PMbot Case Study: AI-Powered Project Reporting

Executive Summary

Delivery leaders rely on time-tracking data to understand utilization, cost efficiency, and project health. However, accessing meaningful insights from tools like Clockify often requires manual exports, spreadsheet manipulation, and repeated follow-ups with analysts or project managers. Auxiliobits designed and implemented PMbot—an AI-powered project management bot integrated with Slack that enables teams to ask natural language questions and instantly receive accurate, summarized insights from Clockify data. By integrating PMbot (via Slack App), Clockify, Google Sheets, and Azure-hosted GPT models, the solution eliminated manual reporting overhead and enabled on-demand operational intelligence—without changing existing tools or workflows.

Business Challenges

Despite having Clockify in place, the organization faced persistent reporting friction:

Time-tracking data was available but underutilised.
Reports required manual exports and cleanup
Managers depended on analysts for ad-hoc insights
Non-technical users struggled to frame complex reports
Reporting cycles were slow, leading to delayed decisions
Teams had to switch tools instead of working inside Slack

The need was clear: Make time-tracking insights conversational, instant, and reliable—inside Slack.

Solution Overview

Auxiliobits built PMbot, an AI-driven project management bot connected to Slack via a secure Slack app that converts natural language questions into real-time analytics on Clockify data.

Users can ask questions such as:

The agent automatically:

Fetches Clockify data for the last 4 months
Stores and maintains it in Google Sheets
Translates user queries into executable analytics logic
Executes Python and pandas scripts dynamically
Summarizes results and posts them back to Slack

The process completes in 5-10 seconds, requiring no manual intervention.

Solution Architecture

Core Components

PMbot (Slack App Integration)

PMbot is deployed as a Slack App that receives user queries from Slack channels and delivers summarized insights back to users.

Clockify API Integration

Fetches detailed time-entry data with pagination support to ensure completeness.

Google Sheets Data Layer

Acts as a persistent, refreshable cache for historical data.

AI Processing Layer (Azure OpenAI)

Python Analytics Runtime

Executes dynamically generated pandas logic securely.

Keep-Alive & Monitoring Layer

This layer prevents deployment sleep and ensures high availability on the render.

Functional Capabilities

Conversational Analytics via PMbot

Intelligent Time-Tracking Insights

Slack-Native Experience

Admin Controls & Reliability

Security & Compliance

The PMbot solution was built following enterprise-grade security standards:

Deployment & Operations

The system runs with minimal operational overhead and high reliability.

Business Impact

Measurable Outcomes

70–80% reduction in manual reporting effort

Near real-time visibility into utilization and costs

Faster decision-making for managers and leadership

Reduced dependency on analysts for ad-hoc reports

Strategic Benefits

Higher adoption due to Slack-native experience

Improved transparency across projects and teams

Better utilization and profitability tracking

Increased trust in reporting accuracy

Representative Use Cases

All delivered through conversational interactions with PMbot in Slack

Future Enhancements

The PMbot architecture supports seamless expansion, including:

Why Auxiliobits

Auxiliobits specializes in AI-first automation that enhances existing enterprise systems rather than replacing them. This engagement demonstrates our ability to:

By using agentic AI, automation engineering, and knowledge of business processes, Auxiliobits helps companies move from simple reporting to smart, self-managing operations