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:electron:
Boosting AI's ego while fixing its mistakes
:electron:
Boosting AI's ego while fixing its mistakes

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@HacKP-CyberDome @CatalystIEDC @kp-grapnel

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rahulsunil2/README.md

👋 Hey, I’m Rahul Sunil

I build production-grade GenAI systems that solve hard enterprise problems.
If it involves LLMs, workflows, data, or risk automation at scale, I’m in.


🧠 What I Actually Do

  • 🏗️ Own GenAI products end to end, from architecture to production
  • 🤖 Build reliable LLM systems, not demos
  • ⚡ Optimize for impact: latency, cost, accuracy, and scale
  • 📈 Ship things that sales teams can confidently sell

🏆 Highlights

GenAI Patent Inventor
Workflow Context Extractor improving enterprise workflow accuracy from 62% → 97%

⚙️ Led RiskAgent (LLM Product)

  • Onboarding time reduced from weeks to hours
  • +40% citation readability, 15% fewer SOC2 misses
  • 60% API latency reduction, 75% DB load reduction

🚀 Built DesignAI

  • 60% faster solution design
  • Adopted by Fortune 100 teams
  • Contributed to a $35M Series B

📊 Scaled Product Data

  • Coverage across 280+ sanctions lists
  • Foundation for a projected 2.5× revenue uplift

🧪 Selected Projects

🛡️ RiskAgent – LLM-Powered Risk Automation
  • Auto-extracts answers and risks from SOC2, ISO, policies, and pentests
  • Audit-grade citations with full coverage and lower reasoning variance
  • Parallelized pipelines achieving 21–35 min SIG runs
  • Designed for enterprise reliability, observability, and scale
🧩 DesignAI – GenAI Solution Designer
  • Reduced solution-design time by 60 percent
  • Used by Fortune 100 implementation teams
  • Scaled with containerization, retries, and load balancing
  • Deep LLM analytics via Portkey and Datadog APM
📈 Product Data Platform
  • Built ingestion and refresh pipelines for 280+ sanctions lists
  • Automated normalization, indexing, and entity linking
  • Forms the data backbone for advanced risk and compliance workflows
🧠 Workflow Context Extractor (Patent)
  • GenAI system for large-scale enterprise workflows
  • Improved action accuracy from 62 percent to 97 percent
  • Filed in the US and India
  • Winner of company-wide innovation award

🛠️ Tech Stack

Languages
Python • TypeScript • JavaScript • SQL

GenAI & ML
LLMs • RAG • Prompt Engineering • Fine-tuning • Embeddings

Backend & Data
FastAPI • Django • Celery • PostgreSQL • MongoDB • Elasticsearch • Qdrant

Infra & Ops
AWS • Azure AI • Docker • CI/CD • Observability • LLM Tracing


🌍 Let’s Connect


💡 Fun Fact

I enjoy turning “this sounds impossible” into “it’s live in prod.”

If you’re building something ambitious in GenAI,
we’ll get along just fine.

GitHub contribution snake animation

Pinned Loading

  1. ABC-Bank ABC-Bank Public

    Python 2 1

  2. deblurGAN deblurGAN Public

    Python 2

  3. MediLocker MediLocker Public

    ThingQbator

    Python 1

  4. COVID-Dashboard COVID-Dashboard Public

    CSS

  5. Eyantra-ConstructOBot-hack.io Eyantra-ConstructOBot-hack.io Public

    C

  6. hackp-ngcollege hackp-ngcollege Public

    TypeScript