I’m commissioning a comprehensive, AI-driven platform that delivers accurate, defensible valuations for residential, commercial, and industrial properties. The system must combine traditional appraisal logic with machine-learning models so users receive instant estimates backed by Comparative Market Analysis, the Income Approach, and the Cost Approach. Scope • Data pipeline – automate collection, cleaning, and periodic refresh of public records, MLS feeds, rental rolls, construction costs, and macro-economic indicators. • Model layer – train, test, and deploy ML/AI models that dynamically weight the three valuation methods above and surface confidence scores. • Business logic – allow users to toggle assumptions (cap rates, vacancy, depreciation schedules, cost indices) and see live sensitivity analysis. • User experience – responsive web dashboard plus REST/GraphQL API so the valuations can be embedded in partner CRMs or mortgage platforms. • Audit trail – versioning of every data point and calculation step to satisfy regulators, lenders, and auditors. • Security & compliance – role-based access, encryption at rest/in transit, GDPR-ready data handling, and detailed logging. • Documentation – architecture diagram, deployment scripts (Docker/Kubernetes), user manual, and model interpretability notes. • Testing & support – automated unit/integration tests and a hand-off period for bug fixes and performance tuning. Tech preferences Python, TensorFlow/PyTorch, PostgreSQL, and a modern JS front-end (React or Vue) are ideal, yet I’m open to alternatives that achieve equal robustness and maintainability. Success is a production-ready platform I can host on AWS or Azure, complete with CI/CD, monitoring hooks, and the full source code in a Git repository.