Co-Founder
Synthefy
Synthefy develops multi-modal generative AI models specifically for time series data, enabling users to search, forecast, and synthesize data through simple text prompts. The platform enhances accuracy in time series analysis by incorporating rich contextual metadata, facilitating applications such as anomaly detection and capacity planning across various industries.
- Artificial Intelligence
- Data & Analytics
- Enterprise Software
- Software Only
Updated 7 months ago
Funding
Raised to date
$6.2MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 2 rounds
$150K · Seed
Announced Jan 2025
Disclosed cumulative: $6.2M
2/2
RoundDateAmountValuationInvestors
Founders
Product
Problem
Analyzing time series data for forecasting, anomaly detection, and scenario simulation is challenging due to the complexity of the data and the need to incorporate contextual information. Traditional methods often lack the ability to effectively synthesize new data or search for specific patterns within time series datasets.
Solution
Synthefy offers a multi-modal generative AI platform designed specifically for time series data, enabling users to search, forecast, and synthesize data using natural language prompts. By incorporating contextual metadata, the platform enhances the accuracy of time series analysis, providing actionable insights for various applications. Synthefy's platform allows users to generate synthetic time series data for stress-testing and data anonymization, search for patterns for visualization and analytics, and forecast future trends for anomaly detection and capacity planning. The platform leverages state-of-the-art generative AI to provide a comprehensive time series analysis solution.
Target Audience
Synthefy targets customers in industries such as energy, finance, networking, and e-commerce who need to extract insights from complex time series data.
Features
- Multi-modal time series data analysis incorporating contextual metadata
- Generative AI for synthesizing new time series data
- Natural language interface for searching and querying time series data
- Time Series RAG (Retrieval-Augmented Generation)
- Scenario simulation (digital twin) capabilities
- Forecasting and prediction tools
- Anomaly detection algorithms