AI Analytics Assistant: 5 Part Implementation Guide
Published: January 19, 2025
AI Analytics Assistant V2: Execute Tasks, Automate Reports, Analyze Data with Voice and Text Instructions
AI Analytics Assistant - 5 Part Implementation Guide
Browse the slides or download the PDF
Full implementation guide. Demo app is live with restricted features. You can set up workflows as per the guide, connect them to your specific data sources, and add enhancements as needed.
Total video content is over 2 hours, split into 5 parts. Each part includes chapters with timestamps so you can jump directly to what matters.
3-minute snippets: demo and architecture
Build AI Action Agents: Beyond Chat - Voice agents that execute, automate, and analyze. Read the post.
Detailed video guide
Hands-on, step-by-step implementation: Full Video on YouTube
Source code
Source code, JSONs, and blueprints on GitHub. For updated source code go to Tigzig Analyzer and hit Docs.
Step by Step Guide
Medium guide with free access: Build an Analytics Assistant App - Medium
Full Deck Content (Text Format)
Text below was extracted from the source deck. Chart visuals stay in the PDF and as slide images above the post.
GenAI
TIGZIG.COM
Part 2
Voice Mode: Querying & Analyzing Data
AWS – Azure Data Warehouse
Custom GPT
Applied Generative AI for Analytics, Data Science & Business
Connect to AWS–Azure Data Warehouse
via Custom GPT & LLM Apps
Query. Transform. Analyze. Chart. File Ops. Build ML Models
All in the Natural Language of your choice
GenAI
TIGZIG.COM
Part 1
Lighthearted Introduction
Custom GPT: Connecting and Working with AWS-Azure DW
Colloquial Hindi / Hinglish example, with English translations
Applied Generative AI for Analytics, Data Science & Business
Amar Harolikar
TIGZIG.COM
Demo Video and Posts
Voice Mode Interaction with Data warehouse Tables
Data Transformations : Calculations – Data Cleaning – Summarizing – Merging
Data Analysis : Cross Tabs – Adhoc Query – Charts
Table Operations : Insert Records – Create Tables - Drop Tables – Download Tables
Multi-Warehouse Operations : Move tables between data warehouses (AWS – Azure)
Build ML Models: Similar to earlier model build with file upload . But with data from DW this time.
Limitations, Caveats & Constraints
How To Video & Posts [With Codes / Schemas / Github Repos]
With special focus on how to use GPTs to get all this done quickly and efficiently
Setting up & deploying FastAPI Server and Endpoints
Connecting Custom GPT to API endpoints – Build JSON Schema quickly and efficiently
Setup basic MySQL Server on AWS & Azure / Setup VM / Install phpMyAdmin
External LLM Apps: Build with Flowise AI. Rapid deploy to internet/ intranet
External LLM Apps: LLM options. Cost-Performance trade-offs
External LLM Apps: Low-cost custom deployment of Open Source LLMs.
External LLM Apps : API Connections with Flowise Custom Tool and JavaScript functions.
Basic Security: LLM Injection / API Keys / IP Rules / Allowed Domains
Access Controls and selective access.
Setting up MySQL Server on AWS & Azure, Installing phpMyAdmin for rapid prototyping
COMING NEXT
Applied Generative AI for Analytics, Data Science & Business
GenAI
Applied Generative AI for Analytics, Data Science & Business
TIGZIG.COM
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