CleanSlop vs NM Signals: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of CleanSlop and NM Signals — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

CleanSlop

Freemium

Scans vibe-coded apps for launch-blocking issues like leaked keys and data exposure, and reports what to fix first, right from your coding agent.

Agent-Native Scanning: Tell Claude Code, Codex, Cursor or other agents to scan with CleanSlop and fix findings where you build.
System-Level Code Mapping: Maps routes, background jobs, data and external services so each finding follows a real call path.
Prioritized Reports: Findings grouped as needs attention, worth a look and minor, worst first.
Evidence for Every Finding: Shows where each issue lives in your code and why it was flagged.
Plain-English Fixes: Explains what is wrong, why it matters and what to do about it.
Copy for LLM: Hands any report to your AI tool so it can apply the fixes.
GitHub App Integration: Scan only the repositories you select, read-only access.
Expert Triage Calls: Book a 15-minute call with a CleanSlop engineer to walk through findings.

A solo founder checks a Next.js and Supabase app built with an AI coding assistant before launching.
A developer discovers an exposed AI API key bundled into browser code and moves it to a server route.
A side-project builder verifies that one user's data cannot be read by another through database policies.
A Claude Code user runs a scan from the terminal and has the agent fix the top findings.
A team lead gets a short prioritized list instead of hundreds of linter warnings.
An indie hacker books an expert triage call to confirm an ambiguous finding.

Nyman Media

Freemium

Audits whether AI crawlers and assistants can actually read your website, then tracks how often they mention your brand.

AI Readiness Audit: Scores a public URL across 106 checks in six categories — crawlability, structured data, entity clarity, content structure, answerability and trust signals — for a readiness score out of 100.
Served-vs-Rendered Comparison: Measures how much of the browser-rendered page survives a fetch-only request, flagging JavaScript-dependent content that non-rendering AI crawlers never see.
AI Crawler Access Checks: Reports robots.txt, canonicals, redirects and status codes specifically for AI crawlers such as OAI-SearchBot, not just traditional search bots.
UX Review with Developer Brief: Runs a separate usability pass with visual layout analysis on paid plans and produces a copyable brief a developer can work straight from.
Saved Action Plans: Keeps an audit as a private baseline, lets you rank findings by priority, and records implementation progress against it.
Generated Fixes and Verification: Premium plans generate implementation guidance for a selected finding and verify the change against a fresh audit rather than trusting a checkbox.
AI Answer Snapshots: Asks the same five core questions weekly with three samples each, deciding by majority whether the brand is named, and charts the trend against tracked competitors.
Programmable Surface: A public REST API, CLI and MCP server let audits run inside CI/CD pipelines or be called directly by AI agents.

AI Search Readiness: Find out why an AI assistant summarizes a competitor's page instead of yours and fix the specific access or rendering issue behind it.
Pre-Launch QA: Audit a new marketing site before launch to catch blocked crawlers, missing markup and unreadable server-rendered content.
CI/CD Regression Guards: Call the REST API or CLI on every deploy so a rendering change that hides content from crawlers fails the build.
Brand Monitoring: Track weekly whether AI assistants name your brand in answers to the questions your buyers actually ask.
Agency Reporting: Produce white-label PDF audits and score comparisons for client sites on the Partner plan.
Content Restructuring: Use heading/body agreement and attribution checks to rewrite pages into retrievable, quotable sections.