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Technologies Involved:
PYTHON
Area Of Work: Machine Learning
Project Description

A remote-first startup dedicated to workplace engagement approached Oodles for a privacy-first check-in solution within Slack. The client sought a scale-ready MVP that allowed employees to share responses privately through DM prompts while generating a single, identical weekly summary for the entire company. Oodles developed a Slack-native application that combined simplicity, transparency, and scalability.
Scope Of Work

The project addressed the challenge of fostering open communication while protecting individual privacy. The client sought Oodles for a Slack-integrated tool to conduct weekly check-ins, publish uniform summaries, and provide clear “who can see what” transparency. Oodles designed a solution encompassing DM workflows, company-wide reporting, data minimization, and deployment setup, ensuring trust-driven engagement and long-term adaptability.
Our Solution

To meet the client’s requirements, Oodles built a privacy-first MVP using Slack’s API and a modular backend architecture. The tool was designed to minimize stored data, enforce strict access controls, and present audit logs for user-visible actions. A flexible structure enabled the solution to scale into new channels or integrate optional AI features in the future without major rework.

Key Features Implemented

  • Private DM flows with multiple-choice and optional text inputs
  • Weekly identical company-wide summary posted to a single Slack channel
  • Plain-English privacy view clarifying visibility rules
  • Data minimization with configurable retention policies
  • User-visible audit logs for accountability
  • Deployment-ready setup with editable prompts and channel configurations

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