You’ve always wished an AI platform could do this.
Ours does.  

We automate the pre-task of most data-driven analysis by “stitching” multiple data sources together.

aiMatch learns the dataset ontology and the relationships between rows and columns within datasets, enabling faster data reconciliation and better downstream processing. 

Our customers use aiMatch to do SKU mapping and new product launches in retail, auditing in insurance, compliance in the financial market, entity resolution across verticals, and more.

We enable best-in-class time series forecasts and provide hierarchically consistent model learning.

aiCast transforms time series data into tabular format, trains large graphical models (LGM), and identifies change points for better forecasting. 

Customers have used aiCast to identify factors influencing forecasts and quantify the relationship between multiple time series. 

We run scenario analysis for planning to make recommendations using reinforcement learning. 

aiPlan can evaluate the impact of possible decisions using historical data. 

Customers have used aiPlan to create and find the optimal policies for all types of optimization problems.

Large graphical models (LGM) provide computationally convenient, probabilistic representation of data for AI tasks. Ikigai’s LGM learn generative representation of any sparse tabular data efficiently, solving a longstanding roadblock in which only experts produced domain- and task-specific graphical models. 

Ikigai’s LGM:

• Learn generative representation of any sparse, tabular data efficiently.
• Enable identification of similarities between rows, columns, and cells of tabular data.
• Predict missing data.
• Identify anomalous observations.
• Generate new data that looks like original data (synthetic data or simulation).