Key Features
Ontology-Driven
Define your knowledge domain with simple YAML files. a1facts uses this ontology to automatically create the tools and structures needed to interact with your data.
Verifiable Ingestion
Convert unstructured knowledge into structured entities and relationships within your knowledge graph, ensuring data quality and consistency.
Natural Language Queries
Ask questions in plain English. a1facts translates your queries into precise graph traversals to retrieve the information you need.
Reliable AI Agents
Build AI agents that can provide accurate and trustworthy answers by grounding them in the factual data of the knowledge graph.
How a1facts Works
In many applications, it’s critical for AI agents to provide answers that are not just plausible but also precise and factually correct. Standard large language models (LLMs) can sometimes “hallucinate” or generate incorrect information, which can be a major issue in domains like finance, legal, or scientific research. a1facts addresses this problem by grounding your AI agent in a knowledge graph. Here’s how it works:1
Define Your Domain
You start by creating an ontology that describes the key concepts and relationships in your specific domain. For example, in finance, you might define entities like 
Company, Product, and Market, and relationships like competes_with or operates_in.2
Populate the Graph
You then ingest data from different sources into the knowledge graph. This could be from internal documents, databases, external APIs, or the web. a1facts ensures that this data is structured according to your ontology, but also that it’s factual by triangulating facts from different sources.
3
Query with Confidence
When your AI agent receives a question, it doesn’t just rely on its internal training data. Instead, it uses a1facts to query the knowledge graph. This means the agent’s answers are based on the structured, verified data.
a1facts is a work in progress. We’d love to hear from you on how you’re using it and what you need it to do next.