About Hourly

When Hourly was launched publicly
Machine Learning
We think time tracking lends itself to AI
We're a Customer Too
We use Hourly in our consulting business
9 Great People
in our team working on Hourly
Self Funded
We're proudly 100% self funded to date
Brisbane, Australia
Where Hourly is designed and developed
Our Mission
"To bring great design and advanced technology to software that helps services companies run better, happier, more profitable businesses"
The Team
Why We Built Hourly
To improve cashflow forecasts
Project plans and sales forecasts are popular but unreliable tools for predicting future cashflow. They’re based on best guesses, subject to constant change and hard to keep up-to-date.

We think a better way is use technology like machine learning to generate a more realistic picture of the future.
To provide more certainty about timesheet accuracy and completeness
Missing, forgotten or incomplete time entries mean lost revenue and/or trust with clients.

We’ve built features into Hourly that aim to make it easy to spot and correct issues before people have forgotten what they’ve worked on and before invoices go out to clients.
To motivate people
A lot of timesheet tools are all data entry and not much feedback.

We’ve found that giving users real time feedback to show them how they’re tracking is a great motivator. We’ve integrated actionable data right into users’ time sheets - much of it visual and chart based for ‘at a glance’ visibility.
To remove friction and minimise overhead
The time tracking and invoicing lifecycle can be a clunky and cumbersome process.

We’ve tried to make Hourly as fast and easy to use as possible with features like 100% inline editable time sheets (no dialogs and pop ups to click through) and draft invoices and timesheets that are created automatically and kept up-to-date in real time.
To uncover new insights
There’s a wealth of information in time tracking data that can help us understand more about how we work and where we can improve.

Our long term goals with Hourly are to explore the area of prescriptive analytics and whether it’s possible to do things like predict when people might be at risk of burn-out, stress or sickness.