Leeroo offers the Uniforge platform, a unified context engine that automatically connects to enterprise data sources, resolves entity identities, and builds a consolidated knowledge graph enriched with relationships, processes, and external web data. By exposing this graph through a single API, it provides AI agents and applications with accurate, complete context, eliminating data fragmentation and reducing integration effort.
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
Enterprises often store customer and operational data in multiple siloed systems such as CRM, billing, ticketing, and data warehouses, resulting in fragmented and inconsistent records. AI applications that consume this disjointed data can produce incomplete or contradictory outputs, undermining trust and limiting automation.
Solution
Leeroo provides a unified context engine that automatically ingests data from heterogeneous enterprise sources, resolves entity identities, and enriches information with relationships and process knowledge. Its Uniforge platform builds a consolidated knowledge graph without requiring custom pipelines or schema mapping, delivering a single source of truth to any downstream AI agent. The system continuously extracts and normalizes data, applies autonomous machine‑learning models for entity resolution and relationship discovery, and adds web‑sourced enrichment to keep context current. By exposing this enriched graph through a unified API, Leeroo enables AI agents to generate accurate, consistent responses and automate workflows with minimal integration effort.
Target Audience
Leeroo targets data engineering and AI teams within large enterprises that need a reliable, consolidated data foundation for conversational AI, automation, and analytics initiatives.
Features
- Automated connectors for major SaaS systems (e.g., Salesforce, Oracle, ServiceNow) and on‑premise data stores
- Real‑time entity resolution that merges duplicate records across all sources into a single unified profile
- Process and rule extraction that captures organizational workflows from tickets, documentation, and code repositories
- Autonomous machine‑learning pipelines that generate schema mappings and relationship graphs without manual modeling
- Web enrichment layer that supplements internal data with external knowledge to improve context relevance
- Single API endpoint delivering a consolidated knowledge graph to any AI model or application