PANKAJ PRAMANIK.
LET’S TALK

03 / Services / GenAI / RAG

LLM & RAG SystemsTurn knowledge into answers.

LLM-powered and RAG-based applications that deliver accurate, context-aware answers through retrieval pipelines, vector search and prompt orchestration.

Discuss this service All services
Area
GenAI / RAG
Stages
8
Related projects
1

01 / The problem

What usuallygets in the way.

Build AI applications grounded in your documents, with retrieval you can inspect, citations people can check, and evaluation built in.
01

Knowledge is spread across PDFs, wikis and inboxes

02

Chatbot demos hallucinate on real questions

03

No way to tell whether answers are getting better or worse

02 / What I build

Capabilitiesin this service.

01

Prompt Engineering

Design structured prompts and response flows that improve output quality, consistency, and task performance.

02

AI Assistants & Chatbots

Develop conversational assistants for support, internal knowledge access, lead handling, and business interactions.

03

Vector Search & Embeddings

Use embeddings and vector databases to enable semantic search, similarity matching, and context-aware retrieval.

04

Agentic Workflows

Connect LLMs with tools, APIs, and business logic to build agents that can reason, act, and automate tasks.

  • Ingestion, chunking and retrieval tuned on your content
  • Answers with citations and clear fallbacks
  • An evaluation set and quality dashboard

03 / How it works

From documents to cited answers

A typical architecture for this kind of work. Scroll to follow the flow, or hover a stage for detail.

  1. Documents

    PDFs, pages and tickets, with access rules preserved.

  2. Parsing

    Text, tables and structure extracted cleanly.

  3. Chunking

    Sections sized for meaning, not a fixed character count.

  4. Embeddings

    Vectors that capture what each chunk is about.

  5. Vector database

    Fast similarity search with metadata filters.

  6. Retrieval

    Hybrid search and re-ranking pick the best context.

  7. LLM

    A prompt that answers only from retrieved context.

  8. Answer + citations

    Sources shown, so people can verify.

04 / In depth

The details.

LLM Applications & RAG Solutions

I build LLM-powered and RAG-based applications that deliver more accurate, context-aware, and business-ready AI experiences. My solutions combine large language models, retrieval pipelines, vector search, prompt orchestration, and structured workflows to help systems respond with relevant information instead of generic outputs.

I focus on building AI assistants, knowledge chatbots, document Q&A systems, and internal search tools that can understand context, retrieve the right information, and generate reliable responses. This includes prompt engineering, embeddings, vector databases, conversation memory, and dynamic input-output pipelines for real-world production use.

My Expertise in LLM & RAG Systems

I specialize in delivering custom LLM, RAG, and AI application solutions for clients across healthcare, legal tech, e-commerce, and other knowledge-driven industries.

  • 🧠 LLM Applications
  • 🔎 RAG Pipelines
  • ✍️ Prompt Engineering
  • 💬 AI Assistants & Chatbots
  • 🧬 Vector Search & Embeddings
  • 📚 Knowledge Retrieval Systems
  • 🔗 API Integrations & Webhooks
  • 🤖 Agentic Workflows

LLM Applications

Build intelligent applications powered by large language models for chat, summarization, classification, and content understanding.

RAG Pipelines

Create retrieval-augmented systems that fetch relevant information from documents and knowledge bases before generating responses.

My Expertise in Data & AI Industry

From raw data to AI-powered decisions - I help businesses implement end-to-end machine learning workflows, analytics dashboards, and automation pipelines.

Hire Me

Remote Data & AI Solutions

I’m a freelance Data Science and ML Engineer with 7+ years of experience transforming data into actionable insights. I specialize in building intelligent solutions, custom ML models, and scalable pipelines for startups and SMEs worldwide.

  • United States (Remote Projects)
  • Germany (AI Consultancy)
  • EUROPE (Remote Projects)
  • Remote - Available Worldwide

You Can find me here

As an independent Data & AI Engineer, I help companies unlock the full potential of their data. From predictive analytics to workflow automation, I offer strategic and technical support that drives real impact.

Book Free Consultation View Portfolio

05 / Tech stack

Tools I usefor this work.

  • LangChain
  • LlamaIndex
  • OpenAI
  • Claude
  • Pinecone
  • FAISS
  • FastAPI

More in GenAI / RAG

06 / Related work

Proof,not promises.

Open source
RAG

Medical RAG ChatbotLangChain + Pinecone + Flask

AI-Powered Medical Knowledge Assistant Retrieval-Augmented Generation (RAG) Chatbot

  • Python
  • LangChain
  • Pinecone
  • OpenAI
  • +2
Showcase

Bird Disease Classification (MLOps)

Built an end-to-end deep learning MLOps pipeline for bird disease classification using TensorFlow/Keras, PyTorch, OpenCV for image processing, FastAPI for model serving, and Docker for containerization. Implemented automated training pipelines, model versioning, and deployment workflows.

Showcase

U.S. Visa Approval Prediction (MLOps)

Developed a complete MLOps pipeline for visa approval prediction using Python, FastAPI, Docker, AWS (EC2, ECR, S3), XGBoost, and CatBoost. Implemented automated model training, versioning, deployment, and monitoring with CI/CD integration for production-grade machine learning.

Showcase

Uber Data Analytics Pipeline

Built a robust time-series forecasting solution with over 90% accuracy using Python and statistical modeling.It enabled a growing online retailer to better manage inventory, reduce overstock, and forecast seasonal demand shifts.

Showcase

CareSage - RAG Medical Chatbot

Developed an intelligent medical chatbot using Retrieval-Augmented Generation (RAG) with Flask, LangChain, Pinecone vector database, OpenAI embeddings, and sentence-transformers. The system processes medical PDFs using PyPDF to provide accurate, context-aware responses for healthcare queries with citation support.

Showcase

n8n Workflow Automation

Built 8 comprehensive automation workflows using n8n platform integrated with Retell AI, GoHighLevel, Twilio, OpenAI GPT-4, and PostgreSQL. Projects included AI Voice Agent for customer service, Multi-Channel Communication Hub, Lead Qualification System, CRM Sync Automation, Content Generation Pipeline, Customer Onboarding Workflow, Voice-Based Appointment Scheduler, and BI Reporting Dashboard.

GenAI / RAG

Need LLM & RAG Systems?Let’s scope it together.

Tell me about the goal, the data and the constraints. I’ll reply with questions and a practical first step.

Let’s chat