Context
SignAssist is a PropTech SaaS startup building an AI-based property and investment management platform for real estate agents. Early growth stage, team of ~6, operating in a market still heavily reliant on fragmented tools and manual workflows.
Problem
Agents were managing the full rental lifecycle — listings, tenant screening, contracts, portfolio tracking — across disconnected tools. Retention was low and unit economics were poor: CAC/LTV was running at 162%, blocking sustainable growth. The business needed to fix activation and retention before scaling acquisition.
The main issues in property management are the volume of manual work and the complexity of regulation, which is difficult to keep current on. We also built a prototype addressing the growing challenge of selecting and screening rental applicants — a process that can attract upwards of 200 applications per property, particularly in high-demand markets such as Amsterdam.
My role
I owned the product end-to-end: strategy, roadmap, design direction, analytics infrastructure, and investor relationships. Engineering and design execution were delegated; all product and commercial decisions were mine.
Approach
Funnel analysis and prototype testing identified onboarding friction as the primary drop-off driver. I redesigned onboarding and in-product messaging flows, testing iteratively before shipping. In parallel, I led development of an AI document parsing module that converted unstructured legal and contract text into structured data with compliance flags — removing significant manual overhead for agents. I also built a pricing strategy from usage data and customer segmentation, and set up a recurring analytics review cadence to keep the team aligned on growth levers.
Outcome
- Active users grew 87%
- Time to value reduced 75%
- CAC/LTV improved from 162% to 21%
- MRR grew 23% following the new pricing strategy
