Kater builds executive data assistants using structured decision trees to translate complex data into clear, actionable next steps. The platform enforces a shared semantic layer, ensuring consistent metric definitions across the organization without relying on data team intervention for common queries. This system guides stakeholders toward relevant business questions, enabling proactive decision-making directly from their data sources.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many organizations struggle to translate raw data into actionable insights due to a lack of unified data models and the complexity of querying data effectively. Stakeholders often don't know which questions to ask or how to interpret data to drive decision-making, leading to inefficient data exploration and underutilization of available information. Existing analytics processes often fail to track the impact of data-driven decisions, hindering continuous improvement.
Solution
Kater provides a data storytelling platform that automates the process of answering data questions, defining next steps, and generating personalized reports with actionable insights. It utilizes a unified semantic data model, built automatically as users interact with the system, to enable querying data using plain English. The platform's AI-powered AgentMesh orchestrates multiple AI agents to perform composite analysis, evaluate results, and provide accurate data interpretations. Kater bridges the gap between data and decision-making by allowing users to pre-define questions and next steps based on data outcomes, and then receive automated report summaries.
Target Audience
Kater is designed for data professionals and business users who need to easily access, understand, and act on data insights, as well as data teams looking to build a unified data model and empower business users with self-service analytics.
Features
- Data Playbooks: Pre-define questions and next steps based on data outcomes for automated report generation.
- Butler AI: Ask follow-up questions about personalized reports in plain English.
- Unified Semantic Data Model: Automatically builds a shared data model as users answer questions, ensuring reusability and governance.
- Confidence Ranks: Provides transparency by showing data sources, tests run, and a confidence ranking for each response.
- AgentMesh: Orchestrated group of AI agents that work together to create a composite analysis.
- Integrations: Connects with Snowflake, BigQuery, Databricks, Redshift, and MS-SQL.
- Security: SOC 2 Type 2 compliant, with encrypted data in-transit and at-rest, and secure credential storage.