A VibeKiln ProductProduct Optimization for the AI Era.
See how humans, chat AIs, and agents find, understand, trust, and use your product, then turn the evidence into prioritized fixes, constraints, and agent-ready implementation context.
Free preliminary score. 3 left this session. The paid audit explains why, who answer engines recommend instead, and which source clusters they trust.
Trust pipeline for the AI era
See whether your product can be found, believed, compared, and safely used.
Cutline connects discoverability and engineering rigor into one evidence trail: source surfaces, citable proof, competitive context, and constraint-backed implementation your team or coding agent can execute.
Be found
Check whether answer engines can retrieve, classify, and include your product in the right category.
Be believed
Use source surfaces, proof pages, reviews, and citations to make claims citable instead of merely crawlable.
Be compared fairly
Map the directories, reviews, comparison pages, and developer sources that shape recommendations against competitors.
Be safely used
Turn citation gaps, action readiness, and engineering risks into constraints, action maps, and guardrails agents can follow.
Built for developers tired of:
Fictional Wall of Shame
Satirical quotes, not customer testimonials. They illustrate the failure modes Cutline is built to prevent.
"Did My AI Transformation in an afternoon!"
— Sloppy McSlopperson
VP of AI Buzzword Enforcement, ToDoLy
"Finally added AI to my AI! Now my roadmap has AI-powered AI features with AI on top!"
— Chad Disruption
Chief Vibes Officer, StealthMode.io
"Cutline told me NOT to build 47 of my features. Ignored them all. My app has 3 users now (all bots)."
— Brock Shipper
Founder & Full-Stack Everything, MoveF4st.app
"Spent 6 months building in stealth. Cutline would've saved me, but I was too busy grinding."
— Preston Hustle
CEO of Grinding, PivotPending.com
"Web3 meets AI meets blockchain meets... wait, what problem were we solving?"
— Devin Synergy
Head of Innovation Theater, Unicorn.xyz
Proof surfaces Cutline can verify today
What Buyers and Answer Engines Can Verify
Cutline is still early-access, so this section uses factual product proof instead of invented customer quotes: live surfaces, packaging details, and engineering context humans and answer engines can cite.
Engineering risk scan
Cutline scans code for security, reliability, and scalability risks before teams trust AI-generated changes in production.
Run a code scanAgent-ready constraints
Cutline turns product intent into structured constraints that can travel into MCP-compatible coding agents and IDEs.
Install the MCP integrationCitable proof surfaces
Security, pricing, and comparison pages give buyers and answer engines crawlable sources for how Cutline is packaged, governed, and used.
Read security and trust detailsTrust before, during, and after build
Where product trust breaks down
Cutline sits between product validation, code risk review, and MCP-enabled implementation: the moments where unclear intent, weak proof, or missing constraints can break confidence.
Validate intent before you build
Use Cutline to validate the product idea, surface assumptions, and turn product intent into implementation constraints buyers and agents can understand.
Verify the change before you ship
Use Cutline to scan for security, reliability, and scalability risks that generic code generation prompts often miss.
Harden the context before agent handoff
Use Cutline MCP outputs to give Cursor, Claude, and other MCP-compatible agents concrete guardrails, source context, and acceptance criteria.
Not generic AI safety
Cutline is focused on product engineering constraints, code risk, and agent implementation context rather than general AI policy review.
Not content moderation
Cutline evaluates what should be built and how the software should behave, not just whether generated text is acceptable.
Not a passive checklist
Cutline turns findings into MCP-readable context that can travel into the coding environment where implementation happens.
Try our Candid AI
Confidence Infrastructure for AI-Built Products
Trusted Intent: The Intent-to-Constraint Engine
Your coding agent needs more than vibes: it needs trusted intent. Cutline extracts security, scalability, and reliability requirements from your ideas and feeds them to your AI.
What it does
Identifies critical non-functionals—auth patterns, rate limits, data privacy, error handling—that most prompts miss.
Prevents the first failure mode: building the wrong thing from an underspecified prompt.
Trusted Changes: The Dependency Logic Guardrail
Your coding agent doesn't know every existing security policy or reliability constraint. Cutline's Constraint Graph helps new features respect existing architectural decisions.
What it does
Real-time constraint checking. If a new feature would violate security policies, break rate limits, or introduce reliability issues, your agent knows before writing code.
