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Proptech

Productizing AI/ML Models

for PropTech leader, Tailorbird

Keywords

AI-ML, Real Estate

Services Rendered

Product Design

Product Engineering

Continuing Design

Tech stack

Material UI, GraphQL, Twilio SendGrid

Series A

Tailorbird partnered with Zemoso to co-create elements of their proprietary AI-ML solution and provide proof of value to customers and investors.

iNDUSTRY CHALLENGE

Tailorbird's proprietary model for property management companies enables reality capture from preexisting photos and floor plans. It helps property management groups cost out renovations and interior design projects to the finest detail using their proprietary technology.

Zemoso Labs Partnership Challenge

Zemoso worked with Tailorbird’s engineering and design team to:

  1. Create a prototype and use it to generate leads, attract investors, and hone product-market fit.
  2. Launch V1 of the product in four months.
  3. Evolve design and features using a fast-tracked feedback loop to increase adoption

Impact created

Within one year, Tailorbird raised a seed round of funding from Moderne Ventures, identified their most profitable customer cohort, and  acquired three out of the top five multi-family property management companies in the US as customers within 6 months of launch.

What are our clients saying?

Our clients love what we do:

“Tailorbird is making it easy to realize large real-estate renovations. We partnered with Zemoso because of their differentiated capability to work through uncertainties of an early-stage product design and launch.”

Ashish Jain, CTO and Co-founder

Tailorbird

How did we do this?

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Phase 1:

We hosted our version of Google Ventures Design Sprint to create high-fidelity, clickable prototypes. We co-created the golden path for key users as part of that. The prototype generated leads and helped with product discovery.

Leveraging the early customers acquired, Tailorbird:

  1. Iterated the prototype quickly using customer feedback with Zemoso’s product pod
  2. Reduced the timeline to acquire proof of value
  3. Raised seed funding from Moderne Ventures with the prototype

Phase 2:

We worked closely with our stakeholders to help productize the AI/ML model and outline the experience around it on an expedited timeline.

The Zemoso expertise: We provided them with an agile team that self-organized and evolved based on development and design needs.

Key highlights of the solution:

As the first step to laying out a detailed UX design and the system architecture, we aligned with Tailorbird leadership on platform essentials:

  1. Make the platform intuitive and easy-to-use
  2. Deploy user controls
  3. Enable quick release of new features based on user feedback

The product has a microservices architecture to make it more robust. This ensures that the system itself is scalable and during new feature introduction, any faults introduced are isolated and quickly resolved. Simultaneously, we co-created the detailed design for all user flows and helped set up access controls.

We opted for a Material-UI design approach for a seamless, adaptable, and uniform front-end. We used atomic design principles  to make releasing new updates and features more efficient.

Together with Tailorbird’s internal team, we ensured that the product has superior abilities to store and process flexible document schemas (spreadsheets, images, etc.). A GraphQL interface layer was used to ensure easier testing for new features and faster processing times. DevOps practices were used to automate testing and integration and containerization for easier deployment.

An integration with Twilio SendGrid ensured that all internal and external stakeholders got alerts within three seconds of an action being taken. The modular approach to building the entire product had another benefit. As most capabilities offered are built in their own self-contained units, property management companies can pick which services to buy and use, encouraging trial and early adoption of the product.

P.S. The partnership with Tailorbird is ongoing and evolving.

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