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I will build your ai saas with rag, langchain, openai, and vector database
Adefiyin Grace
About this gig
Your AI SaaS demos well and breaks in production. I build RAG systems, LLM integration, and custom vector database AI apps.
Without a RAG system, your AI hallucinates. Without a Vector Database, it can't search your data. Without proper LLM integration, it's just a wrapper. I build AI that is accurate, sovereign, and cited.
As an AI Developer and Full Stack Developer, I build production AI SaaS on OpenAI, Gemini, LangChain, LangGraph, and MCP.
What I build:
- Custom RAG system private knowledge base, internal AI search, corporate brain
- Vector Database pgvector, Pinecone, Weaviate custom LLM grounding on your data
- LLM integration OpenAI API, Gemini, LangChain, LangGraph, GPT agent, MCP pipelines
- Full stack AI SaaS React, Supabase, Stripe, airtable automation, Softr webapp
- Data Sovereignty AI self-hosted, zero third-party exposure, fully yours
Data stays in your infrastructure. Your AI cites its sources. Your users trust the output.
Message me with your feature list I'll map the exact stack and where most AI founders waste their first $10k.
Get to know Adefiyin Grace
Adefiyin Grace
Systems Architect and AI Engineer
- FromNigeria
- Member sinceJun 2026
- Avg. response time1 hour
Languages
English, Spanish, German, French
Stop paying the Admin Tax. I engineer Revenue Operating Systems, autonomous infrastructure that eliminates manual chaos and scales.
Four build categories:
→ n8n Expert | Zapier | WhatsApp chatbot | Lead qualification | CRM integration | AI agents
→ Lovable Expert | Bolt.new | Base44 | Replit AI | React JS | SaaS MVP rescue | vibe coding
→ RAG systems | Vector Database | LangChain | OpenAI | Custom LLM | MCP pipelines
→ Supabase Developer | SQL backend | RLS | Admin dashboard | Stripe integration
Production-grade. High-ticket. Message me with your bottleneck.
FAQ
What is a RAG system and do I actually need one for my AI SaaS?
RAG allows your AI to read and cite from your private documents; SOPs, CRM data, knowledge base; instead of hallucinating generic answers. If your AI SaaS needs accurate, cited outputs rather than guesses, you need a RAG system and Vector Database.
Which LLM should I use; OpenAI, Gemini, or open-source?
Depends on accuracy, cost, and data sovereignty needs. OpenAI API (GPT-4o) is default. Gemini for multimodal cases. Open-source models (Llama, Mistral) for full Data Sovereignty AI where your data must never leave your own infrastructure.
What is LangChain or LangGraph and when do I need them?
LangChain is the LLM integration framework connecting models to tools, memory, and data. LangGraph extends this with multi-agent graph orchestration, branching, looping AI pipelines that reason across multiple steps. I use LangGraph for complex GPT agent and agentic workflow builds.
Can you build on Airtable or Softr for a rapid AI MVP?
Yes. Airtable automation, Airtable Softr, Softr Airtable, Airtable CRM, and Airtable Interface builds are part of my rapid deployment toolkit. I can build a fully operational client-facing product with AI layers added on top within days.
