Skip to content Skip to footer
0 items - $0.00 0
Get Started
0 items - $0.00 0
Get Started
0 items - $0.00 0
KIMBODO

Slack Message Aggregation System

Close

Objective

The Slack Message Aggregation System was developed to collect and centralize conversations from hundreds of channels across multiple Slack workspaces. By leveraging Dialogflow, Google Cloud Functions, and Google Sheets, the system aggregates messages where the bot is present and transmits them to designated centralized channels within a corporate Slack workspace. This project was designed to improve visibility, compliance, and communication retention for teams supporting a large financial client.
Developed in collaboration with one of the largest security software companies in the world, the system was implemented for a top five financial institutions in the United States to streamline cross-workspace messaging and compliance tracking.

Slack Message Aggregation System

Problem Statement

Large enterprises with multiple Slack workspaces often face fragmented communication across teams, leading to:

  • Lack of visibility into key discussions happening in other workspaces.
  • Compliance risks due to lost or inaccessible conversations.
  • Inefficiencies in information retrieval, requiring manual switching between workspaces.
  • Limited retention and archiving capabilities for financial and security compliance purposes.

Given that financial and security industries require strict message retention policies, a scalable solution was needed to aggregate Slack messages while ensuring compliance, ease of access, and operational efficiency.

Solution Overview

Technologies Used

  • Dialogflow – Natural Language Processing (NLP) to process and manage bot interactions.
  • Google Cloud Functions (Node.js) – Serverless backend for processing and forwarding messages.
  • Google Sheets – Storage solution for retaining over 1 million messages per month for compliance and auditing purposes.
  • Slack API & Webhooks – Enabling real-time message aggregation across multiple Slack workspaces.
  • Google Cloud Storage (Optional Extension) – For long-term storage and scalable compliance archiving.

Key Features

  • Cross-Workspace Aggregation: The bot listens in multiple Slack workspaces and forwards messages to a centralized corporate workspace.
  • Automated Compliance & Retention: Stores messages for retention and auditing purposes in Google Sheets.
  • Visibility Enhancement: Enables non-members of specific Slack workspaces to monitor relevant discussions.
  • Real-time Processing: Cloud Functions ensure seamless and instant message transfer.
  • Scalability: Capable of handling 1M+ messages monthly with low latency.
  • Secure Storage & Access Control: Ensures compliance with financial security standards.

Use Cases Solved

  1. Enterprise Communication Unification: Aggregated messages into a single workspace to ensure key stakeholders were informed without needing access to multiple Slack instances.
  2. Compliance & Retention: Provided long-term storage and searchability of messages for audit and regulatory needs.
  3. Security & Incident Monitoring: Enabled a real-time feed of security-related messages for rapid incident response.
  4. Operational Efficiency: Reduced the need for employees to switch between multiple Slack workspaces, saving time and improving productivity.
  5. Scalable Logging & Analytics: Allowed leadership to analyze conversation trends and engagement across teams.

Success Criteria

  • Message Volume Handling: System reliably processes and stores 1M+ messages per month without data loss.
  • Latency & Performance: Messages are aggregated in real-time with minimal delay.
  • Compliance & Retention: Messages are stored securely with proper retention policies in place.
  • User Adoption & Visibility: Key stakeholders use the system to monitor cross-team discussions effectively.
  • Security & Access Control: The solution complies with corporate security and data governance standards.

Potential Future Enhancements

  • AI-Powered Message Tagging & Summarization: Using Vertex AI to categorize and summarize messages for easier consumption.
  • Advanced Search & Retrieval: Implementing a searchable UI for historical message lookup.
  • Integration with BigQuery: For deeper analytics and trend identification.
  • Multi-Cloud Storage: Extending compliance storage beyond Google Sheets to Cloud Storage or a Data Lake.
  • Enhanced Access Control: Role-based access and permission layers for secure message viewing.
  • Multi-Language Support: Expanding NLP capabilities for international teams.

Conclusion

The Slack Message Aggregation System successfully bridged communication silos, ensuring visibility and compliance for hundreds of employees. By leveraging Google Cloud technologies, the solution demonstrated high scalability, security, and efficiency in managing enterprise conversations. Future enhancements will focus on AI-driven insights, deeper analytics, and improved access control, making this a foundational tool for large-scale enterprises managing multi-workspace communication.