Enterprise RAG & AI Engineering PunePune & Global Delivery
Custom RAG (Retrieval-Augmented Generation) & AI Solutions
Query millions of private enterprise documents, manuals, and databases with sub-second accuracy, verifiable source citations, and complete data privacy.
Enterprise RAG architecture with sub-second hybrid vector search
Qdrant, Pinecone, pgvector & ChromaDB vector database integration
Document ingestion, chunking, reranking & metadata filtering
Private LLM deployment (Llama 3, Mistral, DeepSeek) on secure cloud
Core Capabilities
What we build & deliver
Every project is backed by deep technical rigor, modern code standards, and agile sprint transparency.
01
Hybrid Search & Semantic Reranking
Combines BM25 keyword matching with dense vector embeddings and cross-encoder reranking for 99%+ accuracy.
02
Enterprise Document Ingestion Pipeline
Automated parsing of PDFs, Excel, Word, Notion, and SQL databases with intelligent chunking algorithms.
03
Verifiable Citation & Audit Trails
Every AI answer links directly to the exact source paragraph, page number, and document timestamp.
04
Private & Air-Gapped Cloud Deployments
Deploy on your AWS / Azure account or private VPC using open-source models (Llama 3, DeepSeek) for complete compliance.
Technology Stack
Engineered with best-in-class tools
We select battle-tested modern frameworks for maximum performance, security, and developer velocity.
LangChain
LlamaIndex
pgvector
Qdrant
Pinecone
Cohere Rerank
Hugging Face
AWS Bedrock
Why Vedrisha
Why Pune & global clients choose us
Direct senior engineer access, rapid iterations, and complete intellectual property ownership.
100% Data Confidentiality
Your proprietary data is never used to train public models. Strict VPC isolation and encryption at rest.
Eliminates LLM Hallucinations
Answers are strictly constrained to retrieved context with exact source citation links.
Instant Enterprise Knowledge Access
Empower employees and customers to find precise answers across millions of files in milliseconds.
Our Methodology
How we take your project from idea to production
Transparent milestone delivery with clear communication sprint over sprint.
01
Data Audit & Chunking Strategy
Analyze document formats, taxonomies, and design optimal token chunking and metadata tagging.
02
Vector Embedding & Indexing
Generate high-dimensional vector embeddings and build low-latency indexes in pgvector or Qdrant.
03
Retrieval & Reranking Tuning
Implement hybrid search, parent-child chunk retrieval, and cross-encoder rerankers to maximize precision.
04
UI & API Integration
Deliver web interfaces, conversational Copilots, and REST/GraphQL APIs for your internal apps.
Got Questions?
Frequently Asked Questions
Clear answers about our rag & generative ai solutions process, pricing, and timelines.
What is RAG and why is it better than fine-tuning an LLM?
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.
Can RAG handle complex PDFs, tables, and scanned documents?
Yes. We use advanced OCR and multimodal layout parsers (Unstructured, LlamaParse) to accurately preserve table structures, charts, and hierarchical headings.
Is our company data sent to external AI providers?
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.
Explore our other technology capabilities
Pune Engineering Delivery Hub
Ready to start your rag & generative ai solutions project?
Partner with Pune’s trusted engineering specialists. Get a scoped roadmap, tech stack review, and fixed-milestone estimate within 24 hours.