Portfolio governance with facts
CAST Highlight scores technical debt, cloud maturity, open source risk and agentic readiness across your entire portfolio, so priorities are backed by empirical data.
CAST performs a deterministic MRI of your code, turning source files into maps, dashboards and facts. Executives, engineers and AI agents finally work from the same verified truth.
Every annual subscription includes complementary concierge services with dedicated specialists.
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Why change
Most organizations steer billion-dollar software portfolios with spreadsheets, tribal knowledge and guesswork. AI coding agents make it worse when they guess too.
The old way
The CAST way
Getting started
Start fast and see the difference within weeks.
CAST plugs directly into GitHub, BitBucket and Azure DevOps, or runs on your own codebase, on-premises or as SaaS.
Semantic analysis maps every object, dependency, transaction, data flow and cross-application relationship across 450+ technologies.
Dashboards, call graphs and architectural maps drive tech debt priorities, cloud plans, integrity gates and AI agent context.
Choose a plan
CAST Highlight subscriptions are tiered by portfolio size and include the number of developers at no extra cost. Each portfolio requires its own subscription.
From $20,000/year25 applications; scales to $240,000/year for 1000 applications
Portfolio-level intelligence in three editions: Complete Insights, Cloud Insights and SCA Insights.
Private offerVolume discounts available
Interactive architectural blueprints of individual applications for modernization and ADM productivity.
FreeFree download
Generate and manage software bills of materials for your applications.
Good to know
AI + CAST combines AI coding agents with deterministic software intelligence. CAST maps the architecture, dependencies, data access, call paths and structural risks inside an application, then provides that context to AI agents so they can understand and transform complex software more accurately, efficiently and safely.
AI coding agents are effective at generating code, but they struggle to understand large, complex applications from source files alone. Important relationships may span multiple technologies, databases, repositories and applications. CAST gives the agent a factual map of these relationships, reducing blind spots, incorrect assumptions and unintended downstream impacts.
It means the information is derived through semantic analysis of the actual software rather than inferred or generated probabilistically by an LLM. CAST identifies the application's real objects, dependencies, transactions, data flows and architectural relationships, so the AI agent can ground its work in verified facts.
Application inventories and statistics, internal architectural graphs, cross-application dependencies, transactions, data-access paths, call graphs, object details, source-code locations, and direct and indirect dependencies. The agent gets both a high-level view of the system and the detailed context needed for specific changes.
Understanding unfamiliar applications, extracting business rules, change-impact analysis, generating documentation and tests, remediating technical debt, removing cloud blockers, identifying microservice candidates, replacing frameworks or databases, and transforming legacy applications. The common thread is giving AI the context to make complex changes with less rework and risk.
No. CAST complements the AI tools your organization has already selected. It supplies deterministic application context to MCP-compatible agents and development tools, improving your existing AI-enabled workflows without introducing another tool to rip out.
Take the next step
Book a call and see how CAST maps your software, measures your debt and gets your AI agents working from verified truth.
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