I will build a rag knowledge base ai chatbot for your documents and data


Sobre este Serviço
I build production-ready RAG (Retrieval-Augmented Generation) systems that let your AI answer questions from your documents, databases, and knowledge bases with high accuracy.
What I deliver:
- Document ingestion: PDF, Word, Notion, Confluence, web pages
- Vector search: Pinecone, Weaviate, pgvector, Chroma
- LLM integration: OpenAI GPT-4, Claude, Llama via LangChain
- REST API: query endpoint with source citations and confidence scores
- Admin panel: document upload, index management, usage analytics
Proven results:
- GovTech knowledge base: 94% accuracy on 10K document corpus
- Legal document search: sub-500ms retrieval on 50K+ page archive
- Internal wiki chatbot: 60% reduction in support tickets
Tech stack: Python, LangChain, OpenAI, Pinecone/pgvector, FastAPI, Go, PostgreSQL
Why choose me:
20+ years of backend engineering. Clean ingestion pipelines, reliable chunking strategy, and production deployment included. No hallucinations - every answer cites its source.
Conheça mais sobre Val Solomko
I will develop Go and Java backend, AI agent, or workflow automation
- A partir deUcrânia
- Membro desdemar. de 2025
Idiomas
Ucraniano, Inglês
Meu portfólio
Perguntas frequentes
What document formats do you support?
PDF, Word, Excel, plain text, Markdown, Notion export, Confluence pages, and web URLs. Custom parsers available on request.
How accurate are the answers?
Accuracy depends on document quality. With clean, structured content I typically achieve 90%+ relevance. Every answer includes source citations so you can verify.
Can I update the knowledge base after delivery?
Yes. I build an ingestion pipeline so you can add or remove documents without touching the code. Admin panel or API endpoint included per package.
