You will get a RAG System, LLM Integration & Vector Database
Project details
Your company's knowledge is buried in PDFs, wikis, and drives. I build AI search engines that surface it in seconds — with answers, not just links.
I've shipped exactly this in production: an end-to-end resume parsing platform using LLM-powered NLP and Pinecone semantic search, matching thousands of candidates by meaning, not keywords. I'll bring the same architecture to your data.
What I deliver:
• RAG pipelines: your documents chunked, embedded, and indexed in a vector database (Pinecone, pgvector, Qdrant)
• "ChatGPT on your data" — accurate answers with source citations, no hallucinated facts
• Semantic search that understands intent: synonyms, context, and meaning
• Auto-sync from your sources: Google Drive, Notion, Confluence, websites, databases
• Production engineering: REST APIs, hybrid search + re-ranking, Docker, CI/CD
Why me:
• I've built and scaled real vector search systems — not tutorial projects
• Full ownership: source code, docs, and deployment access, no lock-in
• Measurable quality: I test retrieval accuracy, not just "it seems to work"
Send me a sample of your documents and your top 5 questions — I'll show you what's possible.
I've shipped exactly this in production: an end-to-end resume parsing platform using LLM-powered NLP and Pinecone semantic search, matching thousands of candidates by meaning, not keywords. I'll bring the same architecture to your data.
What I deliver:
• RAG pipelines: your documents chunked, embedded, and indexed in a vector database (Pinecone, pgvector, Qdrant)
• "ChatGPT on your data" — accurate answers with source citations, no hallucinated facts
• Semantic search that understands intent: synonyms, context, and meaning
• Auto-sync from your sources: Google Drive, Notion, Confluence, websites, databases
• Production engineering: REST APIs, hybrid search + re-ranking, Docker, CI/CD
Why me:
• I've built and scaled real vector search systems — not tutorial projects
• Full ownership: source code, docs, and deployment access, no lock-in
• Measurable quality: I test retrieval accuracy, not just "it seems to work"
Send me a sample of your documents and your top 5 questions — I'll show you what's possible.
AI Development Type
Deep Learning, Knowledge Representation, Recommendation System, Software MaintenanceAI Tools
MLflow, PyTorchAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$190
|
Standard
$490
|
Advanced
$990
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 18 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - |
Frequently asked questions
About Ayesha
AI Automation Engineer | Chatbots, RAG, Voice AI | Python and FastAPI
Lahore Cantt, Pakistan - 5:05 am local time
✔ AI voice agent platform on Retell AI, Vapi, Twilio, OpenAI Realtime API and ElevenLabs. 40 to 60% reduction in manual call workload through intelligent inbound and outbound conversations
✔ Multi-agent sales automation using LangGraph, CrewAI and OpenAI. 60% improvement in outreach efficiency through autonomous prospect qualification
✔ Enterprise RAG chatbot using LangChain, Pinecone and semantic search. Knowledge retrieval from hours to under 8 seconds
✔ AI automation pipeline using n8n, FastAPI and LLM decision logic. 20-hour manual workflow fully eliminated
✔ AI candidate screening system. 70% reduction in manual screening time, token costs cut 42%
🤖 AI CHATBOTS AND RAG SYSTEMS
GPT-4, Claude and Gemini chatbots backed by RAG pipelines that answer from your actual data. Document ingestion, Pinecone or ChromaDB vector search, LangChain orchestration, multi-turn memory and streaming responses. Deployed across web widgets, WhatsApp, Telegram, Slack and SMS for customer support, lead qualification and document intelligence.
🎙️ AI VOICE AGENTS
Real-time voice AI systems on Retell AI, Vapi, Twilio Voice API, OpenAI Realtime API, ElevenLabs and Deepgram. Lead qualification, appointment booking, inbound and outbound calling, CRM sync and live transcription. WebSocket and event-driven architecture for low-latency AI conversations. Tested against real edge cases before going live.
⚡ AI AUTOMATION AND WORKFLOWS
Intelligent automation using n8n, Make, Zapier, LangGraph and FastAPI. Multi-agent systems with LangGraph and CrewAI for autonomous task execution. Connected to GoHighLevel, HubSpot, Salesforce and any CRM via API or webhook. Lead qualification, document processing and business operations fully automated.
🛠️ TECH STACK
AI and RAG: GPT-4, Claude, Gemini, LangChain, LangGraph, CrewAI, Pinecone, ChromaDB, FAISS
Voice: Retell AI, Vapi, Twilio, OpenAI Realtime API, ElevenLabs, Deepgram
Automation: n8n, Make, Zapier, GoHighLevel
Backend: Python, FastAPI, Node.js, REST APIs, WebSockets, RabbitMQ
Cloud: AWS, Docker, CI/CD
🧠 HOW I WORK
Scoped before development. Built for production. Handed over documented.
✔ Business goals defined before architecture is designed
✔ Tested against real production scenarios, not scripted demos
✔ Clean code, complete documentation, structured handover
✔ Fast communication throughout, you always know where things stand
📩 BUILD WITH ME
Tell me what you are building, what it needs to connect to and the specific outcome you need. I will give you a direct answer within 24 hours.
Steps for completing your project
After purchasing the project, send requirements so Ayesha can start the project.
Delivery time starts when Ayesha receives requirements from you.
Ayesha works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & data audit
I review your documents and sources, define the questions your search must answer, and confirm architecture, scope, and timeline with you.
Pipeline build & indexing
I build the RAG pipeline — chunking, embeddings, vector index, retrieval, and LLM answers with citations — and share a working demo early.