AI Driven Advanced Analytics. Reasoning based Sequential Agents. Connect to any database — o3-mini/deepseek-r1 / gemini-flash-2.0.

Amar Harolikar

Feb 143 min read

Updated: 4 days ago

𝐓𝐫𝐲 𝐢𝐭 𝐅𝐫𝐞𝐞 👉 rex.tigzig.com (Mobile/ Web). Open Source

✔ Watch Short 7.5 mins video here on LinkedInhttps://link.tigzig.com/rex3

✔ Full 1 Hour Video on YouTube

𝐁𝐮𝐢𝐥𝐝 𝐚𝐧𝐝 𝐃𝐞𝐩𝐥𝐨𝐲 𝐀𝐈 𝐃𝐫𝐢𝐯𝐞𝐧 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐰𝐢𝐭𝐡 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 & 𝐒𝐞𝐪𝐮𝐞𝐧𝐭𝐢𝐚𝐥 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐋𝐢𝐯𝐞 𝐀𝐩𝐩

https://www.youtube.com/watch?v=hqn3zrdXVSQ

𝐀𝐈 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐀𝐩𝐩 (𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞) - 𝐌𝐮𝐥𝐭𝐢 (𝐒𝐞𝐪𝐮𝐞𝐧𝐭𝐢𝐚𝐥) 𝐀𝐠𝐞𝐧𝐭𝐬. 𝐓𝐫𝐲 𝐢𝐭 👉 rex.tigzig.com Connect to any database - o3-mini/deepseek-r1 / gemini-flash-2.0.

✔ 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐭𝐫𝐲 𝐢𝐭 𝐪𝐮𝐢𝐜𝐤𝐥𝐲?

Use the "Sample" functionality to auto-upload a sample file. No DB credentials needed.

✔ 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬?

▸ Multi-step agent flow. Analysis plan by reasoning models, execution by GPT-4o

▸ Choice of reasoning model: Gemini Flash 2.0, o3-Mini, DeepSeek-R1.

✔ 𝐖𝐡𝐚𝐭 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧𝐬?

▸ Connect to any remote database or upload a file.

▸ No database? Not to worry- Temporary Postgres DB created on-the-fly

✔ 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠?

Execution logs , Agent reasoning view

✔ 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐁𝐀𝐔 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐢𝐞𝐬?

Natural Language (NL)-to-SQL, NL-to-Python: Statistical Analysis & Charts, Interactive Tables with quick Stats

✔ 𝐃𝐞𝐦𝐨𝐞𝐝 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬

▸ Bank Credit Cards: Modeling Data Mart creation + Customer Profile (1M-row Customer Table & 10M Transactions Table on AWS RDS MySQL).

▸ Weighted Score-Based Ranking of Indian credit card issuers

✔ 𝐂𝐨𝐯𝐞𝐫𝐞𝐝 𝐢𝐧 𝐕𝐢𝐝𝐞𝐨, in addition to demo:

▸ Query failures, live debugging, performance & cost considerations

▸ High Level architecture & API Flows

▸ Agent setups and orchestration.

✔ 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐜𝐥𝐨𝐧𝐞, 𝐝𝐞𝐩𝐥𝐨𝐲, 𝐦𝐨𝐝𝐢𝐟𝐲?

▸1 primary repo, 3 backend repos, 6 Flowise Agent Schemas + Tool Schemas

Link in comments and on build section on rex.tigzig.com

▸ Step-by-step deployment guide + how to deploy app with Cursor using natural language prompts.

✔ 𝐇𝐨𝐰 𝐰𝐚𝐬 𝐢𝐭 𝐛𝐮𝐢𝐥𝐭?

▸ Built with Cursor AI - my top AI-coding tool

▸ AI Backend: Flowise AI - My top LLM App platform.

▸ Python Charts/Stats: E2B on Flowise

▸ UI: React, TypeScript, Vite, Shadcn.

▸ DB Connections: Custom built FastAPI servers.

▸ Deployment: Vercel (main site), Hetzner (via Coolify for FastAPI & Flowise)

▸ On-the-fly PostGreSQL DBs: Neon (blazing fast, great API's, ideal for LLM apps).

▸ Auth: Auth0 (experimental)

▸ Workflow: Make.com for Auth records (experimental)

✔ 𝐕𝐢𝐝𝐞𝐨 𝐆𝐮𝐢𝐝𝐞

https://lnkd.in/dzxuziAD. See comments for time-stamped contents.

✔ 𝐂𝐚𝐯𝐞𝐚𝐭𝐬

Prototype (working version)

YouTube Time Stamps

Click on time-stamp (in YouTube description) to jump direct to section of interest

https://www.youtube.com/watch?v=hqn3zrdXVSQ

00:00:00 - Quick Overview of Capabilities

00:02:08 - Connect to DB & Analyze: Modeling Data Mart & Customer Profile Summary

00:06:43 - File Upload & Analyze: India Bank Ranking - Credit Cards

00:09:21 - Sequential Agent Framework: Setups & Orchestration

00:15:02 - Performance Considerations (Quality, Speed, Reliability, Latencies, Agent Backend, SQL Call Failures, Database Server, API Call Failures, Error Catching, Validations)

00:23:09 - Cost Considerations

00:29:49 - Live Error Debugging

00:37:37 - High Level Architecture & API Flows

00:45:08 - Deployment Guide & GitHub Repo Walkthroughs

00:58:19 - App Functionalities - How to Have Cursor Explain it.

01:01:05 - Top Resources

01:01:51 - End

𝐆𝐢𝐭𝐇𝐮𝐛 𝐑𝐞𝐩𝐨𝐬 & 𝐒𝐜𝐡𝐞𝐦𝐚𝐬

1. Main Repo

https://github.com/amararun/shared-rexdb-auth-embed-v2

2. FastAPI Server : SQL DB Connect

https://github.com/amararun/shared-fastapi-rex-db-coolify