Repository Trail
Code repositories and software builds linked from project records where available.
AI SOFTWARE SYSTEMS
Software systems where AI, automation, and data support real work.
Custom internal tools, workflow automation, AI-assisted applications, and data platforms built around business use cases — with visible project records.
A Think Big Digital Solutions vertical led by Parth Ghumatkar.
System flow
AI tools
Data pipelines
Automation
Business problem
Workflow logic
Software system
AI / data / automation
Usable product
Internal systems
Dashboards
Content engines
Work You Can Review
Visible work you can review.
Current Work and Early Work projects bring together code, documentation, system context, and project links in one place.
Build Record
Project walkthroughs and technical notes attached to individual project pages.
Build Documentation
Readmes, architecture notes, and explanations published alongside each build.
Data & Systems
Dashboards, data platforms, and working applications shown as project outcomes.
Technical Founder Layer
Self-built credibility shaped through building, testing, and improving.
Parth’s technical path started with curiosity, electronics, YouTube learning, robotics, Arduino, C++, IoT, drones, Python, Raspberry Pi, and public project demos.
Over time, that foundation moved into software systems, AI-assisted workflows, data platforms, internal tools, and business-facing applications.
The stronger point is not that he started young. It is that he kept building.
Independent technical learning
Self-led building across hardware, software, automation, and AI workflow experiments.
Project-led development
Learning through working builds, debugging, testing, documentation, and improvement.
Public technical history
GitHub repositories, demos, presentations, and documented build material.
Current software ownership
Full-project ownership across software, AI workflow, automation, and data-system builds.
Think Big delivery
Review, QA, documentation, deployment discipline, and delivery oversight can be added through Think Big when needed.
What We Build
Software systems where AI, automation, data, and workflow logic support business work.
The work can start from a business problem, a repeated manual task, a data-handling need, or an internal process that needs better structure.
Internal Tools
Custom tools for business processes, forms, users, data, admin workflows, product-style utilities, and repeated internal tasks.
AI-Assisted Workflow Applications
Applications where AI supports analysis, summarization, classification, reporting, research, content creation, media workflows, or decision support inside a defined workflow.
Automation Systems
Workflows that connect APIs, databases, AI models, files, forms, dashboards, and business operations — including content and media pipelines.
Data and Reporting Platforms
Systems that collect, structure, analyze, display, and publish data through dashboards, reports, SaaS-style interfaces, static pages, or searchable views.
Featured Projects
Real projects. Real code. Real systems you can explore.
Featured builds from Current Work and Early Work — with more published projects available on the projects pages.
TradePre
ML-powered trading prediction system for Indian stocks using technical indicators, ensemble models, backtesting, and an LLM synthesis layer.
- Python
- XGBoost
- scikit-learn
- Pandas
- Kite Connect
- Flet
- Claude / LLM
- Backtesting
How We Think About AI
AI works best inside a clear system.
AI can support analysis, routing, content, reporting, and automation — but the surrounding software still matters: data flow, permissions, testing, documentation, deployment, and user adoption.
The focus is not AI as a slogan. The focus is useful systems that can be built, reviewed, improved, and used.
Workflow line
- Problem
- Software logic
- Data
- AI layer
- Workflow output
How the Work Happens
Start with the workflow. Then define the system around it.
Useful software work starts with the workflow, then moves through system design, build, testing, and improvement.
- 01
Understand the workflow
Clarify users, inputs, outputs, and current manual steps.
- 02
Define the system
Map screens, data, logic, integrations, and technical structure.
- 03
Build the first version
Create a working version with the required software, data, or automation layer.
- 04
Test and improve
Debug, simplify, refine, and improve based on actual use.
- 05
Review for serious delivery
Add review, documentation, security checks, and delivery oversight where needed.
Have a software or AI workflow idea?