You will get Production RAG MVP: Hybrid Retrieval Knowledge Base in 2 Week
Project details
Most 'RAG demos' you've seen are pure vector search with no reranking, no eval, no observability. They break on real production traffic. I build production RAG: hybrid retrieval, cross-encoder reranking, eval harness, observability, cost controls.
What you get:
• Hybrid retrieval (dense + sparse + RRF fusion + cross-encoder reranking)
• Document ingestion (PDF, DOCX, HTML, MD, CSV, web crawl)
• Vector DB setup (Qdrant default, or Pinecone, Weaviate, pgvector)
• FastAPI endpoint with auth, rate limits, observability
• Eval harness with 50+ test queries graded for retrieval quality
• Cost-control layer (token budgets, caching, model routing)
• Production deploy guide (Docker, AWS/GCP, your stack)
Why this matters: naive RAG scores 60-70% relevant in top-10. Hybrid plus reranking scores 85-95%. The difference between users finding what they need vs. churn.
Stack flexibility: Vector DB (Qdrant, Pinecone, Weaviate, pgvector), LLM (Claude, OpenAI, Gemini), Framework (LangChain, LangGraph, custom Python).
Past work: shipped 800M-profile vector search in production (sub-100ms latency, 95th-percentile recall at top-10). Same patterns applied to your use case.
What you get:
• Hybrid retrieval (dense + sparse + RRF fusion + cross-encoder reranking)
• Document ingestion (PDF, DOCX, HTML, MD, CSV, web crawl)
• Vector DB setup (Qdrant default, or Pinecone, Weaviate, pgvector)
• FastAPI endpoint with auth, rate limits, observability
• Eval harness with 50+ test queries graded for retrieval quality
• Cost-control layer (token budgets, caching, model routing)
• Production deploy guide (Docker, AWS/GCP, your stack)
Why this matters: naive RAG scores 60-70% relevant in top-10. Hybrid plus reranking scores 85-95%. The difference between users finding what they need vs. churn.
Stack flexibility: Vector DB (Qdrant, Pinecone, Weaviate, pgvector), LLM (Claude, OpenAI, Gemini), Framework (LangChain, LangGraph, custom Python).
Past work: shipped 800M-profile vector search in production (sub-100ms latency, 95th-percentile recall at top-10). Same patterns applied to your use case.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, PyTorch, StreamlitAI Models
BERT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$5,000
|
Advanced
$10,000
|
|---|---|---|---|
| Delivery Time | 14 days | 21 days | 30 days |
Number of Revisions | 2 | 3 | 4 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | |||
Model Documentation | - | - | |
Model Monitoring | - | ||
Model Testing & Optimization | - | ||
Model Tuning | - | ||
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | |
Prompt Engineering | |||
Setup File | |||
Source Code |
About Badal
Senior AI Engineer | AI Agents + RAG, Voice Agent, GEO/AEO
Bengaluru, India - 8:29 am local time
I led the technical build at two AI-first startups:
▸ An AI candidate sourcing platform running 800M-profile vector search in production (Qdrant + hybrid retrieval + cross-encoder reranking, sub-100ms latency), and
▸ A GEO/AEO audit platform monitoring AI visibility of any brand across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
My niche is the 20% custom AI work that off-the-shelf SaaS can't deliver. I focus on systems that actually ship to production with eval harnesses, observability, retry logic, and cost controls. Not POCs that rot in a sandbox.
Stack: Python, Claude Sonnet 4, OpenAI GPT-5, Qdrant, vLLM, n8n, FastAPI, LangChain, LangGraph, Portkey/Langfuse for observability.
What I deliver:
▸ Custom AI Agents. Multi-step Claude or OpenAI agents with proper orchestration, eval harnesses, retry logic, and observability. LangChain, LangGraph, or custom Python. Production-ready in 3-4 weeks.
▸ Production RAG Systems. Hybrid retrieval (dense + sparse + RRF fusion + cross-encoder reranking) with eval harness across your real corpus. Stack flexibility on vector DB (Qdrant, Pinecone, Weaviate, pgvector) and LLM (Claude, OpenAI, Gemini).
▸ Custom Support Chatbots. RAG over your docs AND ticket history. The systems Intercom Fin, Ada, and Zendesk AI can't deliver because they need deeper integration. Includes deflection-rate eval before launch.
▸ GEO / AEO Audit + Implementation. Get your brand cited when buyers ask ChatGPT, Claude, Perplexity, Gemini, or Google AI Overview about your category. 10-dimension audit, schema markup, llms.txt setup, citation strategy. Monthly retainer option for ongoing visibility tracking.
▸ n8n / Make Workflow Automation. The engineering-depth tier with custom Claude or OpenAI nodes, retry logic, observability, and Python sub-workflows. For projects that outgrew drag-and-drop.
▸ Cold Outreach + Lead-Gen Pipelines. Apollo + Instantly + Smartlead + Claude personalization. Account research agents that take a company URL and output decision-makers + intent signals + outreach angle. Built scraping infrastructure handling 5M+ records/day with anti-bot circumvention.
▸ Custom Sourcing Agents for Recruiting Firms. Multi-source candidate search beyond LinkedIn and Indeed, with custom scoring matched to your hiring criteria. For boutique recruiting firms with real engineering budget.
How I work:
1. 30-min discovery call to find the actual bottleneck. Most prospects describe the symptom, not the root cause.
2. Fixed-scope quote with one round of revision included. You know the price before we start.
3. Weekly Loom updates plus Slack or Notion for ongoing work.
4. Ship to production with eval harness, observability, and 30 days of post-launch bug-fix support.
Steps for completing your project
After purchasing the project, send requirements so Badal can start the project.
Delivery time starts when Badal receives requirements from you.
Badal works on your project following the steps below.
Revisions may occur after the delivery date.
Build, eval, and deploy
Discovery call, architecture doc for your approval, then hybrid retrieval RAG build with cross-encoder reranking, eval harness, observability, and production deploy with handoff documentation.