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Fiverr Pro
English
$
USD
I will build custom ai chatbots
Kairo
About this gig
Generic ChatGPT does not know your business. A RAG
chatbot does because it pulls from your documents,
policies, products, and data before answering.
This is real RAG engineering, not a no-code wrapper:
Document ingestion pipeline (PDFs, Word, web pages,
databases, Notion, Confluence, Google Drive)
Smart chunking and embedding strategy (most builders
get this wrong)
Vector database: Pinecone, Weaviate, pgvector, or
Chroma based on your stack
Hybrid search (semantic + keyword) for better recall
Re-ranking for precision right chunks, not just
similar chunks
Citations so users can verify every answer
Conversation memory
Production deployment with monitoring
Perfect for:
Customer support bots grounded in your help docs
Internal knowledge assistants for HR, IT, policy
Product Q&A for e-commerce
Technical documentation chatbots
My background: enterprise AI and solution architecture
across aviation, healthcare, and industrial sectors.
Message me first if your use case is in regulated
territory.
Send me a description of your use case and rough
document count.I will reply within a few hours with feasibility, approach, and scope.
Get to know Kairo
Kairo
AI, Solution Architecture and Full Stack engineering
- FromCanada
- Member sinceApr 2026
- Avg. response time1 hour
Languages
English, Hindi
Kairo is a solo-operated technical studio.
I build production systems — not demos. My background is enterprise architecture for Banks, Retail, healthcare, and industrial clients, which means I design for scale, security, and cost before writing a line of code.
What I do:
— Enterprise architecture and cloud migration/modernization
— AI development & deployment, agentic systems, LLM integration
— DevOps, AWS, Kubernetes
— Full-stack web (React, Next.js) and native iOS (Swift)
Message me with a paragraph about your project and I'll reply within a few hours with honest feedback.
FAQ
How is this different from a no-code chatbot builder like Chatbase or CustomGPT?
Those tools are good for a quick prototype, but they offer no control over chunking, retrieval strategy, or citations. Accuracy plateaus quickly on real use cases. What I build is a real RAG system where every layer — ingestion, chunking, embedding, retrieval, re-ranking, generation — is tuned for y
What file formats can you ingest?
PDFs, Word, plain text, markdown, HTML, web pages via crawling, Notion, Confluence, Google Drive, Dropbox, and custom APIs or databases. Tell me what you have and I will confirm during scoping.
How do you handle document updates?
The Basic package is a one-time ingest. Standard and Premium include incremental sync — new documents can be added by you, and the Premium package includes a scheduled update pipeline so the knowledge base stays current automatically.
Who pays for the LLM API costs and vector database hosting?
You do, using your own accounts (OpenAI, Anthropic, Pinecone, etc.). I will help you estimate monthly costs before we start. For smaller deployments, self-hosted options (pgvector, Chroma) eliminate vector DB costs entirely.
Can I hook this into Slack, Intercom, or my existing website?
Yes. The deliverable is an API endpoint, so it connects to any frontend — Slack, Intercom, Zendesk, your own website, or a custom UI. The Premium package includes UI branding if needed.
