Slide 1: Title

1. Subtitle: Analytics & Create Rapid Tally TDL with Zero Coding Skills Needed
2. Presenter: CA Ankush Jain, FCA | CISA (US) | FAFD | DISA | CCAB(ICAI) | B.COM.(H) | CEH(US) | ISO 27001 LA | PCI DSS Imp (TUV) | Full Stack Software Developer |
3. Slide 2: Disclaimer
4. This presentation is for informational purposes only and is based on personal research and experience. It is not intended to promote or demotivate any technology or application.
5. The implementation of any technology solutions should be evaluated against your organization's specific requirements, security policies, and regulatory obligations.
6. Application of these use cases should be done in a safe environment and with proper guidance and research only.

Slide 3: The Challenge

1. Traditional Tally TDL Development Challenges:
2.   Steep learning curve with complex syntax and definitions
3. Time-consuming development process
4.   High risk of syntax errors and debugging difficulties
5.   Limited access to comprehensive documentation
6.   Difficulty integrating advanced features like ODBC, external files, etc.

Slide 4: The Solution - Local LLM Deployment

Let’s Connect Tally Software with AI Tools for Data Analytics & Rapid TDL Creations.

Tally Data Analytics and AI-Enhanced TDL Development Pipeline.

✓ Prompt Engineering - Converting user requirements to expert-level prompts

✓ Knowledge Acquisition - Systematic collection of TDL documentation and examples

✓ Knowledge Processing - Creating optimized standalone reference document

✓ AI Integration - Configuring Claude with MCP and with specialized TDL knowledge

✓ ODBC Connection - Direct database access for real-time analytics and TDL generation

Slide 5: Demonstration Overview & Tech Stack Used for Implementation

1. ChatGPT: For Prompt Engineering.

2. Claude Desktop: AI-Tool for connecting Tally with AI and using NLP Chat prompt and Coding TDL.

3. MCP Tools:

1. File MCP Tool Access Disk Drive and to read and write files.
2. Playwright MCP Tool Access browser on command of Claude Prompt and scrape Tally Data
3. ODBC MCP Tool Connect with Tally ODBC Port 9000 and Access Tally Data

4. Tally Software: Education Mode

5. Windows Folder & Json Files : For Tally Knowledge Base

What are MCPs?

1. Simple Definition: Tools that give AI models special abilities to interact with the outside world
2. Technical Explanation: Extensions that allow Claude to access external systems and perform specific tasks
3. Benefits: Enable AI to go beyond conversation to take real actions and access live data
4. Implementation: Integrated directly within Claude Desktop for seamless workflow

Slide 7: MCPs We Leveraged:

1. File MCP: Access and manage files directly from Claude
2. Used to collect and organize Tally documentation
3. Enabled creation of the master TDL reference document
4. Playwright MCP: Web browsing and data collection capabilities
5. Used for systematic scraping of Tally developer documentation
6. Collected examples and reference material from authorized sources
7. ODBC MCP: Direct database connection abilities
8. Our "rocket engine" for TDL development
9. Connects Claude directly to Tally's database structure
10. Enables real-time data analysis and code generation
11. Creates a natural language interface to Tally data

Slide 8: Prompt Engineering Process

From Natural Language to Expert Instructions:

1.  Initial requirement gathering in plain language
2.  Custom GPT refinement to create structured prompts
3.  Conversion of business needs to technical specifications
4.  Enhancement with TDL-specific terminology and constraints

Slide 9: Knowledge Acquisition

Comprehensive TDL Knowledge Base:

1. Systematically web scraped Tally developer documentation
2. Collected all publicly available TDL references
3. Added reference manuals from official sources
4. Incorporated real-world TDL examples from community
5. Structured for optimal AI retrieval and comprehension

Slide 11: ODBC mcp Integration - The Game Changer

Slide 12: TDL Factory:-

Productivity Transformation:

1. 20+ TDLs created in just 15 days
2. Complex TDLs generated in single prompts
3. Reduced development time from days to minutes
4. Zero syntax errors in generated code
5. Enhanced capabilities through AI-suggested optimizations
6. Consistent implementation of best practices

Slide 13: Case Study: Turnover Analysis

Prompt In Claude Desktop:-

Direct Analytics Results in Claude connected with Tally:

Slide 14: Why Our Approach Wins :

1. Comprehensive Knowledge: Complete TDL reference embedded
2. Intelligent Understanding: AI comprehends TDL structure and relationships
3. Data Integration: Direct access to live Tally data
4. Speed: From concept to implementation in minutes
5. Quality: Error-free, optimized code following best practices
6. Accessibility: Natural language interface removes technical barriers

Conclusion :

Transforming the Tally Development Experience

1. Democratized Tally customization for all users
2. Eliminated technical barriers to Tally extension
3. Dramatically reduced development time and cost
4. Enhanced quality through consistent best practices

Created new possibilities for Tally integration and

1. Set new standard for Tally TDL development