Services

Back-Office Business Data Applications

I build back-office business data applications.

Custom systems for companies that need to manage business data, operational processes, and reporting in a controlled application.

Typical cases

• Reduction of errors, time, or cost in manual processing.
• Internal portals for structured data entry and reporting.
• Data-centric applications supporting the customer’s own business process.
• Better visibility over costs, data, responsibilities, and process status.
• Replacement of critical Excel-based or email-based processes.
• Applications built for compliance, traceability, and operational control.

Delivery model

• Cost-effective delivery based on reusable framework components and a defined implementation methodology.
• Outsourced and fully managed service.
• SaaS as the primary model.
• Optional deployment in customer infrastructure for mission-critical systems.
• Subscription model or IP transfer.
• Built with a proven AI-native technical framework and delivery methodology.
• Professional delivery covering support, continuity, security, and compliance.

Quantitative Data Systems

I build quantitative systems for small and medium buy-side firms.

Trading and data pipelines systems for financial-market research, model evaluation, portfolio analysis, and real operational deployment of trading models.

Typical cases

• Market-data processing.
• Quantitative research tooling.
• Backtesting and model evaluation.
• Portfolio analysis and monitoring.
• Data validation and research automation.
• Multi-asset, multi-strategy, and multi-horizon research workflows.
• Trading pipelines.

Scope

• Assets: equities, options, futures, and derivatives.
• Strategies: cross-sectional portfolios, intraday models, directional strategies, options based strategies.
• Horizons: tick data, intraday data, and daily data.

Platforms

• REDSTONE: market-data processing and research infrastructure.
• ALTONA: quantitative research and model evaluation platform.

Delivery model

• Cost-effective versus traditional deployments.
• Project-based development or managed service.
• Deployment in private infrastructure or managed colocated infrastructure.
• Built with proven components developed through real trading and research use.
• Professional delivery covering operational support, continuity, data quality, and reproducibility.