Ikigai Tech Stack
Generative AI platform for forecasting, data prep, and scenario analysis on tabular and time-series data
Ikigai builds a generative AI platform grounded in MIT research that automates data prep, forecasting, and what-if analysis for operational teams. The tech stack (PyTorch, TensorFlow, Ray, PostgreSQL, Elasticsearch, DynamoDB, Apache Arrow, Dremio) reflects a data-heavy ML architecture optimized for both training and inference at scale. Core pain points—scalable data integration, ML performance optimization, and handling messy data—align directly with the product's three surfaces (aiMatch for prep, aiCast for prediction, aiPlan for scenario modeling), and the hiring mix is heavily weighted toward data roles, suggesting they're scaling data pipelines and model deployment rather than sales.
Tech Stack 36 technologies
What Ikigai Is Building
◆Challenges
- Troubleshooting complex issues
- Ensuring high client satisfaction
- Data-related challenges
- Optimizing ml performance
- Scalable data integration
- Cloud-native deployment challenges
- Cleaning messy data
- Data reconciliation
▲Active Projects
- Data transformation pipelines for aicast ingestion
- Reporting and dashboarding solutions
- Forecasting and analytics workflows
- Predictive modeling platform
- Ml optimization and deployment
- Cloud-native deployment