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Fiverr Pro
English
$
USD
I will build rag pipelines and integrate llms into your system
Asad A
About this gig
We build production-grade RAG pipelines that connect your private data to large language models turning documents, knowledge bases, and internal data into intelligent, searchable systems.
This is not a ChatGPT wrapper. Every pipeline is architected from the ground up with proper chunking, embedding strategies, retrieval logic, and LLM orchestration designed for accuracy and scale.
Results from recent projects:
- Ignite Ventures uses a RAG-powered layer that learns from every investment decision made across 326 funded startups, continuously improving evaluation accuracy.
- MOHR Partners replaced manual document extraction with an automated pipeline delivering clean, structured data across their entire portfolio on demand.
What you get:
- RAG pipeline architecture using LangChain, LlamaIndex, Pinecone, and Weaviate
- Intelligent document search and AI knowledge bases
- Context-aware applications connected to your private data
- LLM fine-tuning on domain-specific data for precision performance
- Full API integration with your existing stack
Built for teams that need AI systems engineered to last, not demos that break in production.
Message me with your use case. I respond within a few hours.
Get to know Asad A
Asad A
AI Systems Engineer, Automation and RAG Pipelines
- FromUnited Kingdom
- Member sinceMar 2026
- Avg. response time6 hours
Languages
English
I design and build production-grade AI systems that replace manual operations with scalable, intelligent infrastructure.
My work includes AI automation workflows, RAG pipelines connecting private data to LLMs, AI agents for multi-step task execution, and full API orchestration across CRMs, ERPs, and third-party platforms.
Recent results: helped deploy $17M across 326 startups through an automated deal flow system, scaled a flight platform to $2M/day in bookings, and collapsed a weeks-long onboarding process into days unlocking a $30M investment offer.
FAQ
What is a RAG pipeline and how does it help my business?
RAG connects your private data documents, knowledge bases, internal records to an LLM like GPT or Claude. Instead of generic AI answers, you get accurate responses grounded in your own data. Great for document search and internal knowledge systems.
Which LLMs and vector databases do you work with?
OpenAI GPT-4, Anthropic Claude, LLaMA, and Mistral. For vector storage I use Pinecone and Weaviate. Pipelines built with LangChain, LlamaIndex, and LangGraph. I recommend the best stack based on your data volume and accuracy needs.
Can you build a RAG system using my private documents?
Yes, that is my specialty. I build pipelines that ingest PDFs, docs, spreadsheets, and databases then chunk, embed, and index them for intelligent retrieval. Your data stays private and is never used to train external models.
How is this different from just using ChatGPT?
ChatGPT has no access to your data and gives generic answers. A RAG pipeline connects an LLM to your actual documents and knowledge base so every response is accurate and specific to your business, not surface-level.
Do you provide support after delivery?
Premium includes 30 days post-delivery support. Every project comes with full documentation and a handoff guide so your team can maintain the system independently. Extended support available on request.
