Why Berry AI ?

The Problem: AI agents run on knowledge, but there’s no system to manage it today. Berry AI is the knowledge system for AI agents

Every AI agent needs governable semantic information or knowledge about the data to understand, reason and take action, as opposed to just having a black box vector DB.

Knowledge is the representation of meaning extracted from raw data. For example, in a customer support agent, raw audio becomes useful only after it's transformed into transcription, entities (customer names, product names, etc), intent, sentiment, taxonomy, relationships & more. This enriched, interconnected data forms the knowledge layer.

However, building the knowledge system is complex. Developers are manually stitching together multiple backends: RDBMS; Elasticsearch; GraphDBs, Annotation Systems. It’s brittle. It’s bespoke. And it’s the most time-consuming layer in building AI agents.

Berry AI provides a knowledge infrastructure using AI. It is a governable, queryable, modifiable, automated multi-layer knowledge graph, the semantic brain that powers AI agents. It sits between the data lake and AI agents and vector DBs.

“Think of Berry AI as Postgres + Neo4j + Scale AI + Git (for version control), purpose-built for AI-driven Knowledge Management”

• Automates the organization of semantic knowledge from structured and unstructured data (text, audio, video, docs). Understands and integrates JSON snippets emitted by data pipelines without writing code.

• Enables governance of the multi-layered knowledge graph. 

• Offers a semantic studio to manage ground truth and supports human-in-the-loop changes

Whether it's a customer support agent, a legal assistant, or a healthcare agent, BerryAI becomes the intelligent memory, semantic brain and a truth layer powering it. It simplifies the complexity, significantly increases the time to market and accuracy in AI agents