IntentGuard produces seven distinct use cases from a single analysis. Each audience gets a report written in their language — not a raw security dump. Here is exactly how each workflow works, and what you get.
Manual TDD costs $22,000–$150,000 per engagement and takes 3–12 weeks. You evaluate 50+ deals a year. Every week a deal stalls in diligence is a week a competitor can move. IntentGuard delivers the same investor-grade technical health report — architecture maturity, security posture, compliance status, intent alignment, and TCO signals — in under one hour, for a fraction of the cost.
The research is clear: VCs treat AI-generated reports with "trust but verify" discipline. IntentGuard's multi-model consensus directly addresses this — every finding is verified by up to 4 independent AI models. A finding that only one model raised does not appear in your report.
Intent drift is the gap between what your product was designed to do and what the codebase actually does. It widens with every AI-assisted sprint. Your Series A meeting is in three weeks. IntentGuard measures that gap — architecture maturity, security posture, compliance status, undeclared AI components — so you walk in prepared, not surprised.
"Your investor meeting is in three weeks. IntentGuard measures the gap between what your product was designed to do and what the codebase actually does — before your investors measure it for you."
Your team ships with Cursor, Copilot, or Windsurf. Each AI session starts without context of your original product design. Each commit can silently drift further from the architecture you intended. By the time architectural drift becomes a production incident, it's expensive. IntentGuard detects it while it's still cheap to fix.
You need evidence-backed findings at file path and line number — the level of detail you need for sprint reviews, architecture decisions, and technical discussions with your team and your stakeholders. Not a dashboard summary. An audit trail.
Architectural drift creates AI-native vulnerabilities — code that passes linting and tests but silently violates the security assumptions in your original architecture. Standard scanners don't detect this. IntentGuard does. And every finding is auto-mapped to the relevant clause in your compliance framework — SOC 2, ISO 27001, GDPR, ISO 42001, EU AI Act — with file-level evidence included.
You've been shipping with Cursor or Copilot. The app works. But your AI assistant had no context of your product design. Every session started fresh. The code it shipped may pass all the tests — but does it match what you intended to build? Does it have vulnerabilities you haven't seen yet? IntentGuard tells you, before your users, investors, or production environment do.
"Your AI assistant has no memory of what you were building. Every session starts without context. IntentGuard is the contextual ground truth your codebase never had — the specification your AI forgot."
Manual controls testing takes days per framework. Producing a working paper with file-level evidence citations requires hours of manual cross-referencing between code reviews, scanning tools, and compliance checklists. IntentGuard maps every finding to the relevant ISO 27001, SOC 2, PCI DSS, HIPAA, and NIST CSF control — with file-level citations on every finding — in under one hour.
Connect with GitHub. Describe what your product does. Your first Intent Audit is ready in under one hour.
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