Property Managers Turnover Units Faster With AI Agents

A well-known Property Management firm in the Bay Area that manages the day-to-day operations of multiple 200+ unit apartment complexes, came to us in crisis mode dealing with nonstop operational chaos, lost documentation, and lower NOI on their property portfolio. Prolonged unit downtime after tenant turnover was a result of disconnected workflows between office staff and technicians especially when hitting a wall with slow insurance claim resolutions. These challenges are amplified when properties include a diverse mix of unit types—ranging from standard single-family and multi-bedroom apartments to high-turnover luxury condos.

Our approach started with technical debt evaluation to determine how we can use software tools you've already paid for and use to minimize friction of building out your first automation process, or improving an existing one. By integrating Artificial Intelligence (AI) and Large Language Models (LLMs) streamlining operations from the back office to field technicians is unlocking new gains for Real Estate Operations Professionals.

We built a bespoke orchestration between their systems targeting Intelligent Scheduling & Turnover Optimization. Since AI systems can analyze historical turnover data to predict average timelines for cleaning, repair, and listing & LLMs can generate proactive task schedules, assigning field technicians becomes an afterthought. For example, upon tenant move-out, the system can instantly auto-generate a checklist tailored to the unit type then assign available contractors based on location, capacity, past performance etc.

Our AI-agents can now extract data from field reports, photos, and videos captured by maintenance staff. Using OCR and NLP, these tools auto-generate standardized insurance documentation, reducing the administrative burden and speeding up claim processing by integrating through 3rd-party insurance portals.

For instance, a technician uploads a video of water damage. The LLM summarizes the issue, identifies the likely cause (e.g., burst pipe), estimates the damage cost using historical data, and populates the relevant insurance form—all within minutes.