unlock the potential of data\. ## At a glance | Field | Value | | --- | --- | | Category | Data & Analytics | | Type | Solutions Provider | | Region | EMEA | | Headquarters | Warsaw, Poland | | Founded | 2018 | | Stage | Growth | | Employees | 11-50 | | Funding | Series A | ## Overview Algolytics is a data science and machine learning platform provider for enterprise clients in financial services, insurance, telecommunications, e-commerce, and logistics. Based in Warsaw, Poland, the company builds tools that enable organizations to automate AI/ML processes and deploy predictive models at scale. Primary insurance use cases include medical claims automation, fraud detection, credit risk scoring, and customer behavior analysis. The company offers a suite of analytical products: Scoring.One (low-code MLOps platform), AdvancedMiner (data science IDE), Automatic Business Modeler (AutoML), Event Engine (feature store for real-time streaming data), and Data Quality tooling. Products are deployed on-premise with API connectivity via REST and SOAP, and support integrations with Kafka, JDBC/ODBC connectors, and standard model formats such as PMML. Algolytics serves 50+ enterprise clients in Central and Eastern Europe. A published case study documents their work with Minte.ai, where they built an NLP module to extract medical information from document scans and an MLOps engine to integrate AI models into insurance company workflows, reducing claims processing costs and handling times. The company raised a Series A round in 2017 from Luma Ventures, Experior Venture Fund, and other investors. ## Products & services ### Scoring\.One Low-code MLOps platform for deploying, managing, and monitoring predictive models in production. Processes thousands of queries per second across multiple simultaneous models with sub-2ms response latency. Supports one-click deployment without container or DevOps expertise. **Key features:** - REST and SOAP API endpoints with Kafka message queue integration - Model versioning, A/B testing, and configuration promotion across DEV/TEST/UAT/PROD environments - JVM\-based reactive engine \(Vert\.x\) delivering higher throughput and faster response times than standard MLflow deployments *Target users:* Insurance and financial institution data science and IT operations teams ### AdvancedMiner Comprehensive data science platform integrating data processing, modeling, validation, and analysis in a unified graphical workspace. Supports classification, regression, segmentation, neural networks, time series, survival analysis, and graph models. **Key features:** - Algorithms include logistic regression, decision trees, random forests, and XGBoost - Multi\-language support: Python, SQL, Java, Groovy, R - Client\-server architecture with parallel computation and proprietary GDBase storage *Target users:* Data science teams in financial institutions developing and validating predictive models ### Automatic Business Modeler \(ABM\) AutoML platform for creating advanced ML models without programming expertise. Generates batch and real-time predictions, enabling non-technical business users to develop credit scoring, churn, and fraud models. **Key features:** - Automated model creation for credit scoring, churn prediction, and fraud detection - Supports both batch and real\-time scoring outputs - Designed for business users without data science background *Target users:* Insurance and financial business analysts and underwriters ### Event Engine Feature store for real-time streaming data processing. Aggregates and analyzes streaming data for real-time decisioning in fraud detection and customer scoring applications. **Key features:** - Real\-time event aggregation and feature computation - Kafka integration via Scoring\.One - Supports fraud detection and customer scoring workflows *Target users:* Data engineering teams requiring real\-time ML feature pipelines ### Data Quality Tools for cleaning, enriching, geocoding, and standardizing databases, with particular strength in Polish and international address validation. **Key features:** - 99% accuracy in geocoding and address standardization - Supports address validation and data enrichment pipelines - Reduces manual verification requirements significantly *Target users:* Insurance operations teams managing policyholder and claims address data ## Category & focus - Subcategories: Predictive Analytics, MLOps, AutoML, Fraud Detection, Claims Automation - Insurance verticals: P&C Commercial, Health, Specialty/E&S - Target customers: Carriers, TPAs ## Notable customers - Minte\.ai \-\- insurance medical claims automation \(NLP module and MLOps engine\) - NovaLend \-\- financial services predictive analytics - PGE \(Polish Energy Group\) \-\- creditworthiness assessment automation - DHL \-\- logistics analytics - Bonprix \-\- e\-commerce analytics ## Links - Website: - Directory profile: - Blog: - Linkedin: - Scoring\.one product: - Advancedminer product: - Case study: insurance claims: --- *Last updated: 2026\-06\-11*