[ENT] AI, BACKEND, FRONTEND

Automating Weekly Hours Review & Anomaly Detection with AI

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The Challenge

A confidential client required a system to automatically validate and analyze weekly working hours submitted by employees. The existing manual review process involved downloading spreadsheets, cross-checking entries, and manually flagging issues — a slow, error-prone, and resource-heavy workflow.

Key challenges included:

  • Inconsistent Detection – Overbooked, underbooked, or invalid entries were sometimes overlooked.
  • Limited Insights – Managers only had raw data, without summaries or actionable recommendations.
  • Lack of Automation – The process relied entirely on manual checks, consuming hours each week.

The goal was to deliver an automated anomaly detection and reporting system that could integrate with existing time-tracking tools, apply custom rules, and present results in a clear, management-friendly format.

The Solution

Megatron Solutions delivered a web-based dashboard with integrated AI-powered anomaly detection and LLM-generated summaries.

Core Features Delivered:

  • Multi-Source Data Parsing – Extracted time logs from APIs (Harvest, Clockify, JIRA), Excel files, and CSV exports.
  • Customizable Business Rules – Detected anomalies such as:
    • Overbooked hours (e.g., > 60 hours/week)
    • Missing weekday entries
    • Invalid task/project codes
    • Zero-hour weeks
    • Department/role-specific thresholds

  • AI-Powered Analysis – Used NLP to identify unusual patterns and generate plain-language explanations for each anomaly.
  • Management Dashboard – Provided real-time visualizations, downloadable Excel/PDF reports, and an audit trail.

  • Automation & Scheduling – Allowed on-demand checks or scheduled weekly reviews, with optional auto-emailed reports.

Technical Approach:

  • Backend logic for parsing, normalization, and rule execution.
  • AI integration for anomaly detection and summary generation.
  • Responsive web UI for managers and admins to review flagged issues and access historical data.

Team Composition:

Backend Developers, Frontend Developers, AI Engineers, QA Engineers, and a Business Analyst.

The Impact

The implemented system delivered:

  • 70% Time Savings – Reduced manual review efforts from hours to minutes.
  • Higher Accuracy – Customizable rules ensured all relevant anomalies were flagged consistently.
  • Better Decision-Making – Natural language summaries gave managers clear, actionable insights.
  • Scalable Operations – Capable of processing large datasets across multiple departments and geographies.

Reference

Client details are confidential