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Mahbub Rahman
Mahbub Rahman
Available for new projects

Enterprise RAG & Vector Database Integration

Make your AI answer accurately from your proprietary data.

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EXECUTIVE SUMMARY

Mahbub Rahman specializes in implementing Retrieval-Augmented Generation (RAG) systems and vector databases for startups that need LLMs to securely query proprietary business data.

The Technical Reality

Effective RAG is not about throwing documents into a vector database. It's about chunking strategies, metadata filtering, and hybrid search. I architect systems that use dense vectors for semantic meaning alongside sparse retrieval (BM25) for keyword exactness, ensuring the LLM is fed the highest-quality context possible.

THE RAG PROBLEM

The AI doesn't knowwhat it doesn't know.
THE HALLUCINATIONS

Your AI makes up answers.

Generic foundational models don't know your business context. You need a robust RAG pipeline to chunk, embed, and retrieve your specific documents before the LLM generates a response.

Zero hallucinations
THE SECURITY

You can't leak customer data.

You can't just paste enterprise data into a public chatbot. You need a secure pipeline where tenant data is strictly isolated within the vector database and access controls are respected.

Enterprise-grade isolation
THE SCALE

Keyword search isn't cutting it.

Traditional search fails on intent. You need semantic vector search (Pinecone, pgvector) to find exactly what users mean, not just what they type.

High-dimensional search

WHAT I BUILD WITH

Precision retrieval.No hand-offs required.

From database to deployment. I own the whole thing.

AI PIPELINE
Vercel AI SDK
OpenAI text-embedding-3
VECTOR DB
Pinecone
pgvector
Weaviate
PROCESSING
Unstructured.io
Custom Chunking
BACKEND
Next.js
Node.js
PostgreSQL

THE RAG PROCESS

Injecting knowledge.

We build a pipeline that turns raw PDFs into actionable intelligence.

01

Ingestion & Chunking

Data prep

We parse your PDFs, docs, and databases, chunking the text semantically (preserving headings and context) rather than blindly cutting at 500 characters.

02

Embedding & Storage

Vectorization

Text is passed through modern embedding models and stored in Pinecone or pgvector, tagged heavily with metadata (tenant ID, date, author) for secure, filtered retrieval.

03

Hybrid Retrieval

The query layer

When a user asks a question, we combine semantic vector search with keyword search, re-rank the top results, and inject only the most relevant text into the LLM prompt.

PROVEN RESULTS

TryOn Live
ShopifyTypeScript

TryOn Live

AI-powered virtual try-on Shopify app for clothing and accessories using GCP Vision API for real-time fitting.

Read Case Study→

COMMON QUESTIONS

Questions aboutalways ask me.

Ensuring accuracy and security in data retrieval.

Multi-tenancy is critical. I implement strict metadata filtering at the vector database level. Before a similarity search is executed, a hard filter is applied requiring the tenant_id to match the authenticated user's ID. It is cryptographically impossible for data to leak across tenants.

Usually, this is a chunking or retrieval problem, not an LLM problem. If the search returns the wrong paragraph, the LLM will give the wrong answer. I fix this by implementing hybrid search (Semantic + Keyword) and contextual chunking so the retrieval step is highly accurate.

If you already use PostgreSQL, we can simply enable the pgvector extension. It is highly performant for most startup workloads and simplifies your infrastructure by keeping relational data and vector data in the same place.

NEXT STEP

Resume, email,or a 30-min call.

Recruiters: download the PDF or email me. Founders: book 30 minutes. No pitch deck. Calendar is Pacific Time.

Download Resume
Contract / fractionalW-9 · full IPPT / CT / ET overlap