Leasehold Management MVP Development
Budget: £36 – £0 GBP
QED Labs: Project Liberty MVP Development
About QED Labs
QED Labs is a technology spin‐off from L1 Capital, a globally recognised hedge fund and real estate private equity investor with $6bn AUM. We build AI-powered systems and software that autonomously monitor, maintain, and manage real estate - eliminating human intervention while maximising efficiency and compliance. www.qedlabs.co.uk
Project Overview
Project Liberty is a new initiative aimed at transforming leasehold management. Leasehold is a common form of property ownership in the UK where individuals purchase the right to occupy a property for a fixed term – often ranging from 99 to 999 years – while the freeholder retains ultimate control of the land. Many leaseholders mistakenly believe they are buying their own home, only to later discover that true ownership remains with the freeholder. With over 8 million leaseholders, escalating ground rents, soaring service charges, and restrictive lease terms significantly limit their control and financial stability, imposing considerable financial, emotional, and practical challenges on many.
Our ultimate vision is to empower leaseholders to take over the management of their buildings – allowing them to manage with full transparency, improved service, and lower costs. As a starting point, our MVP will provide a simpler, focused solution: leaseholders and property managers can upload their service charge budgets, have the system extract and break down cost categories, and then compare these figures against flexible, data-driven benchmarks. This approach delivers an estimate of a "fair" service charge, revealing the potential gains from taking over management, and lays the foundation for building the only large, robust dataset of service charge costs in the UK.
Key MVP Functionality:
• Upload Interface: A simple front-end UI that allows users to upload their PDF service charge budget (typically PDF or Excel) along with key building details (e.g., address, region, number of apartments, amenities)
• Parsing Engine: An AI-driven engine to extract and standardise budget information from the PDF into a common format
• Data Storage: A database to store the standardised service charge data and building information
• Predictive Analytics: An analytics model that estimates ‘fair’ service charge costs per building
• Results Display: A clean, intuitive UI to present the predicted costs, break down the figures, and calculate potential savings for the building
Technical Requirements
• Frontend:
o Build the UI with a modern framework like React.
o Include a user-friendly form for uploading PDF budgets and entering building details.
o Provide a simple dashboard to view upload history and results, optimized for all devices.
• Backend:
o Use a modern database framework or service like Supabase for secure user authentication and data storage
o Implement a Python-based service for parsing PDFs and running the analytics model using modern AI tools.
o Expose API endpoints for user management, PDF upload, parsing, and data retrieval.
• Security & Compliance:
o Store PDFs securely (encrypted at rest) and scan for malware.
o Ensure data protection in line with UK standards, including GDPR compliance.
Timeline & Milestones
Week 1: Kickoff and finalise requirements
Weeks 2-3: Design, prototyping, and core feature development
Week 4: Testing, bug fixes, and final delivery
Note: Timelines may be adjusted based on progress and feedback.
Budget and Payment Terms
Budget: $5-15k
Milestone-based payment structure with clear deliverables for each phase.
Final payment contingent upon a successful demonstration and approval of the MVP.
Freelancer Qualifications
• Demonstrable experience in developing MVPs for SaaS platforms, ideally with exposure to PDF parsing and AI-driven solutions.
• Proven track record in secure, scalable full-stack development and cloud deployment.
• Familiarity with UK market standards and regulatory requirements (including GDPR).
• Strong communication skills and commitment to meeting defined timelines.
Proposal Guidelines
• Provide an overview of your approach to building the MVP, detailing the proposed technology stack and development plan.
• Include examples of similar projects or case studies, ideally within the UK market.
• Outline your availability, estimated timeline, and proposed milestones.
• Submit any questions or clarifications regarding the project scope.
• Proposals should be submitted by 28th February 2025.
Additional Information
• Confidentiality: The selected freelancer will be required to sign an NDA before commencing work.
• Evaluation Criteria: Proposals will be assessed based on technical expertise, relevant experience, clarity of approach, and cost efficiency.
