Berry AI

Everything Starts With Knowledge and Annotations

We build the knowledge and annotation infrastructure on your data.
You build the future AI software.

What is knowledge in AI?

It is the meaning and semantic information derived from the raw data such that AI agents can understand, reason and act.

An example knowledge graph

In a customer support agent, raw audio becomes useful only after it's transformed into transcription, named entities, intent, sentiment & connectivity to structured customer tables. This enriched, interconnected data forms the knowledge layer

Why Vector DB is not sufficient

A vector DB helps find similar things, but AI agents need to know how things relate, what they mean, and why they matter. For that, you need a governable, queryable, semantic layer, which in turn can populate a vector DB.

More> https://berrydb.io/blog/why-vector-dbs-not-sufficient

Own Your Knowledge. Own Your Future.

As AI agents start writing code and make decisions, a governable knowledge layer is not optional.
Its the foundation for control, intellectual property and long-term competitive advantage

Why Berry AI ?

It is the first system purpose-built for building and managing knowledge graphs on structured and unstructured data. The missing layer between raw data lake and AI agents & vector DB

Rapidly build governable knowledge graphs

Building knowledge graphs using traditional DBs is hard. Developers need to integrate multiple backends including RDBMS for metadata, text search engine, an annotation system and a graph store. You cannot govern multiple backend systems easily. Berry AI consolidates it into a single system.

Built-in semantic studio and multi-layered knowledge graphs

Berry transforms raw documents, media and other data into richly annotated, multi-layered knowledge graphs, without manual data modeling

Natively manage PDF, text, images, audio, video, and JSON data types

Natively supports unstructured data types, including PDFs, images, audio, video, and JSON

Infinite flexibility to handle changing data

Berry can understand, integrate and index JSON data produced by AI workflows. JSON is the standard output format for AI models and it offers unparalleled flexibility to adapt to changes

PDF/Docs

Text

Image

Audio/Video

JSON

Extract semantic info and build multi-layered knowledge graphs

Embedded semantic studio with built-in AI models to process unstructured data

AI driven annotations

Named entity recognition, object recognition in image, text labeling, taxonomy, text summarization and 50+ semantic extraction models are built-in. Users can configure custom AI models for annotations

Built-in annotation studio for manual curation

Ability for human-in-the-loop changes to annotations and tracking lineage and ground truth

Supports in-place annotation on a multi-layered knowledge graph

Supports in-place enrichment of data by adding annotations as JSON layers

Unified search on multi-layered knowledge graphs

Integrates metadata search (SQL), full-text search, annotation search, and graph search in a single system. Users have the flexibility to combine these search queries as needed

SQL search

Search through field names using SQL and API

Full text search

Search through large text docs embedded. No need for Elastic search

Annotation search

Annotate data (manual or ML-based) and search through annotations. No need for a separate annotation system

Vector search or BYO vector db

Berry has a built-in vector DB that integrates with the knowledge layer. You have the option to bring your own vector DB

Multi-dimensional scaling and super fast performance

Horizontally scale knowledge graphs

Horizontally and separately scale knowledge data and knowledge index nodes

High performance query on knowledge graphs

Berry is designed to be used in application serving layer. It creates multiple indexes right on JSON knowledge graphs and is 5x or more faster on reads and writes compared to other DBs (MongoDB, MySQL etc)

BerryDB API

Powerful API to ingest, enrich and search through knowledge graphs: See https://docs.berrydb.io/python-sdk/

User friendly APIs for AI applications

Powerful APIs to process unstructured data, multi modal search APIs for queries, annotation APIs and others provides a comprehensive APIs to build AI applications

Notebook access and SDKs

Notebook UI for accessing/processing unstructured data and building knowledge layers using Python or Java SDKs

Knowledge System for AI Agents