Neutrino

Inspiration

The most expensive product failures are not bugs. They are architectural bets that aged badly.

Engineers understand systems. Executives understand risk. Neither sees the full picture.

Neutrino was built to bridge this gap automatically — translating technical reality into strategic clarity while detecting stack decay before it becomes business risk.

What it does

Neutrino analyzes any public GitHub repository in under two minutes and produces two structured outputs from a single pipeline:

Technical Deep Dive

Architecture patterns, dependency risks, security gaps, deprecated packages, and prioritized technical debt.

Executive Brief

Stack longevity, ROI exposure, modernization urgency, and market-backed trend signals — expressed in business language.

Trend Intelligence Engine

Live RAG cross-references your stack against:

GitHub velocity
HackerNews sentiment
Real version data
Search momentum

It does not just detect what is broken. It detects what is becoming obsolete.

How we built it

Neutrino runs an async-first, multi-agent architecture optimized for speed and cost efficiency.

Pipeline Overview

Smart Ingestion

GitHub URL → recursive file tree
Strategic 3-Pass Fetcher selects ~150 most relevant files

Parallel Stack & Feature Detection

Stack detection (languages, frameworks, DBs)
Integration inventory
Feature extraction

Live Trend Enrichment (RAG)

pgvector cache lookup
On miss: GitHub + HackerNews + Serper fetch
LLM synthesis stored for reuse

Parallel Deep Review

Frontend Agent
Backend Agent
Infrastructure Agent
Executed via asyncio.gather

Synthesis

Report Agent merges technical + trend intelligence
Generates Technical and Executive dashboards
Persisted in Supabase and rendered in React

Core Technologies

Frontend: React 19, TypeScript, Tailwind
Backend: Python 3.12, FastAPI, asyncio
Data Layer: Supabase + pgvector
AI Routing: OpenRouter with smart model selection
Optimization: 40–60% token compression, intelligent caching

Challenges we ran into

Reducing token cost without losing analytical depth
Designing true parallel agent execution
Building version-aware intelligence with live market signals
Translating technical findings into executive-ready language
Maintaining speed under full-repository analysis

Speed, cost, and depth had to be balanced simultaneously.

Accomplishments we're proud of

Full repository audit in under 2 minutes
~60% token reduction with zero depth loss
Dual-lens reporting from a single analysis
Version-aware RAG with persistent trend caching
Live deployed demo with real-time parallel agent execution

What we learned

Architectural decay is more dangerous than visible bugs
Context selection matters more than model size
Parallel orchestration drastically reduces latency
Caching strategy directly impacts AI economics
Business stakeholders require structurally different insight formats

Summarization is not enough. Synthesis and prioritization are critical.

What's next for Neutrino

Commit Intelligence

Event-driven analysis on every push. Developer velocity trends and tech debt curves over time.

Enterprise Security Mode

Private VPC deployment, on-prem inference, full audit logging.

Adaptive Learning Feed

Stack-aware alerts, security advisories, and curated updates matched to active builds.

Neutrino Score

A composite stack health score measuring: Security, scalability, maintainability, cost efficiency, and ecosystem momentum — benchmarked against similar repositories.