How we unlock value from your time series data
We’ve put almost 2 decades of MIT research into unlocking the business value of your enterprise data with our patented Large Graphical Model (LGM) technology. It's time to elevate your decision-making with the most accurate, efficient, and cost-effective generative AI platform for data reconciliation, forecasting, and scenario planning.
See how we're different
A generative AI model that's purpose-built for enterprise data
Unlike common AI models, Ikigai’s patented Large Graphical Model (LGM) uses multidimensional graphs to represent the relationships between sets of random variables such as sales data, inventory levels, budgets, and customer trends. Designed for tabular and time series data, LGMs are highly effective at harmonizing multiple data sources and forecasting critical business trends like revenue, inventory levels, and supply chain performance.
- Learn generative representation of sparse tabular data
- Correlate data to create synthetic data
- Identify anomalies and outliers
- Generate forecasts on limited data
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Infusing AI with domain expertise and human intuition
Ikigai’s unique eXpert-in-the-loop capability seamlessly blends AI and human experience for the ‘X-factor,’ enabling domain experts such as supply and demand planners to train and improve the AI models with industry-specific knowledge and intuition.
- Easily identify anomalies and points of interest
- Review, accept, reject or adjust predictions with a few clicks
- Continuously improve machine learning runtimes
- Drive more accurate data reconciliation, forecasting, and planning
Explainable AI for decisions you can trust
For AI outputs to be trusted and adopted by business decision-makers, they need to be easily understood and explained. Ikigai incorporates explainability throughout its platform with transparent model metrics, interactive visualizations, and eXpert-in-the-loop feedback.
- Verify prediction accuracy with confidence intervals
- Evaluate temporal patterns, trends, and seasonality
- Understand model results from generated forecasts
- Overlay external data such as weather and promotions
- Correlate products with limited history to similar products
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Get to know the Ikigai platform
connectors
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connectors
We deliver measurable results
Frequently
asked questions
Ikigai provides flexibility in how solutions can be built on the platform to align with various levels of AI expertise. The low code/no code interface provides a simple drag-and-drop experience with pre-built connectors, facets, and data transformations. The API toolkit enables data scientists and developers to build robust solutions that can be standalone or integrated with existing business process applications and BI platforms.
The Ikigai platform is built on foundational Large Graphical Models (LGMs) which are computationally-efficient probabilistic models. Unlike LLMs which require extensive resources and training, Ikigai's models are cost-effective and resource-efficient, performing effectively without extensive pre-training, computational and network resources.
While other demand planning solutions are based on rigid models that only work for long-term historical data (2-3 years) and cannot adapt predictions to recent trends, Ikigai’s solution is built to address dynamic data and high variability. It works on as little as 2-3 weeks worth of data and offers confidence intervals, ensuring the highest accuracy in the market. In addition, it implements cutting-edge ML techniques, such as Reinforcement Learning, to incorporate learnings from your decisions into its predictions.
Many business users fail to adopt AI-driven forecasts and plans because they don’t understand or trust the outputs. Explainability helps bridge this gap by identifying key drivers influencing the predictions and explaining how outliers, anomalies, patterns, and trends are factored into the algorithms.
Our eXpert-in-the-loop (Xitl) governance model puts people firmly in the loop, ensuring that human expertise is at the heart of AI-powered decision making/support. Reinforcement learning empowers and underlines the partnership that generative AI can and must have with domain experts.
LLMs primarily work by analyzing text in a linear fashion with the main goal of creating new text or images. LGMs capture relationships across multiple dimensions which makes them particularly effective for multivariate time series forecasting and scenario planning.
eXpert-in-the-loop unlocks human insight through reinforcement learning. It allows your team to add expert insights to the forecasts produced by aiCast, infusing domain expertise and fine-tuning for more accurate predictions.
Explainability provides detailed insights into quantifying what is driving a prediction and why. It ensures transparency, helping users understand the rationale behind each forecast, thus building trust in the AI-generated predictions.
aiCast utilizes a proprietary time series prediction Large Graphical Model to forecast demand with industry-leading accuracy. Industry benchmarks are available upon request.