Vedrisha
TECHNOLOGYEngineering & Solutions
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Scoped proposal & architecture review in 24h
Engineering & Solutions
Have a project in mind?
Scoped proposal & architecture review in 24h
Query millions of private enterprise documents, manuals, and databases with sub-second accuracy, verifiable source citations, and complete data privacy.
Core Capabilities
Every project is backed by deep technical rigor, modern code standards, and agile sprint transparency.
Combines BM25 keyword matching with dense vector embeddings and cross-encoder reranking for 99%+ accuracy.
Automated parsing of PDFs, Excel, Word, Notion, and SQL databases with intelligent chunking algorithms.
Every AI answer links directly to the exact source paragraph, page number, and document timestamp.
Deploy on your AWS / Azure account or private VPC using open-source models (Llama 3, DeepSeek) for complete compliance.
Technology Stack
We select battle-tested modern frameworks for maximum performance, security, and developer velocity.
Why Vedrisha
Direct senior engineer access, rapid iterations, and complete intellectual property ownership.
Your proprietary data is never used to train public models. Strict VPC isolation and encryption at rest.
Answers are strictly constrained to retrieved context with exact source citation links.
Empower employees and customers to find precise answers across millions of files in milliseconds.
Our Methodology
Transparent milestone delivery with clear communication sprint over sprint.
Analyze document formats, taxonomies, and design optimal token chunking and metadata tagging.
Generate high-dimensional vector embeddings and build low-latency indexes in pgvector or Qdrant.
Implement hybrid search, parent-child chunk retrieval, and cross-encoder rerankers to maximize precision.
Deliver web interfaces, conversational Copilots, and REST/GraphQL APIs for your internal apps.
Got Questions?
Clear answers about our rag & generative ai solutions process, pricing, and timelines.
RAG dynamically retrieves real-time facts from your private knowledge base and feeds them into the prompt. Unlike fine-tuning, RAG is instant to update, does not hallucinate, provides exact document citations, and is significantly more cost-effective.
Yes. We use advanced OCR and multimodal layout parsers (Unstructured, LlamaParse) to accurately preserve table structures, charts, and hierarchical headings.
We offer 100% self-hosted deployments using open-weight models (like Llama 3 or Mistral) on your dedicated AWS/Azure cloud, guaranteeing zero third-party data transmission.
Partner with Pune’s trusted engineering specialists. Get a scoped roadmap, tech stack review, and fixed-milestone estimate within 24 hours.