Graphwise provides an end-to-end enterprise AI platform that transforms fragmented corporate data into a semantic knowledge graph, enabling hallucination-free generative AI through its GraphRAG engine. The low-code platform includes graph database, automation pipelines, and retrieval tools, allowing companies to deploy governed AI assistants in weeks. It serves over 200 blue-chip customers across regulated sectors like financial services, life sciences, and the public sector.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises struggle to scale generative AI because large language models lack corporate memory and frequently produce hallucinated answers that sound authoritative but have no grounding in verified business data. Fragmented data silos across structured and unstructured systems compound this problem, forcing organizations to stitch together multiple tools that fail to deliver trustworthy, explainable AI outputs.
Solution
Graphwise provides an end-to-end Enterprise AI framework that transforms raw, disconnected corporate data into a single, interconnected, traceable source of truth using semantic knowledge graph technology. The platform maps business logic into a structured corporate memory, then uses its GraphRAG engine to force AI models to retrieve answers exclusively from verified facts, significantly reducing hallucinations. Graphwise combines every essential component—graph database, automation pipeline, modeling tools, and retrieval engine—into one unified environment, eliminating the need to manage multiple vendors. The low-code platform with out-of-the-box templates enables teams to deploy secure, governed AI assistants in weeks rather than quarters, moving organizations directly to ROI.
Target Audience
Primary customers are large enterprises in regulated, data-intensive sectors such as financial services, life sciences, and the public sector, as well as organizations needing technical knowledge management, compliance intelligence, or semantic digital twin capabilities.
Features
- GraphRAG engine that retrieves context from structured knowledge graphs to deliver deterministic, explainable AI responses with multi-hop reasoning across different systems
- GraphDB, an enterprise-grade semantic graph database supporting native reasoning and explicit, governed, queryable relationships
- Graph Modeling component that uses LLMs to suggest concepts, synonyms, and business rules while keeping human experts in control of final models
- Graph Automation pipeline that coordinates repeatable data transformation across systems like SharePoint, SQL databases, and content repositories
- Semantic Analytics for entity identification, concept extraction, and automatic metadata tagging to enrich knowledge graphs
- AI-assisted modeling that automatically generates data pipelines, reducing manual integration work