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Fiverr Pro
English
$
USD
I will develop a custom rag chatbot and ai agent using langchain
Bavs
About this gig
Struggling with hallucinating bots & scattered data?
I build secure custom RAG chatbots that read your exact files to automate support instantly.
Features:
| Ingest PDF/CSV/Docx
| Web URL training
| SQL integration
| Vector search
| Zero hallucinations
| Hybrid search
| Session memory
| Custom UI embed
| WhatsApp/Slack setup
| API triggers
| Fallback routing
| Encryption
| Multi-language
| User auth
| Prompt engineering
| Admin dashboard
| Lead capture
| Email dispatch
| Sub-second speed
| Cloud hosting
7 Benefits:
| Cut costs 70%
| 24/7 accuracy
| Fast onboarding
| Data privacy
| High satisfaction
| Scalable traffic
| Auto-leads
5 Tools:
| Python
| LangChain
| n8n
| Pinecone
| OpenAI/DeepSeek
3 Reasons to Hire Me:
| Security-First: Zero public LLM training
| Production-Ready: Scalable architecture
| Support: Free maintenance included
Message me to discuss data architecture before ordering!
Get to know Bavs
Bavs
AI SaaS MVP Developer!
- FromUnited States
- Member sinceJun 2026
- Avg. response time1 hour
Languages
English
Are you struggling to turn your SaaS, AI, or startup idea into a working product? I help founders build scalable SaaS MVPs, AI web applications, and custom web apps using Lovable AI, Bubble, Supabase, Next.js, React, Stripe, and n8n. Whether you need a fast MVP, AI integration, payment setup, or a complete SaaS platform, I provide practical solutions tailored to your goals. Let's transform your idea into a product users can launch, test, and grow.
FAQ
❓ 1. Will my company data be safe and kept private?
Yes, absolutely. Your data security is the top priority. The chatbot is engineered using secure APIs (like OpenAI Enterprise, Anthropic, or self-hosted open-source models) that strictly guarantee your proprietary data is never used for public model training.
❓ 2. How do you prevent the chatbot from making things up (hallucinating)?
The system uses strict Retrieval-Augmented Generation (RAG) guardrails and advanced prompt engineering. The chatbot is explicitly programmed to only answer questions using the verified context found within your uploaded documents.
❓ 3. Who covers the ongoing API and database hosting costs?
The buyer is responsible for all ongoing running costs, including LLM API keys (OpenAI, Claude, DeepSeek) and vector database hosting (Pinecone, ChromaDB). However, part of the service includes optimization.
❓ 4. What types of files and data sources can the chatbot read?
The RAG pipeline is highly flexible and can ingest a massive variety of data. This includes local files (PDF, CSV, TXT, DOCX), live website URLs, Notion workspaces, Google Drive folders, and structured SQL databases.
