IMPACTE: An AI-First Software Engineering Framework
Intelligent Multi-Agent Product-Centric Architecture with Cost-Efficiency and Trade-offs Engineering
A product-oriented framework for healthcare and financial technology environments.
The RAISE Workflow
A researcher mindset for AI-first software engineering
💡 Core Principle: As the cost of producing code approaches zero, human effort should focus on research-driven activities that extend beyond AI training data.
RESEARCH
Investigate emerging tools, methodologies, and innovations beyond LLM knowledge cutoffs.
DEFINE
Create AI-First documentation (PRD, RFC, ADR) with dual-model review workflow.
IMPLEMENT
Leverage a multi-LLM stack: select the right model for each task based on strengths and cost.
VALIDATE
Cross-validate between LLMs with human checkpoints at critical decision points.
ITERATE
Continuous improvement through knowledge sharing and workflow optimization.
🎯 Key Insight: LLMs have knowledge cutoffs. Your competitive advantage lies in researching what AI doesn't know — emerging tools, new frameworks, and cutting-edge practices — while ensuring best practices in Security, Scalability, and Cost Efficiency.
Built with Raise
A completely offline tool that allows you to explore large datasets, outperforming traditional tools like Microsoft Excel and Google Sheets.
Problem Solved
Efficiently analyze large files without the performance limitations of conventional spreadsheet software.
Write DDL SQL code and instantly visualize the resulting database diagrams, helping you make informed modeling decisions.
Problem Solved
Provides an intuitive, visual tool for database design that is more user-friendly than offerings from major cloud providers like AWS and GCP.
Research-Driven Foundations
Large Language Models (LLMs): Deployment, Tokenomics and Sustainability
Institutions:
Early science acceleration experiments with GPT-5
Institutions:
Community References
