HAY Reviews

Cortex is the Engineering Operations Platform that helps engineering organizations improve operational maturity, reduce developer friction, and build a culture of excellence. Engineering teams use Cortex to get centralized visibility into their services, enforce production standards through automated Scorecards, and give developers golden paths to spin up new services in minutes — not days. With deep integrations across your existing toolchain, every team has the context they need to own and improve what they ship. The result: fewer incidents, faster recovery, and developers who can focus on building.

Planview is an AI-powered enterprise platform that connects strategy, planning, and execution across the entire digital product delivery lifecycle. It is designed to help organizations overcome challenges such as limited visibility, fragmented toolchains, and misaligned priorities. Planview links product strategy directly to development work, giving leaders real-time insight into progress, risks, and outcomes. The platform supports strategic portfolio management, product portfolio management, agile planning, and value stream management at enterprise scale. AI-driven insights help assess OKRs, identify dependencies, and detect delivery risks before they impact commitments. Planview enables product management teams to track business outcomes while engineering teams optimize delivery and productivity. Integration with tools like Jira, Azure DevOps, and ServiceNow creates a unified source of truth. The Office of the CTO benefits from enterprise-wide visibility, governance, and data-driven decision-making. Planview helps organizations balance innovation with maintenance and manage technical debt effectively. It replaces spreadsheets and disconnected reports with real-time, actionable insights. Designed for large, complex organizations, Planview supports speed, scale, and accountability. Overall, Planview empowers enterprises to deliver digital products that drive measurable business value.

Weave is a cutting-edge tool that utilizes machine learning to assess engineering output with remarkable precision, fully grasping the complexities inherent in this crucial field. Engineering managers often review performance, whether overtly or covertly, yet they frequently rely on subpar metrics like lines of code, pull request counts, or story points, which exhibit only weak correlations to genuine effort, typically around 0.3 to 0.35. These measures fail to serve as reliable indicators of productivity. To tackle this challenge, we have developed a tailored model that meticulously analyzes code and its impacts, yielding a far superior correlation of 0.94. Our innovative solution establishes a standardized metric for evaluating engineering output that discourages the emphasis on trivial accomplishments. Additionally, it facilitates a confidential comparison of your team's performance against that of others. This comprehensive strategy not only deepens understanding but also supports a more precise assessment of engineering success, ultimately driving improvements in productivity and collaboration within teams.