AI-Powered Timesheet Verification: How It Works

When we added AI-powered timesheet verification to BetterFlow in 2024, our goal at BetterQA wasn't to catch people lying about their hours. It was to catch honest mistakes before they became problems: forgotten entries, miscategorized time, hours logged to the wrong project, patterns suggesting burnout or capacity issues.

The technical implementation of this feature reveals interesting challenges about applying AI to structured business data versus the more common use case of processing natural language.

The Problem AI Needs to Solve

Timesheet errors fall into several categories, each requiring different detection approaches:

Omission errors: Missing entries for days that should have working hours. Someone worked Tuesday but has no timesheet entry for Tuesday.

Categorization errors: Time logged to the wrong project, client, or billing category. 4 hours of client work accidentally marked as internal overhead.

Pattern anomalies: Unusual patterns that might indicate problems even if individual entries are technically correct. Someone who normally works 40 hours suddenly logging 65 hours.

Data quality issues: Vague descriptions, inconsistent formatting, missing required details that will cause problems downstream in billing or reporting.

Traditional rule-based systems can catch some of these but struggle with context-dependent issues. Is 12 hours in one day an error or legitimate crunch time? Rules can't capture this nuance.

Why Large Language Models Work for This

We use OpenRouter's API to access multiple language models (primarily Anthropic's Claude and OpenAI's GPT-4) for timesheet analysis. LLMs excel at pattern recognition and contextual understanding, which is exactly what timesheet verification needs.

The model receives structured data about timesheet entries along with context: the employee's role, typical work patterns, project requirements, billing rules, and company policies. It analyzes this holistically rather than applying rigid rules.

For example, examining this entry: "Fixed bugs - 3 hours, Project ABC"

A rule-based system sees: description present, project assigned, duration within normal range. Approved.

An LLM sees: description is vague (which bugs? what was the impact?), Project ABC is a client project requiring detailed descriptions for billing, this employee normally provides more detail. Flag for review.

The Analysis Workflow

When a user requests AI verification of their timesheet, the system follows this workflow:

Step 1: Data Collection - Gather the current week's timesheet entries, the previous 4 weeks for pattern comparison, employee metadata, and company policies.

Step 2: Prompt Construction - Build a structured prompt that provides the AI with entry data, historical context, verification criteria, and examples.

Step 3: Model Inference - Send the prompt to OpenRouter API, which routes it to the specified model.

Step 4: Response Parsing - Parse the model's JSON response containing flagged entries, confidence scores, suggested corrections.

Step 5: Human Review - Present findings to the user or manager with context. AI flags issues but humans make final decisions.

Handling False Positives

Early versions of our AI verification flagged too many legitimate entries as problematic. An engineer logging 10 hours in one day would get flagged for "unusual hours" even when it was legitimate crunch time before a release.

We added several layers to reduce false positives:

• Confidence scoring: The model provides a confidence score (0-100) for each flagged issue. We only surface high-confidence flags (>70) automatically.
• Historical context: If someone frequently works long days, the pattern isn't flagged as unusual.
• Project-specific rules: Certain projects have different requirements.
• User feedback loop: When users mark a flag as incorrect, we log that feedback and use it to improve prompts.

Current false positive rate is around 8%, meaning 92% of flagged issues are actual problems users correct after review.

Privacy and Data Handling

Sending timesheet data to third-party AI providers raises privacy concerns. Our approach:

• Data minimization: We send only what's needed for analysis.
• No PII in prompts: Employee names are replaced with anonymous IDs.
• No data retention: We use OpenRouter's zero-retention mode.
• Opt-in: Companies can disable AI features entirely if they have policies against third-party data sharing.

Real-World Impact Metrics

After 12 months of production use, we've measured the impact of AI verification:

• 23% reduction in timesheet rejection rate
• 31% increase in description detail quality
• 18% improvement in project categorization accuracy
• 40% faster manager approval time

Conclusion

AI-powered timesheet verification demonstrates practical application of large language models to structured business data. The value comes not from perfect accuracy but from catching common errors automatically and helping users learn what good entries look like.

Combined with human judgment, AI verification improves data quality while reducing administrative burden.