About QED Labs
QED Labs is a technology spin‐off from L1 Capital, a globally recognised hedge fund and real estate private equity investor with $6bn AUM. We build AI-powered systems and software that autonomously monitor, maintain, and manage real estate - eliminating human intervention while maximising efficiency and compliance. www.qedlabs.co.uk
Project Overview
Project Liberty is a new initiative aimed at transforming leasehold management. Leasehold is a common form of property ownership in the UK where individuals purchase the right to occupy a property for a fixed term – often ranging from 99 to 999 years – while the freeholder retains ultimate control of the land. Many leaseholders mistakenly believe they are buying their own home, only to later discover that true ownership remains with the freeholder. With over 8 million leaseholders, escalating ground rents, soaring service charges, and restrictive lease terms significantly limit their control and financial stability, imposing considerable financial, emotional, and practical challenges on many.
Our ultimate vision is to empower leaseholders to take over the management of their buildings – allowing them to manage with full transparency, improved service, and lower costs. As a starting point, our MVP will provide a simpler, focused solution: leaseholders and property managers can upload their service charge budgets, have the system extract and break down cost categories, and then compare these figures against flexible, data-driven benchmarks. This approach delivers an estimate of a "fair" service charge, revealing the potential gains from taking over management, and lays the foundation for building the only large, robust dataset of service charge costs in the UK.
Key MVP Functionality:
• Upload Interface: A simple front-end UI that allows users to upload their PDF service charge budget (typically PDF or Excel) along with key building details (e.g., address, region, number of apartments, amenities)
• Parsing Engine: An AI-driven engine to extract and standardise budget information from the PDF into a common format
• Data Storage: A database to store the standardised service charge data and building information
• Predictive Analytics: An analytics model that estimates ‘fair’ service charge costs per building
• Results Display: A clean, intuitive UI to present the predicted costs, break down the figures, and calculate potential savings for the building
Technical Requirements
• Frontend:
o Build the UI with a modern framework like React.
o Include a user-friendly form for uploading PDF budgets and entering building details.
o Provide a simple dashboard to view upload history and results, optimized for all devices.
• Backend:
o Use a modern database framework or service like Supabase for secure user authentication and data storage
o Implement a Python-based service for parsing PDFs and running the analytics model using modern AI tools.
o Expose API endpoints for user management, PDF upload, parsing, and data retrieval.
• Security & Compliance:
o Store PDFs securely (encrypted at rest) and scan for malware.
o Ensure data protection in line with UK standards, including GDPR compliance.
Timeline & Milestones
Week 1: Kickoff and finalise requirements
Weeks 2-3: Design, prototyping, and core feature development
Week 4: Testing, bug fixes, and final delivery
Note: Timelines may be adjusted based on progress and feedback.
Budget and Payment Terms
Budget: $5-15k
Milestone-based payment structure with clear deliverables for each phase.
Final payment contingent upon a successful demonstration and approval of the MVP.
Freelancer Qualifications
• Demonstrable experience in developing MVPs for SaaS platforms, ideally with exposure to PDF parsing and AI-driven solutions.
• Proven track record in secure, scalable full-stack development and cloud deployment.
• Familiarity with UK market standards and regulatory requirements (including GDPR).
• Strong communication skills and commitment to meeting defined timelines.
Proposal Guidelines
• Provide an overview of your approach to building the MVP, detailing the proposed technology stack and development plan.
• Include examples of similar projects or case studies, ideally within the UK market.
• Outline your availability, estimated timeline, and proposed milestones.
• Submit any questions or clarifications regarding the project scope.
• Proposals should be submitted by 28th February 2025.
Additional Information
• Confidentiality: The selected freelancer will be required to sign an NDA before commencing work.
• Evaluation Criteria: Proposals will be assessed based on technical expertise, relevant experience, clarity of approach, and cost efficiency.