AI-Powered Production Readiness

Know if Your Code Will Survive Production

Detect scalability, security, and reliability issues before they cause outages. AI-powered analysis across 8 quality dimensions.

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prodsensor.com/app
PRODUCTION READY
84 Scanned | 23 Endpoints | 3 Findings | Python · FastAPI
~4,200 RPS MAX RPS
~12,600 MAX USERS
~45ms P95 LATENCY
AI ANALYSIS

api-service is production ready. Strong async patterns, proper connection pooling, and comprehensive error handling detected across all endpoints.

Analyzes codebases in
$5,600 /min Average cost of outages
25-40% Engineering time on incidents
8 dimensions Comprehensive analysis

What Sets Us Apart

Insights you won't find in traditional static analysis tools

Scale Predictions

Know your limits before you hit them. Get RPS ceiling, P95 latency, and max concurrent user estimates.

~4,200 RPS ~45ms ~12,600 users
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NEW

Path to Production Ready

Prioritized fix roadmap with effort estimates. Know exactly what to fix and how long it will take.

Fix pool size (~1h) → +25 pts
Add rate limiting (~4h) → +15 pts
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NEW

Track Technical Debt

Accept risks, mark fixes, or snooze findings with documented reasons. Decisions persist across runs.

Accept Risk Mark Fixed Snooze
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NEW

Track Progress Over Time

Compare current analysis with previous runs. See trends with visual indicators.

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8 Dimensions of Quality

Not just security. We analyze Scalability, Reliability, Performance, Observability, Testability, Architecture & Maintainability.

Scalability Security Reliability +5 more
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CI/CD Integration

GitHub Action and CLI tool for automated production readiness checks on every PR.

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AI-Powered PR Review

Beyond pattern matching — context-aware analysis that understands your code’s intent, catches hidden bugs, and suggests production-ready fixes.

api/routes/users.py +87 −12
40 def get_user(user_id):
41 + query = f"SELECT * FROM users WHERE id={user_id}"
42 + return db.execute(query)
BLOCKER SQL Injection — unsanitized user input in query
56 def list_orders(user):
57 + orders = Order.query.filter_by(user_id=user.id)
58 + for o in orders:
59 + o.items = Item.query.filter_by(order_id=o.id)
MAJOR N+1 Query — loads items individually per order
72 async def send_notification(user):
73 + result = requests.post(WEBHOOK_URL, json=payload)
74 + return result.json()
MAJOR Blocking call inside async function

AI Findings

6 Blockers 4 Major 1 Minor
BLOCKER 95% confidence

SQL Injection Vulnerability

User-supplied user_id is interpolated directly into SQL query string without parameterization, enabling arbitrary SQL execution.

Suggested Fix db.execute("SELECT * FROM users WHERE id = :id", {"id": user_id})
MAJOR 90% confidence

N+1 Query Pattern

Loading items inside a loop triggers a separate query per order. With 1,000 orders this fires 1,001 queries.

Suggested Fix Order.query.options(joinedload(Order.items)).filter_by(...)
MAJOR 100% confidence

Blocking Call in Async Context

Synchronous requests.post() blocks the event loop inside an async function, starving other coroutines.

Suggested Fix await httpx.AsyncClient().post(WEBHOOK_URL, json=payload)

Pattern + AI Reasoning

Combines static analysis rules with LLM reasoning to catch issues that pattern matchers miss—like business logic flaws and architectural anti-patterns.

Context-Aware Analysis

Understands your framework, ORM, and async runtime. Findings reference your actual code paths, not generic warnings.

Suggested Fixes

Every finding includes a concrete, copy-pasteable fix tailored to your codebase—not just a description of the problem.

How It Works

From code to production confidence in minutes

1

Upload

Drop your codebase ZIP, paste a GitHub URL, or enter code directly

2

Analyze

AI scans your code across 8 quality dimensions in seconds

3

Fix

Get prioritized findings with specific recommendations and code fixes

8 Dimensions of Production Readiness

Comprehensive NFR analysis that catches what code review misses

Scalability

N+1 queries, unbounded fetches, missing pagination, connection pool issues

  • N+1 query patterns
  • Connection pool sizing
  • Pagination issues
  • Unbounded data fetches

Security

Hardcoded secrets, SQL injection, missing authentication, XSS vulnerabilities

  • Hardcoded credentials
  • Injection vulnerabilities
  • Missing auth checks
  • OWASP Top 10

Reliability

Missing circuit breakers, no retry logic, unhandled exceptions, cascade failures

  • Circuit breaker patterns
  • Retry logic
  • Timeout configurations
  • Graceful degradation

Performance

Blocking calls, inefficient algorithms, memory leaks, slow database queries

  • Time complexity (O(n²))
  • Data structure choices
  • Memory/space patterns
  • Sync vs async patterns

Observability

Missing logging, no metrics, absent tracing, poor error messages

  • Structured logging
  • Metrics instrumentation
  • Distributed tracing
  • Health checks

Architecture

God classes, circular dependencies, tight coupling, poor separation

  • Class responsibilities
  • Dependency structure
  • Layer separation
  • Coupling analysis

Testability

Untestable patterns, hidden dependencies, static coupling, no interfaces

  • Dependency injection
  • Interface abstractions
  • Mock-friendly design
  • Test coverage gaps

Maintainability

Complexity hotspots, code smells, technical debt, documentation gaps

  • Cyclomatic complexity
  • Code duplication
  • Documentation gaps
  • Naming conventions

Trusted by Engineering Teams

See how teams ship with confidence

ProdSensor caught an N+1 query that would have brought down our checkout service on Black Friday. The fix took 30 minutes. The outage would have cost us six figures.

MK

Michael Kim

Staff Engineer

E-commerce Platform

We integrated ProdSensor into our CI pipeline. Now every PR gets a production readiness score. It's changed how our team thinks about code quality.

SJ

Sarah Johnson

Engineering Manager

SaaS Startup

The AI insights are incredible. It doesn't just find issues - it explains exactly how they'll fail in production and gives you the fix. Like having a senior SRE review every commit.

AR

Alex Rivera

Principal Architect

FinTech Company

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