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From Idea to AI-Powered CRM: How I Built a Semantic Search App with MindsDB + Ollama
Introduction
A few weeks ago, I set out on a challenge — to bring AI intelligence into everyday CRM systems.
The idea was simple:
“What if customer support agents could ask their CRM questions in plain English — and get meaningful, AI-generated answers instantly?”
That idea became my project: AI-Powered CRM Semantic Search, built using MindsDB and Ollama.
It wasn’t just another coding exercise — it was part of the MindsDB AI Use Case Contribution Track, and it completely changed how I see AI integrations, local LLMs, and real-world data workflows.
The Problem That Sparked It
Traditional CRM systems are powerful, but they make humans work hard.
Data is buried inside multiple tables — leads, tickets, customer notes, and chat logs.
Imagine being a support agent trying to find answers fast while a customer waits.
A keyword search like “refund” or “address change” gives random results with no real context.
That’s when I realized:
“CRM search needs to think like a human, not like a database.”
So, I decided to build semantic search — an AI layer that understands intent, not just keywords.
The Building Blocks
I wanted this to be practical — lightweight, local, and privacy-first.
Here’s what powered it:
How It Works
User → Streamlit App (ask query)
↓
MindsDB (semantic reasoning)
↓
Ollama (generate AI response)
↓
CRM Database (data retrieval)
When a user types a query like
“My refund has been delayed for 3 weeks, what should I do?”
MindsDB performs a semantic search on the CRM database, finds the relevant records, and passes them to Ollama.
The local model then generates a context-aware reply — all running completely offline.
What Makes It Special
Demo Snapshot
When I first saw the system respond intelligently to a customer query, it felt surreal.
A plain text query turned into a structured, empathetic AI reply.
Example:
User: “My refund has been delayed for 3 weeks. What should I do?”
AI Reply: “I apologize for the delay. Several refund cases, including yours, are pending. Please contact support to expedite your refund.”
It felt like watching a CRM come alive.
What This Project Taught Me
This project was more than a technical build — it was a personal transformation.
Every time I refined a prompt or optimized a query, I realized that building with AI isn’t just about code — it’s about designing systems that understand humans better.
Next Steps
The journey doesn’t stop here. I’m planning to:
Repository
GitHub:
Final Thoughts
When I started this project, I wanted to learn MindsDB.
But I ended up learning how AI, data, and community can spark real innovation.
This project ignited my passion for open-source contribution — it pushed me to focus, stay consistent, and build with purpose.
It also showed me that you don’t need massive infrastructure to make an impact — you just need curiosity, courage, and commitment to keep experimenting.
If you’re passionate about AI + data + open-source, explore MindsDB.
You’ll discover what I did — that it’s not just a platform, it’s a gateway to building meaningful AI systems that solve real problems.
About the Author
Hi! I’m Akash, a recent Computer Science graduate who’s deeply interested in AI, open-source, and data-driven applications.
Building this project was my starting point in exploring open source seriously — it helped me gain confidence, improve my skills, and connect with inspiring developer communities like MindsDB.
I’m currently focused on contributing to open-source projects, learning advanced AI workflows, and preparing for my first full-time opportunity in tech.
Let’s connect and collaborate!
GitHub:
IntroductionA few weeks ago, I set out on a challenge — to bring AI intelligence into everyday CRM systems.
The idea was simple:
“What if customer support agents could ask their CRM questions in plain English — and get meaningful, AI-generated answers instantly?”
That idea became my project: AI-Powered CRM Semantic Search, built using MindsDB and Ollama.
It wasn’t just another coding exercise — it was part of the MindsDB AI Use Case Contribution Track, and it completely changed how I see AI integrations, local LLMs, and real-world data workflows.
The Problem That Sparked ItTraditional CRM systems are powerful, but they make humans work hard.
Data is buried inside multiple tables — leads, tickets, customer notes, and chat logs.
Imagine being a support agent trying to find answers fast while a customer waits.
A keyword search like “refund” or “address change” gives random results with no real context.
That’s when I realized:
“CRM search needs to think like a human, not like a database.”
So, I decided to build semantic search — an AI layer that understands intent, not just keywords.
The Building BlocksI wanted this to be practical — lightweight, local, and privacy-first.
Here’s what powered it:
How It WorksUser → Streamlit App (ask query)
↓
MindsDB (semantic reasoning)
↓
Ollama (generate AI response)
↓
CRM Database (data retrieval)
When a user types a query like
“My refund has been delayed for 3 weeks, what should I do?”
MindsDB performs a semantic search on the CRM database, finds the relevant records, and passes them to Ollama.
The local model then generates a context-aware reply — all running completely offline.
What Makes It Special- Local-first execution: No API keys or external dependencies
- AI-driven suggested replies: Smart answers generated from real CRM data
- Semantic search: Understanding intent, not just keywords
- CSV upload: Dynamic CRM dataset retraining and ingestion
- Simple UI: Streamlit-based interface anyone can use
Demo SnapshotWhen I first saw the system respond intelligently to a customer query, it felt surreal.
A plain text query turned into a structured, empathetic AI reply.
Example:
User: “My refund has been delayed for 3 weeks. What should I do?”
AI Reply: “I apologize for the delay. Several refund cases, including yours, are pending. Please contact support to expedite your refund.”
It felt like watching a CRM come alive.
What This Project Taught MeThis project was more than a technical build — it was a personal transformation.
- I learned how MindsDB bridges SQL and AI, turning queries into intelligent actions.
- I integrated Ollama’s local LLMs to preserve privacy while maintaining performance.
- I explored semantic search and RAG for context-aware systems.
- Most importantly, I discovered how data and empathy can come together to improve customer experience.
Every time I refined a prompt or optimized a query, I realized that building with AI isn’t just about code — it’s about designing systems that understand humans better.
Next StepsThe journey doesn’t stop here. I’m planning to:
- Add a vector database (FAISS / Chroma) for faster similarity search
- Implement a full RAG pipeline for improved context retrieval
- Integrate with real-time CRM APIs like HubSpot or Salesforce
Repository GitHub: Final ThoughtsWhen I started this project, I wanted to learn MindsDB.
But I ended up learning how AI, data, and community can spark real innovation.
This project ignited my passion for open-source contribution — it pushed me to focus, stay consistent, and build with purpose.
It also showed me that you don’t need massive infrastructure to make an impact — you just need curiosity, courage, and commitment to keep experimenting.
If you’re passionate about AI + data + open-source, explore MindsDB.
You’ll discover what I did — that it’s not just a platform, it’s a gateway to building meaningful AI systems that solve real problems.
About the AuthorHi! I’m Akash, a recent Computer Science graduate who’s deeply interested in AI, open-source, and data-driven applications.
Building this project was my starting point in exploring open source seriously — it helped me gain confidence, improve my skills, and connect with inspiring developer communities like MindsDB.
I’m currently focused on contributing to open-source projects, learning advanced AI workflows, and preparing for my first full-time opportunity in tech.
Let’s connect and collaborate!
GitHub: