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

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

NM Signals

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.

WorkWeave

Freemium

Engineering intelligence platform that measures the ROI of AI coding spend and routes every prompt to the most cost-efficient model.

Prompt-to-Production Analysis: LLM and ML models analyse commits, tokens, pull requests, reviews, deploys, and AI telemetry as a single pipeline rather than isolated metrics.
AI ROI Scoring: Token consumption is scored for cost, efficiency, and quality, benchmarked against thousands of engineering organisations, so spend is measured by value rather than volume.
Per-Engineer AI Impact: A breakdown of AI usage rate, AI score, code quality, and output change versus baseline for each engineer over a rolling window.
Weave Prompt Router: Classifies every prompt and routes it to the most cost-efficient model without compromising speed or quality, learning from individual and organisation-level feedback.
One-Command Router Install: Running npx @workweave/router detects your existing clients and writes one env var per provider for Anthropic, OpenAI, and Google, with the bearer token staying on your device unless you export it.
Wooly Engineering Agent: An AI agent that reviews all your engineering data to suggest where and how to improve, answering questions grounded in your own records with citations, available in-app or over MCP.
Standard Framework Reporting: DORA and SPACE metrics plus survey data combined with AI-specific measures in one pane of glass for executive reporting.
Enterprise Compliance Controls: SOC 2 Type II certification with regular third-party audits, GDPR and HIPAA compliance, SSO via SAML and OIDC, SCIM provisioning, and role-based access.

Justifying AI Tooling Spend: Producing an executive report on what a Claude Code or Cursor rollout actually returned, benchmarked against peer organisations.
Cutting Inference Costs: Routing routine edits to cheaper models and reserving frontier models for work that needs them, without changing how developers work.
Finding SDLC Bottlenecks: Identifying where pull requests, reviews, or deploys stall using DORA and SPACE metrics alongside AI telemetry.
Coaching Engineers on AI Use: Seeing which engineers get real quality and output gains from AI assistance and which are consuming tokens without effect.
Agent Observability: Tracking what autonomous coding agents contribute to the codebase separately from human-authored work.
Ad-Hoc Engineering Questions: Asking Wooly where deployment cycles are getting stuck and receiving an answer cited back to the organisation's own records.