Why do you use pgvector in PostgreSQL instead of a separate vector database?

pgvector allows storing vector embeddings directly alongside your existing relational application tables in PostgreSQL. This eliminates managing multiple databases, supports standard ACID transactions, and avoids expensive per-month vector cloud fees.

How does the system handle document updates or deletions?

When you upload a new document or update an existing one, our pipeline automatically re-chunks and updates vector embeddings in real time, making new knowledge instantly searchable.

Can we restrict which employees can search certain documents?

Yes. Our vector search queries include role-based metadata filters (e.g. `department = 'Legal'`), ensuring employees only retrieve documents they are authorized to see.

Enterprise RAG & Private Vector Knowledge Bases

Specialized Focus

Specialized Overview & Architectural Focus

Generic AI models lack knowledge of your company's private operational guidelines, customer histories, and proprietary documentation. NVIT.SPACE architects private RAG pipelines that ground frontier LLMs in your internal data, delivering instant, cited, and hallucination-free answers to employees and customers.

We design high-performance semantic search pipelines utilizing vector embeddings stored in PostgreSQL with pgvector. Documents are intelligently chunked, embedded, and indexed for fast similarity retrieval.

Our RAG systems enforce strict semantic thresholding, verifiable source citations with direct page numbers, and role-based access governance, ensuring employees only retrieve information they are authorized to access.

Built for Knowledge-Intensive Enterprises & Support Teams:

Corporate legal, compliance, and risk teams searching across thousands of policy documents.

Healthcare networks and medical researchers querying clinical trial data and drug guidelines.

Enterprise customer support teams providing instant answers from technical user manuals.

Software engineering organizations indexing private codebases and API documentation.

Key Deliverables

Core Capabilities & Functional Deliverables

What we build and integrate within our Enterprise RAG & LLM Infrastructure engineering cycle:

Vector Embedding & Chunking Pipeline

Intelligent semantic document chunking preserving tables, headers, and code snippets.

pgvector PostgreSQL Storage

Fast cosine similarity search integrated directly inside your relational PostgreSQL database.

Verifiable Source Citations

Every AI response includes clickable source citations linking directly to the exact source document.

Role-Based Knowledge Isolation

Filter search queries by user role so staff only access documents permitted by their permissions.

Hybrid Search (Vector + Keyword)

Combines semantic dense vector search with sparse BM25 keyword matching for improved retrieval accuracy.

Problem & Resolution

Real-World Use Cases & Implementations

Practical operational problems resolved by our Enterprise RAG & LLM Infrastructure architecture:

Target: Corporate Legal Departments

Enterprise Legal & Regulatory Policy Search

Challenge: Compliance officers spending hours cross-referencing 2,500+ pages of regulatory circulars.

Solution: Private RAG portal with pgvector semantic search delivering instant cited answers with exact paragraph references.

Target: Enterprise Software Teams

Technical Engineering Documentation Q&A

Challenge: Developers struggling to find internal API specifications across scattered Confluence pages.

Solution: Interactive developer search assistant indexing internal repositories and API documentation with code examples.

Engineering Tooling

Technology Stack & Tooling

Verified frameworks and database technologies used for this discipline:

Vector Storage

pgvector (PostgreSQL)
HNSW Indexing
Qdrant / Pinecone

Embedding Models

text-embedding-3-large
Cohere Embed
Open-Source BGE

RAG Frameworks

LlamaIndex
LangChain
Python (FastAPI)
TypeScript

Retrieval Methods

Hybrid Search (BM25 + Vector)
Cohere Reranking
Context Compression

Delivery Methodology

Engineering Process & Project Lifecycle

Our structured delivery roadmap from requirements gathering to production release:

STEP 01

Data Audit & Knowledge Ingestion

Collect corporate PDFs, markdown files, databases, and define semantic search boundaries.

Deliverable: Knowledge Ingestion Architecture

STEP 02

Chunking & Embedding Pipeline

Implementing semantic document chunking, metadata extraction, and vector embedding generation.

Deliverable: Vector Embedding Pipeline

STEP 03

pgvector Index Optimization

Configuring PostgreSQL pgvector with HNSW indexes for fast similarity search.

Deliverable: Optimized Vector Database

STEP 04

RAG Prompt & Citation Grounding

Crafting grounding prompt instructions that enforce strict citation and reject unsupported questions.

Deliverable: Validated RAG Prompt Suite

STEP 05

Hybrid Search & Reranking Setup

Integrating Cohere reranking and BM25 keyword search to maximize retrieval accuracy.

Deliverable: Hybrid Retrieval Engine

STEP 06

Search UI & Role Access Deployment

Deploying intuitive search interface with document viewer and role-based permission filters.

Deliverable: Live Enterprise RAG Deployment

STEP 07

Telemetry & Vector Maintenance

Monitoring retrieval relevance, updating vector embeddings as new documents publish, and ongoing SLA.

Deliverable: Continuous Knowledge Governance

Related Disciplines

Frequently Asked Questions: Enterprise RAG & LLM Infrastructure

RAG retrieves relevant facts from your private vector database at the exact moment a question is asked and provides that text to the LLM to formulate an answer with citations. Unlike fine-tuning, RAG is instant to update, does not hallucinate, and provides verifiable source links.

Schedule an Architecture Consultation

Discuss your Enterprise RAG & LLM Infrastructure project requirements directly with our software engineering leadership.

Enterprise RAG Knowledge Bases & LLM Infrastructure