Browse categories
Explore
Fiverr Pro
English
$
USD
I will build ai ready data pipelines for rag, llm and ai agent applications
About this Gig
Your LLM is only as smart as the data it retrieves.
I build production-ready data pipelines that turn your documents, databases, and SaaS tools into AI-ready knowledge bases for RAG, LLM, and agent applications with the retrieval quality to match.
WHAT YOU GET:
- Multi-source ingestion (Notion, Confluence, Drive, S3, databases, websites, PDFs)
- Smart chunking tuned to your content (not just naive splits)
- Embeddings with your model of choice (OpenAI, Cohere, open-source)
- Vector DB setup (pgvector, Pinecone, Weaviate, Qdrant, Chroma)
- Metadata filtering + hybrid search for accurate retrieval
- Evaluation harness so you can actually measure quality
- Clean docs so your team can own and extend it
WHY ME:
- Google Certified Professional Data Engineer
- 20+ shipped data projects including RAG work (ShareDat)
- Deep data engineering background I treat RAG as a data problem first, LLM problem second
WHO THIS IS FOR:
Founders building AI products, teams adding RAG to internal tools, and agencies shipping AI features who are tired of "it works on 5 docs" prototypes that collapse at scale.
Message me before ordering so I can scope your sources, volume, and retrieval targets.
My Portfolio
Other Data Engineering Services I Offer
FAQ
Which vector databases do you work with?
pgvector (Postgres), Pinecone, Weaviate, Qdrant, Chroma, Milvus, and cloud-native options like BigQuery vector search and Snowflake Cortex. If you don't know which to pick, I'll recommend one based on your scale and budget.
What data sources can you ingest from?
Notion, Confluence, Google Drive, SharePoint, Slack, Intercom, Zendesk, websites, PDFs, databases (Postgres/MySQL/Mongo), S3/GCS, and any REST API. If you have a custom source, message me first.
Do you build the chatbot / frontend too?
My core gig is the data and retrieval pipeline — the foundation most RAG projects get wrong. I can add a simple chat prototype as a Gig Extra. For production UIs, I can recommend partners or handoff to your frontend team.
How is this different from just using LangChain / LlamaIndex?
Those are frameworks, not pipelines. Most failures in RAG aren't about the framework — they're about chunking, metadata, data quality, and retrieval tuning. I build the whole data engineering layer underneath so those frameworks actually perform well in production.
Will the pipeline keep the knowledge base in sync with source updates?
Yes. Standard and Premium include incremental sync so new and updated documents flow through automatically on a schedule. Basic is a one-time load, good for static corpora.
Can you help evaluate if the retrieval is actually working?
Yes, and this is the part most projects skip. Standard and Premium include a retrieval evaluation harness with test queries, hit-rate metrics, and a feedback loop so quality is measurable, not vibes-based.
