Generative AI forecasting and planning for tabular and time-series data
Ikigai sells an AI platform that turns spreadsheet-style and time-indexed business data into forecasts, reconciliations and scenario plans. Its stack pairs a Python ML core (PyTorch, TensorFlow, Ray, Apache Arrow, Dremio) with Rust and C++ for performance, deployed on AWS EKS via Helm and Terraform. Current hiring leans toward data roles focused on cleaning messy customer data and building ingestion pipelines for its aiCast forecasting module.
Ikigai is a San Francisco-based software company founded in 2019 with 51–200 employees. It offers an AI platform for structured and time-series data aimed at operational teams who need to make decisions under uncertainty. Core modules cover data preparation and matching (aiMatch), forecasting (aiCast) and scenario planning (aiPlan). Use cases include supply chain demand sensing, financial reconciliation and what-if analysis. The company hires in the United States and India.
Ikigai runs on AWS with Kubernetes (EKS), Helm and Terraform, using Python, PyTorch, TensorFlow, Ray, Rust, C++, PostgreSQL, DynamoDB, Dremio and Apache Arrow, plus React, TypeScript and Redux on the front end.
It applies AI to tabular and time-series data for data matching and reconciliation, forecasting and scenario planning, through modules named aiMatch, aiCast and aiPlan, targeting supply chain and finance use cases.
Ikigai is headquartered in San Francisco and hires in the United States and India, mainly for data and engineering roles, with some customer support positions.
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Ikigai's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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