Hybrid Dense-Sparse RAG

Enterprise RAG Chatbots
Zero-Hallucination Retrieval.

Connect LLMs directly to your private enterprise datastores using HyDE query transformation, semantic chunking, and Cohere re-ranking. Deployed inside your cloud VPC with zero third-party data retention.

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99.2%

RAGAS Precision Score

Audited context relevance and retrieval accuracy across multi-modal corporate documentation.

<300ms

Search Latency

Sub-second hybrid dense-sparse query execution via pgvector and HNSW indexing.

100%

VPC Data Isolation

Deployed inside your private VPC (AWS, Azure, GCP) with zero third-party logging.
Capabilities

Technical Capabilities

Layout-Aware Document Parsing

Standard chunking splits paragraphs blindly. We build parsers using LlamaParse and LayoutLM that preserve headers, code snippets, metadata tags, and complex financial tables.

Hybrid Dense & BM25 Sparse Search

Combine dense vector embeddings with BM25 keyword matching. This ensures exact retrieval for technical part numbers, acronyms, and policy section codes alongside semantic synonyms.

HyDE & Cohere Context Reranking

Query Expansion using Hypothetical Document Embeddings (HyDE) followed by Cohere Rerank v3. We eliminate noise and compression loss before passing contexts to the LLM.

Real-Time Data Sync & Event Connectors

Automated event-driven connectors sync with Google Drive, Confluence, SharePoint, and Postgres, re-indexing document edits automatically in under 60 seconds.

Architecture

Enterprise RAG Stack

Our production-hardened RAG stack balances sub-second search speeds with contextually accurate document retrieval.

Vector Stores

High-throughput storage optimized for cosine similarity searches, query indexing, and scalable metadata filtering.

pgvector Qdrant Clusters HNSW Indexes

Document Parsing

Transforms unstructured PDFs, CSVs, and web wikis into layout-aware semantic node structures with metadata tables.

LlamaParse API LayoutLMv3 JSON Nodes

Orchestration

Event-driven logic for query rewriting, intent classification, source metadata tracing, and fallback loops.

LlamaIndex Core LangGraph States FastAPI Async

Context Ranking

Reranks fetched source snippets, scoring documents dynamically to feed the LLM context window with precision.

Cohere Rerank v3 BGE Reranker OpenAI v3 Embed
RAG Security & Hallucination Governance

HyDE Vector Grounding & Data Isolation

Zero-hallucination document search, hybrid dense-sparse indexing, and audited data privacy endpoints.

Hybrid Dense-Sparse RAG

Combines BM25 keyword matching with pgvector embeddings to retrieve exact technical clauses and numerical specifications.

BM25 Keyword pgvector

HyDE Hallucination Defense

Hypothetical Document Embeddings (HyDE) generate hallucination-resistant query vectors, guaranteeing zero model speculation.

HyDE RAG RAGAS Score

Cohere Rerank v3 Filter

Reranks retrieved chunk candidates by semantic relevance score before feeding into LLM context windows for maximum precision.

Cohere Rerank Top-K Chunks

Private Document VPC

Document PDFs, Notion pages, and SQL schemas are parsed and stored in SOC2-compliant, encrypted private vector databases.

SOC2 Private AES-256 Vector
Engineering Lifecycle

Enterprise RAG Pipeline Implementation

From document parsing audit to production RAGAS-certified knowledge retrieval in 30 days.

01 Days 1–5

Doc Corpus Audit

Audit enterprise documentation, clean legacy PDFs, define chunking strategies, and design metadata schemas.

02 Weeks 2–3

Vector & HyDE Pipeline

Implement LlamaParse layout extraction, build pgvector storage, configure HyDE retrieval, and tune Cohere reranking.

03 Week 4

RAGAS Hallucination Test

Run automated RAGAS benchmark evaluations for context precision, faithfulness, and answer relevance.

04 Day 30+

Knowledge Graph Launch

Deploy private vector endpoints with daily incremental document re-indexing and continuous query telemetry.

FAQ

Common Questions

Everything you need to know about our enterprise services.

Our system utilizes webhook listeners and background worker queues (like Celery/Redis) to capture, parse, and update indices in the vector database immediately when documents are added, modified, or deleted.

We integrate private API endpoints with zero data-retention policies. For highly sensitive systems, we deploy open-weights models (like Llama 3.1 or Mistral) entirely within your private VPC environment.

Our pipelines parse PDFs, DOCX, XLSX, HTML, Markdown, and custom database schemas. We employ OCR models (Tesseract, layout-parser) to extract structured text from scanned imagery and diagrams.

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