// signal

I build AI-enabled software systems where retrieval, backend architecture, model inference, product UI and infrastructure have to work together.

My current direction is RAG + AI application engineering — moving beyond “chat with a PDF” demos toward systems with ingestion pipelines, vector retrieval, citations, background workers, local/cloud model options and measurable quality.

// system.map

What I care about in AI systems

retrieval quality • grounding • citations • async ingestion • API design • observability • evaluation • developer experience

---

// stack.matrix

🧠 AI RAG Embeddings Vector Search Ollama Groq Chroma

⚙️ Backend Python FastAPI PostgreSQL Redis Workers REST APIs

✦ Product Next.js React TypeScript JavaScript HTML/CSS

⬡ Platform Docker MinIO Git GitHub Linux Android

---

// selected.work

🧠 UNIVERSAL_RAG Local-first RAG platform Document upload → parsing → chunking → embeddings → vector retrieval → grounded answers with citations. Python FastAPI Next.js PostgreSQL MinIO Chroma Ollama	🔬 Advance_RAG Retrieval experimentation A Python project exploring advanced Retrieval-Augmented Generation patterns and AI application workflows. Python RAG LLMs
🚗 ride2go TypeScript product build A modern web project expanding my product-development and TypeScript work. TypeScript Web	📱 android-kotlin-mvvm-architecture Android architecture Android/Kotlin application structure centered around maintainable MVVM patterns. Kotlin Android MVVM

---

// build.protocol

01  PROBLEM
      ↓
02  SYSTEM BOUNDARIES
      ↓
03  END-TO-END VERTICAL SLICE
      ↓
04  OBSERVE THE DATA FLOW
      ↓
05  TEST FAILURE MODES
      ↓
06  MEASURE QUALITY
      ↓
07  SHIP → LEARN → ITERATE

I like engineering the space between prototype and product: clean boundaries, reproducible environments, useful APIs, explainable retrieval and maintainable systems.

---

// learning.2026

current:
  - production RAG architecture
  - retrieval evaluation
  - reranking and hybrid search
  - agent / tool workflows
  - async ingestion pipelines
  - AI observability

next:
  - multi-tenant AI platforms
  - stronger eval harnesses
  - scalable deployment patterns
  - product-level AI reliability

---

// github.telemetry

Current signal: building AI/RAG systems, shipping new projects, and focusing on production-minded engineering.

---

// identity.object

ashutosh = {
    "mode": "builder",
    "focus": ["AI systems", "RAG", "full-stack", "Android"],
    "current_system": "UNIVERSAL_RAG",
    "optimizing_for": [
        "usefulness",
        "reliability",
        "clarity",
        "shipping"
    ],
    "loop": "build → measure → improve → repeat"
}

---

// connect

I’m open to collaborating on AI/RAG systems, backend platforms, developer tools, Android and practical full-stack products.