AI Rental Contract Management
AI Rental Contract Management
Property Management Made Easy
Transformstediousleasedataentryintoanautomatedprocessthatextractsandorganizesrentalinformation,andturnsonboardingintoatime-to-valuemomentforpropertymanagers.
Transformstediousleasedataentryintoanautomatedprocessthatextractsandorganizesrentalinformation,andturnsonboardingintoatime-to-valuemomentforpropertymanagers.
Transformstediousleasedataentryintoanautomatedprocessthatextractsandorganizesrentalinformation,andturnsonboardingintoatime-to-valuemomentforpropertymanagers.
CHALLENGE
RentRedi’s goal is to help multi-property managers manage rental contracts and ensure timely payments. But onboarding required manually entering rent data for each tenant, interpreting complex state-specific clauses, and completing 80+ steps before seeing any benefit. This early friction caused drop-offs and limited adoption.
RentRedi’s goal is to help multi-property managers manage rental contracts and ensure timely payments. But onboarding required manually entering rent data for each tenant, interpreting complex state-specific clauses, and completing 80+ steps before seeing any benefit. This early friction caused drop-offs and limited adoption.
Commercial real estate loan origination is document-heavy, high-risk, and hard to scale. Loan originators and underwriters spend hours per deal:
Chasing missing or outdated documents
Reconciling inconsistencies across records
Repeating manual checks under tight timelines and regulatory pressure
As deal volume grows:
Per-loan costs increase, often requiring more analysts or underwriters
Mistakes can lead to rework, delayed decisions, or financial risk
RentRedi’s goal is to help multi-property managers manage rental contracts and ensure timely payments. But onboarding required manually entering rent data for each tenant, interpreting complex state-specific clauses, and completing 80+ steps before seeing any benefit. This early friction caused drop-offs and limited adoption.
SOLUTION
Property managers can upload unstructured rental agreements and instantly see structured lease data, where is accurately extracted and organized by property → unit → tenant.
Data are editable and transparent, replacing tedious manual entry with guided, structured validation.
A coordinated workforce of AI agents, each handling a specific task, replicates and improves the loan origination workflow. Together, they streamline manual processes, verify key information, execute actions, and deliver detailed deal insights to guide officers’ decisions.
My Role
Product strategy & AI: Defined scope, framed problems, prioritized datasets and edge cases, and oversaw end-to-end AI testing and system integration.
Onboarding & alignment: Led UX/flow redesign, guided by user research and iterative testing, while coordinating cross-functional teams and collaborating directly with clients' CEO and leadership.
Product strategy & AI: Defined scope, framed problems, prioritized datasets and edge cases, and oversaw end-to-end AI testing and system integration.
Onboarding & alignment: Led UX/flow redesign, guided by user research and iterative testing, while coordinating cross-functional teams and collaborating directly with clients' CEO and leadership.
Outcomes
Outcomes
80%+ faster onboarding and contract setup with 96% accurate data extraction
80%+ faster onboarding and contract setup with 96% accurate data extraction
80%+ faster onboarding and contract setup with 96% accurate data extraction
80%+ faster onboarding and contract setup with 96% accurate data extraction
Secured long-term partnership with Rentredi
Secured long-term partnership with Rentredi
Secured long-term partnership with Rentredi
Secured long-term partnership with Rentredi
Significant lift in subscriptions and improved user retention
Significant lift in subscriptions and improved user retention
Significant lift in subscriptions and improved user retention
Significant lift in subscriptions and improved user retention
How it works
How it works
Lease Data Extraction at Scale
Upload leases from multiple properties at once, and AI automatically extracts the most important information.
Structured & Editable Lease Data
See lease data extracted and structured automatically by property → unit → tenant, with real-time results you can review and edit as needed.
Key Decisions I made
Key Decisions I made
Reframed onboarding as a value moment
Structured rental data appeared instantly instead of manual entry.
Impact: Cut onboarding time by 80%+, improved adoption, and gave users a meaningful first experience.
Structured rental data appeared instantly instead of manual entry.
Impact: Cut onboarding time by 80%+, improved adoption, and gave users a meaningful first experience.
Structured rental data appeared instantly instead of manual entry.
