Cubyts

The Core Challenges

📂
Process Drifts

Delivery pressure led to process drifts and feature deviations going unnoticed.

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Inconsistent Standards

Difficulty in tracking code standards consistently across all teams.

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Feature Drifts

Requirements and build plans consistently diverged from agreed benchmarks during sprints

AI-Powered Observability Panel - Dark Mode

The Solution: An AI-Powered SDLC Observability Layer

Cubyts was deployed as an observability tool to provide a "mirror" reflecting the true state of their software delivery lifecycle.

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On-Prem Gitlab & Jira

Existing Infrastructure

Cubyts Layer

Live Observability & Insights

  • Continuous Monitoring: Seamlessly connected to Git repos and delivery pipelines.
  • Real-time Flagging: Surfaced deviations in process, features, and code standards.
  • Performance Dashboards: Provided reports on team and system-level trends
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Improved Delivery

Data-Driven Decisions

The Impact: Measurable Results in 90 Days

The adoption of Cubyts delivered significant, quantifiable improvements in efficiency, alignment, and cost savings almost immediately.

Feature Drifts Detected

400+

within the first 3 months

Feature Drifts Resolved

200+

150+ auto resolved

Person-Hours Saved

1600+

in discovery & resolution efforts

Key Takeaways for IT Leaders

This case highlights critical lessons for managing large-scale, SoW-based projects in today's fast-paced environment.

Early Detection is Key

In high-scale IT services, identifying feature drifts early directly reduces rework, minimizes costs, and mitigates contract risks.

Automation Accelerates Recovery

Auto-resolution capabilities significantly accelerate recovery from deviations, saving measurable time and freeing up development teams.

Visibility Builds Confidence

Real-time dashboards give leadership confidence in project alignment and progress without disrupting team workflows.