Trusted Agent Context: Hardened Spec Injection
Your coding agent is only as trustworthy as the context you give it. Cutline injects production-grade constraints directly into your agent's context window.
What it does
Provides security policies, reliability requirements, and scalability constraints as structured context your agent can actually use.
Prevents context drift by giving agents source-backed constraints they can actually use.
Trusted Release Path: Production-Ready Shift-Left
Security, scalability, and reliability can't be afterthoughts. Cutline shifts them left so release confidence is part of the build path.
The result
Your coding agent ships with security, scalability, and reliability constraints baked in from day one.
Compliance Signals Your Agent Can Actually Use.
How does Cutline add governance trust to AI-built code?
Cutline auto-detects your tech stack (Stripe for PCI-DSS, FHIR for HIPAA, OpenAI for OWASP LLM) and injects framework-specific constraints into your coding agent's context via MCP. Every project gets SOC 2 and security baseline constraints (auth middleware, rate limiting, audit logging). Additional frameworks load automatically when Cutline detects relevant libraries, turning governance into implementation context instead of a separate checklist.
Cutline automatically detects your stack and loads the right compliance constraints into your coding agent's context. SOC 2 and security baselines for everyone; regulated frameworks only when your code needs them.
Access control, monitoring, change management, vendor risk, audit logging
Auth middleware, input validation, secrets management, CSRF, rate limiting
Triggers: Cloud deployment, AWS/GCP/Azure
Cloud security controls, IAM, encryption, logging, incident management
Triggers: Stripe, payment libs
Tokenization, TLS enforcement, audit trails, need-to-know access
Triggers: Health/FHIR/HL7 libs
PHI encryption, minimum necessary, BAA verification, audit controls
Triggers: GovCloud, FIPS
FIPS 140-2 crypto, continuous monitoring, boundary protection, SBOM
Triggers: Analytics, auth libs
Right to erasure, data portability, consent gating, PII anonymization
Triggers: OpenAI, LangChain, RAG
Prompt injection defense, output sanitization, agent RBAC, tenant isolation
Triggers: Plaid, banking SDKs
NPI isolation, MFA enforcement, intrusion detection, 7-year WORM retention
Triggers: Clever, Canvas, EdTech
Parental consent gates, profiling ban, data destruction, age gating
Triggers: iOS, Swift, StoreKit
App privacy disclosures, in-app purchase compliance, in-app account deletion support
Your coding agent gets framework-specific constraints injected automatically, so governance trust is present while code is being written.
Which framework applies to me?
Give Coding Agents the Context They Need to Act Safely
How does Cutline make agent execution trustworthy?
Install Cutline MCP with npm install -g @vibekiln/cutline-mcp-cli@latest, then run cutline-mcp setup to configure your IDE (Cursor, Claude Code, or Windsurf). No account needed for free security vibe checks. Works via Model Context Protocol to inject source-backed security, scalability, and reliability constraints directly into your coding agent's context.
Give Cursor, Claude Code, and Windsurf the security, scalability, and reliability context they need. Cutline turns source-backed constraints into agent-ready context so execution stays aligned with the product trust story.
FREE
Ask your agent to "run a security vibe check" — scans your code for security, reliability, and scalability issues.
PREMIUM
Product-specific constraint graph, RGR remediation plans, pre-mortem analysis, and persona feedback.
Works with
AI Code Editors
No-Code App Builders
Design Canvases
Documentation
How does Cutline make AI-built products trustworthy?
AI-built products become trustworthy when the product story, source surfaces, implementation constraints, and agent context all agree. Without that evidence trail, answer engines may misclassify the product and coding agents may produce technically functional but production-flawed software: security gaps in auth patterns, scalability assumptions that break under load, and reliability issues that only surface in production.
Cutline, built by VibeKiln, is a product trust platform for the AI era. It helps answer engines understand and cite your product, then turns the same product intent into non-functional requirements, pre-mortem risks, and structured constraints your coding agent can use.
The result: humans, answer engines, and agents get a more consistent picture of what the product is, why it should be believed, and how it should be built safely. Cutline integrates directly into AI coding tools like Cursor, Claude Code, and Windsurf via the Model Context Protocol (MCP), providing validated technical constraints while your agent works.