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OTR: Better On The Record

From April 6-7, I participated and won second place in the Redline Hackathon with a team of software engineers and data scientists. We were tasked to build an innovative information management solution using the Yuuvis cloud.


We built OTR, Better on the Record. Our product is a content management system that uses machine learning and natural language processing algorithms to make conversations of contractual nature findable from Slack, that would otherwise get lost in a lengthy chat log.


Corporate lawyers and managers are often frustrated by employees making informal contractual agreements over private chat, agreements that are enforceable in writing and can have negative impact on an organization in terms of time, budget, and legal implications.

We believe we will make communication across a corporate office more transparent by building a CMS that can filter chat logs for contractual agreements.


Eliana Teran  UX/UI Designer

Sylvan O'Sullivan Data Scientist

Alexander Gaiser  Software Engineer

Sam Freedgood  Software Engineer

Eric Grun Software Engineer

My responsibilities: 

Research, Hi-fi Wireframes,

Developer Hand-off, Presentation

Research Goals

  • Discover who the primary user of Yuuvis's Enterprise Content Management System is.

  • Work with the Data Scientist to discover what kind of data can be pulled from Yuuvis's API and what platform will make this data useful. 

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Research Methodology

  • User Interviews with Mentors

  • Data Analysis

  • Archetype 

  • User Flow

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Problem Space

Corporate lawyers and managers often find out about informal agreements after being contacted by an unhappy client or receive an unexpected bill. In order to dispute any claim or fees properly, they need written proof, which is not always findable if a contract is made informally by an employee on slack. 

How might we increase transparency of employee conversations on Slack without further impinging on their privacy?


Internal Research

  • Slack chat logs are already available to team administrators via downloadable CSV's called Compliance Exports. We will not need to further ammend to employee privacy agreements.

  • Our engineering team found that we can search large data sets for keywords to tag conversations and classify content of legal and contractual nature using Query functionality.

  • Yuuvis currently has a mid fidelity CMS system that needs functionality to make data useful. 




Why Slack?

To narrow our scope and apply our technology to the most relevant chat log possible, we utilized Slack, a professional communication and collaboration platform. The program has over fifty thousand corporate teams paying for premium, with 10 million daily active users, who spend an average of 2 hours actively on it every day. That's a lot of room for conversations to get lost in private chats and agreements to be made between clients of a contractual nature. 

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What do we want to build?

A CMS where a manager, or as slack calls it a "Team Owner," can have chat logs filtered for conversations of contractual nature for better transparency in the office place. 

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What is our product?

  • OTR, Better On The Record, makes contractual conversations in Chat Logs findable.

  • It is a responsive platform with a simple search interface conducive for fast  information retrieval for Office Managers and Corporate Lawyers.

  • Based on conversation metadata. OTR utilizes machine learning and natural language processing algorithms to classify conversations based on the legal    nature of the content.

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Landing Page

  • In our scenario the office manager and corporate lawyer look over a number of teams within a department, to be managed on the left.

  • On the dashboard, the users can switch between teams, search within previously uploaded chat logs, and download new files to be filtered. 

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Upload a New Chat Log

  • Organized by team, the user can upload a new chat log to be filtered by OTR. 

  • OTR is fully integrated with yuuvis’s API using Query functionality and can classify conversations using machine intelligence algorithms. 

  • OTR searches large data sets for keywords and tags conversations for content of a contractual nature, which will then be provided to the user as files. 

View a Chat File

  • Users can read a filtered file, with a view of it's relevant metadata.

  • Users can download, archive, edit, and delete files as seen fit.

Next Steps for MVP 

  • Build out the mobile responsive website and conduct usability testing.

  • Create a custom schema that allows OTR to store a high number of machine intelligence derived keywords.

  • Create a Slack bot that will automatically archive and indexSlack conversations, and so user doesn't have to manually upload CSV's.  

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