Caust Inc. develops quantitative analytics, software and decision-support tools.

Quantitative data analysis

Analysis of market data, volatility and structure using statistical methods — applicable also to other time-series and business data.

Algorithmic modelling and research

Rule- and model-based strategy research, signal generation, backtesting and risk assessment methods.

AI and predictive modelling

Time-series forecasting, neural networks, regime detection and model validation on historical data.

Software development and cooperation

Custom analytics tools, data workflows, proof-of-concept projects and technical partnerships.

Startups

Idea validation, proof-of-concept work, data analysis and feasibility assessment of technical implementation.

Growth companies

Tailored analytics tools, automation, modelling and support for business development.

Technology and partner companies

Business clients that need co-development, analytics or prototype building.

1 — Predictive time-series models

Objective: study the structure of time series, their predictability and the uncertainty of forecasts. Methods may include statistical modelling, linear approaches and neural-network-based research.

• Objective: analyse time-series structure and future observations.
• Method: statistical modelling, ML and neural networks.
• Evaluation: out-of-sample tests, error metrics and sensitivity checks.
• Applications: financial markets, demand forecasting and other time-series data.

2 — Signal analysis and optimal trade path

Objective: analyse entry, exit and turning points so strategy behaviour can be reviewed on historical data without exposing internal algorithms.

• Objective: signal generation and trade-timing analysis.
• Method: signal and decision models, rule logic and benchmark construction.
• Evaluation: historical comparison with cost and slippage assumptions.
• Applications: analytics systems, decision-support tools and prototypes.

3 — Market regime and volatility analysis

Objective: identify market conditions such as trend, variability and structure so methods can be evaluated depending on the environment.

• Objective: market-regime detection and risk timing.
• Method: statistical classification, variance models and AI-based segmentation.
• Evaluation: regime-by-regime comparison and stability review.
• Applications: risk analytics, analytics tools and data segmentation.

4 — Strategy evaluation and risk analytics

Objective: assess strategy performance systematically, including costs, and separate actual results from assumptions.

• Objective: backtesting, risk measurement and result validation.
• Method: return and risk metrics, walk-forward style evaluation and cost models.
• Evaluation: out-of-sample checks, robustness tests and documented limitations.
• Applications: client-specific analytics projects and commercial tools.

Proof of Concept

A focused development project to test feasibility, data quality and commercial potential.

Technology Partnership

A collaboration where each side brings its technical, commercial or domain strengths into the project.

Strategic Collaboration

Potential investment or equity discussions are evaluated separately once the goals, structure and terms are clear.