Sightglassv0.1.0

A financial services company uses AI agents to migrate legacy systems to modern frameworks. Sightglass reveals that 80% of dependencies chosen are based on TRAINING_RECALL from outdated training data, exposing the migration to security vulnerabilities and technical debt from unmaintained packages.

A startup uses AI coding agents to quickly build a prototype. Sightglass identifies that the agent selected bloated dependencies through CONTEXT_INHERITANCE from boilerplate templates, allowing developers to swap them for lighter alternatives before scaling.

A large e-commerce platform integrates AI agents into CI/CD pipelines for automated dependency updates. Sightglass flags packages added via REACTIVE_SEARCH as potential technical debt, enabling teams to enforce policies requiring PROACTIVE_SEARCH for production changes.

A coding bootcamp uses AI assistants to help students with projects. Sightglass shows high TRAINING_RECALL percentages, revealing that students are receiving biased package recommendations instead of learning proper dependency evaluation techniques.

Maintainers of popular open source libraries use AI agents to handle pull requests and issues. Sightglass surfaces when agents suggest dependencies based on popularity rather than compatibility, preventing breaking changes in community projects.

Offer tiered subscriptions on sightglass.dev for cloud analysis, historical session storage, and team collaboration features. Enterprise tiers include advanced risk detection, compliance reporting, and dedicated support.

Sell perpetual licenses for on-premises deployment in regulated industries like finance and healthcare. Includes custom integration support, private cloud sync, and enhanced security features for sensitive codebases.

Integrate Sightglass analysis into popular IDEs and development platforms as a premium add-on. Partner with coding agent providers to offer bundled packages and capture users at the point of agent interaction.