Ikigai Research & Innovation

Pioneering AI Research for Enterprise Intelligence

Founded on nearly two decades of groundbreaking MIT research, Ikigai Labs transforms enterprise decision-making through patented Large Graphical Models and cutting-edge AI innovation.

Latest Research

Resource-Efficient Time Series Foundation Model

Our breakthrough research demonstrates how compact foundation models can achieve state-of-the-art performance on time series forecasting tasks while requiring significantly fewer computational resources than traditional large language models.

Core Technologies

Our Foundational Innovations

Ikigai Labs' competitive advantage stems from decades of MIT research translated into commercial AI systems that solve real-world enterprise challenges.

Core Technology

Large Graphical Models (LGM)

Our patented Large Graphical Model technology represents a fundamental shift in how AI approaches enterprise data. Unlike LLMs trained on text, LGMs are purpose-built for structured, tabular, and time series data—combining probabilistic graphical models with deep learning to understand the complex relationships inherent in business operations.

  • Generate accurate forecasts with minimal training data
  • Capture temporal patterns and cross-sectional relationships simultaneously
  • Provide uncertainty quantification with confidence intervals
  • Operate efficiently with lower computational requirements than LLMs

Human-AI Collaboration

eXpert-in-the-Loop (XitL)

Traditional AI systems operate as black boxes, unable to incorporate human expertise. Our eXpert-in-the-Loop (XitL) framework creates a continuous feedback loop between AI models and domain experts, enabling planners, analysts, and managers to actively guide model learning without technical expertise.

  • Validate predictions and provide corrective feedback in real-time
  • Inject domain knowledge about business rules and constraints
  • Flag anomalies and exceptional circumstances for model learning
  • Maintain human oversight while scaling AI capabilities across operations

Transparency

Explainable AI

Understanding why an AI system makes predictions is critical for enterprise adoption. Our explainability features are fundamental to LGM technology - not afterthoughts. Rather than post-hoc interpretation methods, we provide native transparency throughout the entire AI pipeline with rich contextual information.

  • Driver Analysis: Identify which factors most influence each prediction
  • Confidence Intervals: Quantify uncertainty with statistically rigorous bounds
  • Temporal Decomposition: Break down forecasts into trend, seasonality, and events
  • Interactive Visualization: Explore relationships through intuitive dashboards

Research Impact

Why Our Research Matters

Our academic foundation translates into tangible competitive advantages for enterprise AI applications.

Superior Accuracy

Achieve 20-40% better accuracy on enterprise forecasting compared to traditional statistical methods and generic LLMs.

Efficiency

Dramatically fewer parameters than LLMs, enabling faster inference, lower computational costs, and on-premise deployment.

Explainability

Built-in interpretability shows which patterns and features drive forecasts, enabling trust and governance.

Enterprise-Ready

Designed for production deployment with security, scalability, and integration with existing enterprise systems.

Proven Results

Validated across Fortune 500 companies with measurable improvements in forecast accuracy and business outcomes.

Academic Rigor

Built on peer-reviewed research and continuous innovation from MIT's leading AI and operations research labs.

Ready to Transform Your Enterprise?

Discover how Ikigai's AI research is powering the next generation of enterprise decision-making.

Featured Resources