An Example
Last updated
Last updated
A local electrical energy distributor needs to collect the usage readings from all of its customers. Every month, they receive a batch of index cards in which every customer fills in the electrical energy meter readings:
The energy distributor has a legal obligation to scan and store all of the index cards for at least 5 years. Furthermore, at the end of each month, the employees need to read the energy consumption readings from every index card and save it in the company’s database.
As more and more customers join in, the more index cards they need to manually process each month. This implies hiring people to do mind-numbing, tedious tasks, day-in, day-out. As time passes by, mistakes add up, whether willing or unwilling, and retention of staff is at all time lows.
What options do they have?
They can start developing their own software product so that customers can fill in the information electronically! However, they are not a software company and don’t have the budget to build their own tool. The legal ramifications pile up since they have a very strict national data retention policy. This solution is going nowhere fast.
Instead, they hire a data scientist, called Jane, to take a look over their data. The monthly batch of thousands of index cards is delivered to her and she quickly realises that she needs an army of people to start reading the cards and extracting the numbers out of them, and she needs to do this fast. Time waits for no one!
She submits all of the index cards into Timeworx.io and requests for the numbers to be extracted from each scanned image.
Meanwhile, across the world, hundreds of people are waiting in lines, stuck in traffic, or just simply mindlessly scrolling through their social media. A notification pops-up “You’ve got new tasks waiting for you in Timeworx.io!”. They open the app, they read the numbers on the index cards and they transcribe them directly in the app. It took them just a few seconds to do it and they also got rewarded in TIX for it.
A few days later, Jane checks on the data processing to see how it’s going and to her surprise, the data has been delivered on time. She quickly imports it in the company’s database and she’s done!
With a resounding success, Jane repeats the same process for one month, two months and then three. At the end of each month, she gets the job done quickly and painlessly. As time goes by she’s starting to gather more and more processed data, so she uses it to train a machine learning model that extracts the numbers out of the cards automatically. She collects all of the historical data from the company’s archives and adds it into the mix and soon enough, she’s done it! Now the energy distributor needs to scan all of the index cards it receives at the end of each month, pass them through the new and shiny AI, and the data magically gets inserted into the company’s database.
The story doesn’t end here, since Jane notices that this need is very common among local energy distributors. The company still does not have the funds to develop their own software product to sell this to the rest of the market. But the good news is that they don’t have to!
Jane packages the automated index card reader as an AI agent and quickly integrates it into the Timeworx.io platform. Now, all of the other distributors can process their index cards automatically and directly from Timeworx.io, and the company is compensated for every piece of data that their AI agent processes. Simple made easy.
This is a clear example of how Timeworx.io is designed to harness the power of the virtuous cycle between data processing and AI. In Timeworx.io, data processing is not a linear process, rather it is a loop that promotes innovation on every execution, based on the Lean principle of “build, measure and learn”:
The purpose of this virtuous cycle is to drive innovation into the market by transforming manual data processing into automated data processing. With every revolution of this virtuous cycle, human ingenuity is transferred into an AI that can now relieve us from boring tasks with speed and accuracy.
Through its data processing protocol, Timeworx.io gathers all of the actors in the data value chain under the same decentralised roof, governed by the TIX token which ensures that together we create a space for everyone to contribute to the future of AI while being fairly compensated for it.
Fast Track
