Production Document Intelligence for Teams

Turn company documents into an instant, verifiable AI knowledge base.

BetterBee indexes your organization's reports, contracts, spreadsheets, and slide decks into private vector stores. Query thousands of pages simultaneously and receive factual answers with exact page, sheet, and slide citations.

workspace —Enterprise_Procurement_2024
Q
What is our termination clause penalty and what notice period is required under Master_Service_Agreement.pdf?

According to Section 8.2 (Termination for Convenience) of the Master Services Agreement:

  • Notice Period: Either party may terminate without cause by providing at least 60 calendar days written notice.
  • Penalty Structure: Early termination within the initial 12-month commitment incurs an early exit fee equal to 50% of the remaining contracted monthly recurring revenue.
Grounded Citations:Master_Service_Agreement.pdf · Page 14 (Section 8.2)Rate_Card_Schedule_B.xlsx · Sheet: Fee Schedule
Platform Capabilities

What BetterBee Does for Your Organization

Eliminate hours of manual document review. BetterBee acts as a reliable, always-available intelligence layer on top of your files.

Natural Language Semantic Search

Find concepts, clauses, numbers, and technical requirements across thousands of pages even when you don't remember the exact keyword.

Grounded Q&A with Citations

Ask specific questions and get synthesized answers that cite the exact page number, spreadsheet row, or slide for full verification.

Multi-Department Workspaces

Create dedicated workspaces for Legal, Finance, HR, or client accounts with isolated vector collections and strict permission boundaries.

Under The Hood

How BetterBee Works Under the Hood

A transparent, production-grade retrieval-augmented generation (RAG) pipeline designed for low resource overhead and zero hallucinations.

01

1. Ingestion & Structural Parsing

Uploaded files are stored directly in private AWS S3. Background parsers extract formatted text while maintaining exact page numbers, slide indexes, and sheet coordinates.

02

2. Chunking & Local Embeddings

Documents are split into contextual chunks with overlap. Semantic embeddings are computed via sentence-transformers and indexed into local ChromaDB collections.

03

3. Vector Search & Reranking

When a query is submitted, ChromaDB performs cosine similarity search within the target workspace. Top matches are scored and filtered for optimal relevance.

04

4. LLM Synthesis & Streaming

Retrieved context and user prompts are passed to high-speed Groq inference engines (Llama 3.3). Responses stream back in milliseconds with exact citation metadata.

Real-World Applications

How Companies Use BetterBee

Legal & Compliance

Review NDAs, MSAs, and vendor agreements. Check indemnity clauses, liability caps, and renewal deadlines in seconds.

Finance & Operations

Query multi-sheet balance sheets, audit reports, and investor updates. Extract margin figures and cost breakdowns accurately.

Engineering & Product

Search architecture specifications, API guidelines, and security policies without sifting through outdated wikis.

HR & Employee Onboarding

Help new hires find company policies, benefits guides, and standard operating procedures instantly through conversational search.

Format Support

Supported Document Formats

Parsers extract clean text and metadata across standard file extensions.

.pdf
PDF Documents
.docx
Word Documents
.xlsx
Spreadsheets
.pptx
Presentations
.md
Markdown
.txt
Plain Text
Security & Compliance

Enterprise-Grade Privacy Controls

Zero Model Training

Your documents and vector collections remain strictly your property. No client data is ever used to train external LLMs.

Isolated Vector Collections

ChromaDB stores embeddings with dedicated collection prefixes per workspace to eliminate data bleeding between projects.

Private AWS S3 Storage

Direct-to-S3 presigned upload URLs keep file transfers encrypted in transit and at rest with AWS SSE.

Start Searching Your Documents Today

Create your first workspace, upload company documentation, and start receiving grounded answers in minutes.

Open to Work · Mandsaur, MP, India

I'm Yuvraj.

Full-Stack Developer leveraging Java, Next.js, FastAPI, and AI/ML to build scalable applications.

I'm a computer science engineering student (AI specialization) and developer based in India. I focus on building production-grade full-stack applications, intelligent multimodal RAG systems, and performant backend services with Java, Python, and TypeScript.

Curriculum Vitae Preview
Full Catalog

Yuvraj Singh Rathore

+91 6232394854·uv3704@gmail.com·Mandsaur, MP, India
Infosys Springboard · AI/ML InternOct 2024 – Dec 2024
  • Built CNN model using TensorFlow achieving 97.5% accuracy on 10-class image classification.
  • Reduced model size by 35% using quantization and pruning techniques for efficient deployment.
ThrivesUp Consultancy Services · Java Backend InternJul 2025 – Sep 2025
  • Developed 12+ RESTful API endpoints using Java, Spring patterns, JDBC, and MySQL for academic records.
  • Optimized database queries reducing query response times from 500ms to under 90ms.
Mandsaur, MP, IndiaFull Career & Projects