WhyHow.AI
Knowledge Graph Data Pipelines for Deterministic AI.
| Date | Investors | Amount | Round |
|---|---|---|---|
$1.6m | Seed | ||
| Total Funding | 000k | ||
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WhyHow.AI is a software company based in San Francisco that provides open-source tools to enhance Large Language Model (LLM) applications through the use of knowledge graphs. Founded in 2024 by Chia Jeng Yang, Chris Rec, and Thomas Smoker, the company aims to add determinism, accuracy, and memory to Retrieval-Augmented Generation (RAG) pipelines. WhyHow.AI was part of the Y Combinator Winter 2024 batch, which had a notable focus on AI-driven companies. The company has secured early-stage VC funding from investors including 186 Ventures.
The core of WhyHow.AI's offering is its Knowledge Graph Studio, a platform designed to simplify the creation, management, and querying of knowledge graphs from both structured and unstructured data. The technology is built to address common issues in LLM systems like hallucinations by providing a structured, contextual data layer. This allows developers to build more reliable and complex multi-hop information retrieval systems. The platform functions on an Extract-Contextualize-Load (ECL) principle, pioneering the use of Small Knowledge Graphs to give semantic structure to RAG pipelines. It operates with a modular architecture, allowing developers to integrate its components into their existing workflows. The system is database-agnostic, enabling users to export graphs to their preferred database solutions.
WhyHow.AI targets developers and data teams who are building or have incorporated knowledge graphs into their RAG systems. The business model includes an enterprise cloud-hosted solution alongside its open-source backend, providing services and support for enterprises to deploy the platform within their own environments. The company actively engages with the developer community through open-source repositories on GitHub, a Discord channel for discussions, and publications on platforms like Medium that share insights and case studies across finance, healthcare, and legal sectors.
Keywords: Knowledge Graphs, Retrieval-Augmented Generation, RAG, LLM applications, data pipelines, semantic structure, open source graph tooling, AI applications, LLM debugging, information retrieval systems, data structuring, graph visualization, developer tools, agentic workflows, multi-graph infrastructure, enterprise AI, Y Combinator, data contextualization, graph database, structured data extraction