Intelligible offers a platform that ingests enterprise data from files, databases, or warehouses and automatically creates persistent, auditable semantic components—facts, relationships, and definitions—that capture the data's meaning. These components provide a shared, machine‑readable foundation that grounds multiple AI models and applications, improving consistency, reducing token usage, and enabling reusable analyses across teams.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprise AI systems often produce inconsistent or inaccurate results because they ingest raw, unstructured data that lacks a shared, machine‑readable representation. Teams must repeatedly extract, define, and align data concepts, leading to duplicated effort and fragmented analyses.
Solution
Intelligible provides a platform that ingests data from files, databases, or data warehouses and automatically generates semantic components—structured facts, relationships, and definitions—that capture the meaning of the data. These components are persisted, auditable, and reusable, allowing multiple AI models and applications to reason over a consistent, compact representation instead of raw tables. By translating components into natural‑language statements, the platform creates a lightweight context that can be passed to standard LLM APIs, reducing token usage and improving answer consistency. Users interact through a visual interface that lets them query the data, view explanations, and compose new workflows from existing components, turning one‑off analyses into reusable infrastructure.
Target Audience
Primary customers are enterprise data teams, analytics engineers, and AI product developers who need a reliable, shared data foundation for building and scaling LLM‑driven applications.
Features
- Automatic extraction of structured facts and relationships from CSV, XLSX, and database sources
- Generation of reusable semantic components (e.g., segment definitions, thresholds, model effects) that preserve data relationships
- AI reasoning layer that grounds LLM outputs in components, delivering more consistent answers and clearer explanations
- Compact natural‑language context creation for efficient use with standard LLM APIs, lowering token consumption
- Persistent component store that can be shared across queries, applications, and AI systems for cross‑team alignment
- Visual interface (Intelligible Summand) for data connection, component inspection, and interactive chat‑based querying