Background
Founded in 2016 by Megan Rafuse and Jordan Axani, Shift Collab began as a small clinic with a refreshing approach to therapy. The practice gained a reputation for its warm, personal touch, with clients appreciating the deep connection they felt with their therapists.
Challenge
As Shift Collab expanded, the number of inquiries grew quickly. Manually matching them with appropriate therapists was time-consuming and added stress. It sapped the energy they preferred putting into building meaningful connections with new clients.
Solution
Discovering Convert_ gave Shift Collab the opportunity to automate critical processes while preserving their values of personal connection and client-first service.
Background
Shift Collab was founded in 2016 by Megan Rafuse and Jordan Axani with a clear mission. They aimed to make therapy more human, accessible, and personal.
Starting as a small clinic in downtown Toronto, the practice quickly built a reputation for its warm, relationship-driven approach to care.
Shift Collab evolved into a virtual practice serving clients across Canada as demand grew. Despite scaling, their core philosophy remained unchanged.
They weren’t interested in becoming a high-volume, transactional therapy platform. They wanted every client to feel seen, understood, and matched with the right therapist.
The Challenge: Scaling Without Losing Personalisation
Growth introduced a new challenge. As inquiries increased, so did the complexity of matching clients to therapists.
Each match required evaluating:
Therapist specialties and modalities
Identity and lived experience
Availability
Personality fit
Client needs and preferences
Over 100 different data points in total influenced each match.
At first, the Shift Collab team manually handled this process. But as volume increased, it became unsustainable.
Shift Collab co-founder Jordan explained:
We were manually matching every client with three suitable therapists to choose from. It was time-consuming and stressful.
At the same time, competition in the online therapy space was intensifying. Larger, well-funded platforms were investing heavily in acquisition.
Shift Collab needed a system that could:
Scale intake without overwhelming the team
Maintain high-quality therapist matching
Adapt to constantly changing data
Stay aligned with their client-first values
Shift Collab had already attempted to scale through advertising.
But without the right system in place, growth created more strain instead of progress.
We scaled the wrong way before, basically throwing a ton of advertising money at a system that wasn't properly built.
Jordan realised the bottleneck wasn’t demand. It was the intake and matching process.
They needed a smarter, structured system to collect, process, and act on client data. Crucially, Shift Collab had to keep its warm, relationship-driven approach to care within that system.
The Solution: A Custom Matching System Built with Convert_
After testing several tools, the team found that most platforms couldn’t handle their level of complexity.
Typeform lacked the calculation and scoring capabilities.
Outgrow couldn’t support advanced filtering and ranking.
They needed a system that could:
Score clients based on multiple variables
Filter therapists dynamically
Return the top matches instantly
Update continuously as data changed
That’s where Convert_ came in.
So we were looking around high and low for a tool that could do what we needed. And then I found Convert_, and I thought, this is really interesting.
Building the System
Jordan connected with Convert_ co-founder Bas Hennephof. When they spoke about Shift Collab’s needs, the project was highly intriguing to Bas.
Although Convert_ hadn’t been used for this use case before, he knew the platform had the functionality to create exactly what Shift Collab needed.
Bas stated:
Applying filters and scoring on big datasets makes us unique in the no-code space. But these use cases are not easy to spot. And although we had the functionality, we had not applied it in such an elaborate way before.
Shift Collab worked with Convert_ to build a custom intake and matching system powered by:
Conditional logic
Advanced scoring models
Dynamic question flows
Integration with internal systems
Due to the complexity, they initially used the Convert_ Concierge Service to build the system. Once live, their team took full control.
We can now make changes ourselves without relying on developers.
How the Matching System Works
Client completes the intake form: Questions adapt based on responses, and sensitive cases can be redirected appropriately.
Data is scored in real time: Outcomes are based on over 100 therapist and client variables.
Top therapist matches are generated: Clients receive three tailored options to choose from.
Client books directly: Forms are integrated with their booking and healthcare system
“A lot of scoring goes into it. It's not just as simple as a tally of points. Convert_’s conditional logic component is powerful enough to support our question branching, which is vital for finding the correct matches.”
The new system created a seamless end-to-end experience that didn’t exist before.
So now we have clients going in, and they're able to get three good matches. And right from there, they can book. This links directly with the booking and healthcare management system we use. That kind of end-to-end experience just wasn't possible before. It has been very cool.
The Results: More Capacity, Less Strain
With Convert_ powering intake and matching, the impact was immediate.
30% Conversion Rate Increase
Focused intake flows and better therapist matching led to a 30% increase in conversions.
Clients move forward with more confidence because the experience feels tailored to them.
Massive Time Savings
Manual data handling was reduced significantly. The automated system produced a 95% time saving on manual data management.
“Automation lets us process so many more inquiries without stressing the team.”
The team can now focus on client care instead of admin.
44% Customers Self-Booked
Now, 44% of clients book their sessions without manual assistance.
This reduces friction for clients while freeing up internal resources.
Reduced Development Costs
The no-code setup eliminated the need for developers and reduced development costs by around 95%.
Shift Collab can now:
Make updates internally
Test and refine flows quickly
Adapt to changing needs
Scalable Matching System
With over 125 therapists, the system continues to perform reliably.
It can handle increasing demand without compromising match quality.
Preserving the Human Element
One of the biggest risks of automation in healthcare is losing the human touch. Shift Collab approached automation differently.
They used it to remove repetitive tasks, not replace meaningful interactions.
The automated system we set up still lets us see each submission as a person, not just a form.
Improving the intake process created more space for genuine relationships between Shift Collab and its clients.
What’s Next
With the system in place, Shift Collab is now focused on optimisation.
Planned next steps include:
A/B testing intake flows
Refining therapist matching algorithms
Enhancing routing logic at the end of the journey
I'm really excited about the flexibility with Convert_. Especially around what we can do at the end of the flow to route people accordingly.
They can now scale by increasing demand, rather than increasing operational complexity.
If your business relies on complex intake, matching, or qualification processes, it’s possible to automate them without losing the human touch.
Get started for free today with Convert_ and see how structured automation can support both growth and client care.
