Last week we released a new iteration of the Qlerify tool that takes us one big step closer to our vision: To combine AI with domain expertise to draw out any imaginable process out there into comprehensive flowcharts. Modify them together and generate back and front end code with a click to build out any imaginable piece of software.
Qlerify is a process modelling tool that now includes a powerful AI copilot that allows users to generate detailed process and data models and enrich them with software requirements. All in a matter of minutes!
Users can jumpstart brainstorming and modeling meetings and save tons of time. Using a template relevant to your context is much more efficient than starting from a blank canvas. Our AI copilot provides a structure and a starting point for the discussion and aligns the discussion to the problem you are trying to solve. The procedure is described below.
Access the AI copilot by clicking on the button below (you must create a project and a workflow first).
Now you can generate a process or user journey according to your current needs. Please read the instructions first (click on the question mark).
Our AI copilot can help organisations generate enriched process maps for many different use cases including generation of the following information for each process step:
- A headline describing the step
- Actors involved
- Example input form data
- Example KPIs
- Common problems
- Potential improvements
- Example user stories
- Example business rules
- Average lead time
- … and much more data that can be fully configured by the user
Another benefit with our new AI assistant is the possibility to improve existing process models. You can use AI to add to existing process maps, such as additional process steps (events) relevant to your context:
You can also further enrich existing process steps with more information such as systems, KPIs, user stories, business rules, lead time, etc. Ask the AI assistant to generate more ideas with one click:
Another possibility to generate more ideas is to ask an open question to the AI assistant related to a specific card, such as:
- Can you provide 3 examples of acceptance criteria to this user story?
- Are there any more cost-efficient alternatives to Photoshop?
- Is it possible to shorten the suggested lead time and if so, how can this be done?
The AI assistant will respond with a new comment:
Once your model is detailed enough for the first development sprint, you can prioritise your content in a collaborative way:
- Let the team members vote on cards with thumbs up/down
- Let the team members add comments to the cards in the backlog
- Let the team members assign a priority to each card in the backlog (must have, should have, could have, won’t have)
- Drag cards up or down to the desired position in the backlog
- Apply user story mapping to quickly plan your iterations/releases
Content created in the Qlerify tool can easily be transferred to Jira Cloud through API integration so developers can quickly start coding. In order to do this, you need to install the Qlerify app from the Atlassian Marketplace: https://marketplace.atlassian.com/apps/1229550/qlerify?hosting=cloud&tab=overview
You can find installation instructions here: https://www.qlerify.com/jira.
Our next big upcoming milestone is to start generating source code from the models created in Qlerify. This will allow organisations to potentially save thousands of man hours in development projects and complete development projects in a few weeks.
Book a free demo with us here: https://meetings-eu1.hubspot.com/nvarzakakos
Sign up and try for free here: https://app.qlerify.com/signup
Subscribe to our newsletter here: https://qlerify-26849244.hs-sites-eu1.com/subscriptionlist
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OpenAI has produced an updated version of Codex, their API that can autocomplete code based on a natural language input.
Github Copilot is an editor extension that sends your comments and code to Copilot when then uses the OpenAI Codex API to suggest individual lines of code, or even whole functions.
