Enterprise Retrieval-Augmented Generation

Enterprise RAG,
Built Different.

DataMind delivers precision answers from your document corpus through hybrid BM25 + semantic retrieval and Cohere cross-encoder reranking — with streaming synthesis powered by Llama 3.3 70B on Groq.

BM25 + VectorHybrid Retrieval
CohereCross-encoder Reranking
SSEReal-time Streaming
ChromaDBVector Store
Capabilities

Every layer of the RAG stack,
optimized.

01

Hybrid Retrieval Engine

Weighted ensemble of BM25 keyword search (0.4) and semantic vector retrieval (0.6) via ChromaDB Cloud. Never misses an exact match or a conceptual near-miss.

02

Cohere Cross-Encoder Reranking

After retrieval, Cohere's cross-encoder reranks top candidates. The model sees the full query-document pair — precision that similarity search alone cannot achieve.

03

Streaming Synthesis

Groq-powered Llama 3.3 70B delivers answers token-by-token via Server-Sent Events. No waiting — results stream as the model thinks.

04

Document Intelligence

Ingest PDFs, CSVs, and plain text. Auto-chunked at 1,000 tokens with 200-token overlap using Google Gemini embeddings. Every chunk is attributed so answers come with sources.

How it works

From document to answer
in three steps.

1

Upload

Drop in your documents — PDFs, CSVs, or text. The pipeline chunks, embeds, and indexes them automatically into ChromaDB Cloud.

2

Query

Ask in natural language. Hybrid retrieval runs BM25 and vector search in parallel, then Cohere ranks the top passages for relevance.

3

Answer

The LLM synthesizes a precise, cited answer in real time. Every claim is traceable to a specific document chunk with rerank scores.

FAQ

Common questions

What is DataMind?

DataMind is an enterprise Retrieval-Augmented Generation (RAG) system. Upload your documents, then ask questions in natural language — DataMind retrieves the most relevant passages and synthesizes precise, cited answers using Llama 3.3 70B on Groq.

What document types are supported?

PDF, CSV, and plain text files. The ingestion pipeline extracts text, chunks it at 1,000 tokens with 200-token overlap, embeds via Google Gemini, and indexes into ChromaDB Cloud — all automatically.

How does hybrid retrieval work?

DataMind runs two retrieval strategies in parallel: BM25 keyword search (weight 0.4) and semantic vector search (weight 0.6). The results are merged and reranked by Cohere's cross-encoder model, which evaluates each query-document pair directly for maximum precision.

Is my data secure?

Authentication uses JWT tokens. Each user's documents are isolated — you can only query documents you've uploaded. All API calls require a valid Bearer token.

How do I get started?

Create a free account, upload at least one document, then head to the Chat page. Type your question and hit send — the system retrieves, reranks, and streams an answer in seconds.

Get started

Ready to query your knowledge base?

Upload your documents and start asking questions in minutes. No infrastructure to manage — just intelligence built into your data.