CAST sharpens software intelligence to unblock modernization, AI, and green code

Most CIOs have dashboards for runtime, cyber, and FinOps, but not for the one asset defining their competitiveness: the custom software they build and heavily customize. That gap forces leaders to rely on subjective updates, misprioritize modernization efforts, and carry opaque risk. CAST’s pitch is simple: replace opinion with facts directly from the code, then prioritize action. This shift is essential for delivery, cost, and resilience in a world with orders-of-magnitude more code (and AI now accelerating output).

Integration is CAST’s advantage: portfolio facts for leaders, deep dives for doers

CAST separates concerns neatly.

CAST Highlight delivers fast, portfolio-level intelligence for CIOs/CTOs and chief architects, such as code-health, cloud readiness, open-source risk, and ‘green impact’. These scores are normalized on a scale of 0–100, with role-specific views, recommended remediation, and benchmarking available in highly customizable dashboards.

CAST Imaging then maps the internals of a single complex system (data flows, dependencies, transaction paths) for architectural change. Scans run locally against 50+ languages/technologies; source code stays put; results are uploaded as statistics enabling hundreds of apps to be assessed in days, not months.

This two-tier model gives leaders a truthful top-down view while enabling bottom-up change when needed.

Modernization is critical to bolstering the foundation for building an AI-enabled enterprise

Legacy modernization has been a permanent fixture on CIO agendas for decades, but it is often sidelined as organizations chase the shiniest new tech in the market. However, we are at a pivotal juncture when multiple factors simultaneously create an impetus for modernizing legacy code and architectures.

• AI adoption has raised the bar. Boards are pressing for AI at scale, but legacy architectures, mountains of technical debt, and brittle integrations act as hurdles blocking the path to scaled AI adoption. Without evidence, leaders either over-scope (boil the ocean) or under-scope (paper over risks). CAST’s analysis identifies where AI would break, what to fix first, and which AI services/LLMs fit each app, turning board pressure into an actionable roadmap.
• Cloud modernization is stalling on latent blockers. Hidden patterns in hard-coded IPs, file persistence, and framework obsolescence derail cloud-native ambitions and inflate migration budgets. CAST Highlight flags these patterns portfolio-wide and recommends rehost/refactor/re-architect paths with estimated effort, cutting the time leaders spend hunting root causes and debating options.
• Open-source risk and IP exposure are creeping up. Enterprise portfolios routinely run software components whose versions are years (even decades) out of date, exposing security and licensing risk. CAST’s composition analysis surfaces this at the business-decision level rather than as developer noise, making it easier to identify modernization sequencing and board reporting.
• Efficiency and sustainability now hit the profit and loss (P&L). The Green Impact module of CAST Highlight detects energy-wasting code paths and quantifies potential savings using the Green Software Foundation’s Software Carbon Intensity method by linking code changes directly to lower energy cost and CO₂ emissions. That reframes ‘green’ as an operational lever, not a side quest.

CAST is addressing the embedded risk that slows agility and innovation

Higher operational costs, transformation delays, inability to scale systems, and integrate modern technology are some of the major challenges that enterprises face when dealing with outdated technology (see Exhibit 1)

Exbibit 1: Outdated tech leads to higher cost, delayed transformation, and inability to scale

Sample: N=118
Source: HFS Research, 2025

• Leaders can’t see ‘good’ vs. ‘bad’ debt, so remediation is often misallocated. Some debt is intentional and harmless in the short run, while others fuel the drag that slow releases, cause outages, and block AI/cloud moves. CAST’s Technical Debt Advisor separates the two by business criticality, debt density, technology obsolescence, and OSS age so scarce, engineering cycles flow to the few systems where payback is highest. Without this, organizations keep ‘fixing what squeaks,’ not what matters.
• Subjective reporting breeds false confidence. Many CIOs still infer the state of custom software from slideware and status calls. CAST replaces that with evidence from static analyses, giving executives a defensible basis to justify budgets, sequence programs, and report to CEOs/boards on measurable progress. In the current climate of cost scrutiny and risk aversion, that credibility is as valuable as the fixes themselves.
• Cloud programs overrun because blockers emerge late. Late discovery of non-portable patterns forces expensive replans. CAST moves discovery to the front of the funnel. It connects to remediation toolchains (e.g., AWS Transform pipelines that apply automated changes and re-validate with CAST Highlight), shortening feedback loops and reducing hand-offs that typically add months.
• Security/IP exposures hide in plain sight. Portfolio-level OSS visibility is still rare; component age and license issues accumulate across thousands of apps. CAST makes the scope and age profile visible to executives, enabling risk-informed deprecation and modernization waves rather than whack-a-mole patching.
• Inefficient code inflates cost and emissions. Hot loops with embedded queries, avoidable data access, and similar patterns scale linearly with traffic. This means that waste compounds as the codebase grows. CAST quantifies the energy/CO₂ upside and points to specific hotspots developers can change, turning sustainability into a budget-backed engineering outcome.

How CAST does it (and where it’s going)

By providing facts, CAST allows enterprises to focus on surgical fixes. Portfolio dashboards highlight ‘“top-priority’ apps (high business impact + high debt density), obsolete tech (e.g., old  .NET/Java/Spring), and outdated OSS by version year, giving executives a credible, narrow first wave. For a selected app, CAST Highlight pinpoints specific code patterns and files; CAST Imaging provides the system-wide map to change safely. CAST itself does not modify code, but its outputs can feed AI coding assistants and partner toolchains; CAST is adding MCP servers to automate that hand-off in the near term. That matters when portfolios number in the thousands. Future developments also include new insight areas that will recommend AI services/LLMs based on app characteristics, easing the ‘where do we start with AI?’ dilemma and reducing dependence on broad, time-consuming consulting assessments.

That said, CAST stops at factual diagnosis and does not offer remediation tools; enterprises still need engineering cycles (internal, SI, or automated pipelines) to execute. CAST does provide a priority list that can be used to plan wave-based remediation efforts. CAST Imaging’s deeper analysis runs separately and takes longer than CAST Highlight, as it uses two different engines. Providing an ability to upgrade functionalities easily would be a worthy investment in future product roadmaps.

The Bottom Line: Building an adaptive enterprise requires modernization, AI adoption, and greener operations, and initiatives need facts, not faith.

CAST’s software intelligence replaces subjective reporting with code-level evidence at portfolio scale, prioritizing the few apps where action unlocks the most value. In an environment of surging code volume and mounting pressure to ‘do AI,’ this shift separates momentum from gridlock.