Impact: Cut onboarding time by 80%+, improved adoption, and gave users a meaningful first experience.
Prioritized user control over full automation
Users could verify and modify AI-extracted information, ensuring accuracy and maintaining confidence.
Impact: Built trust, minimized errors, and enabled confident handling of complex leases.
Users could verify and modify AI-extracted information, ensuring accuracy and maintaining confidence.
Impact: Built trust, minimized errors, and enabled confident handling of complex leases.
Users could verify and modify AI-extracted information, ensuring accuracy and maintaining confidence.
Impact: Built trust, minimized errors, and enabled confident handling of complex leases.
Treated the dataset strategy as a product decision
Identified and strategically prioritized lease edge cases, overseeing dataset strategy and AI evaluation with defined success metrics.
Impact: Improved AI data extraction accuracy from ~60% to 96%, sped up user validation, and reduced cognitive load.
Identified and strategically prioritized lease edge cases, overseeing dataset strategy and AI evaluation with defined success metrics.
Impact: Improved AI data extraction accuracy from ~60% to 96%, sped up user validation, and reduced cognitive load.
Identified and strategically prioritized lease edge cases, overseeing dataset strategy and AI evaluation with defined success metrics.
Impact: Improved AI data extraction accuracy from ~60% to 96%, sped up user validation, and reduced cognitive load.
Guided users through complex lease edge cases efficiently
Redesigned UX to highlight scenarios where users needed to intervene versus where the system could act automatically, giving them visibility and control over ambiguous or tricky lease cases.
Impact: Reduced cognitive load, accelerated accurate decision-making, and strengthened user trust in AI outputs.
Redesigned UX to highlight scenarios where users needed to intervene versus where the system could act automatically, giving them visibility and control over ambiguous or tricky lease cases.
Impact: Reduced cognitive load, accelerated accurate decision-making, and strengthened user trust in AI outputs.
Redesigned UX to highlight scenarios where users needed to intervene versus where the system could act automatically, giving them visibility and control over ambiguous or tricky lease cases.
Impact: Reduced cognitive load, accelerated accurate decision-making, and strengthened user trust in AI outputs.
The Process
The Process
Workflow & Friction Mapping
I walked through onboarding as a first-time landlord, simulating multi-property, multi-tenant, and state-specific lease scenarios. This revealed that the process required over 80 clicks, involved multiple decision points, and often got users stuck before they could see any value or subscribe. Legal and payment steps were immutable, so we resequenced workflows to surface insights early while preserving compliance. These observations uncovered friction points invisible in analytics and directly guided both the AI strategy and UX redesign.
Workflow & Friction Mapping
I walked through onboarding as a first-time landlord, simulating multi-property, multi-tenant, and state-specific lease scenarios. This revealed that the process required over 80 clicks, involved multiple decision points, and often got users stuck before they could see any value or subscribe. Legal and payment steps were immutable, so we resequenced workflows to surface insights early while preserving compliance. These observations uncovered friction points invisible in analytics and directly guided both the AI strategy and UX redesign.
Workflow & Friction Mapping
I walked through onboarding as a first-time landlord, simulating multi-property, multi-tenant, and state-specific lease scenarios. This revealed that the process required over 80 clicks, involved multiple decision points, and often got users stuck before they could see any value or subscribe. Legal and payment steps were immutable, so we resequenced workflows to surface insights early while preserving compliance. These observations uncovered friction points invisible in analytics and directly guided both the AI strategy and UX redesign.
Experience Principles Alignment
Experience Principles Alignment
Experience Principles Alignment
Dataset & AI Strategy
Dataset & AI Strategy
Dataset & AI Strategy
UX & Trust Design
UX & Trust Design
UX & Trust Design
AI Integration & Risk-Oriented Decision Making
AI Integration & Risk-Oriented Decision Making
AI Integration & Risk-Oriented Decision Making
Client Pilot & Feedback Loop
Client Pilot & Feedback Loop
Client Pilot & Feedback Loop
Explore other projects
Explore other projects
Ready to build something amazing?
Ready to build something amazing?
Let’s connect!
Available Worldwide
Available Worldwide
Available Worldwide
Available Worldwide