AI-powered PropTech governance

Risk intelligence for housing development projects.

EVMesh AI connects stakeholders, contracts, cash flow, sales, ESG, and Python analytics into one practical governance intelligence system.

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Live walkthrough

See EVMesh AI in action.

Product walkthroughs are shared in live sessions with qualified housing developers, investors, and project teams.

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Functional MVP Browser interface connected to a local Python analytics engine.

Research-based Inspired by published Emerald research on AI stakeholder network modeling.

B2B SaaS Built for developers, investors, project owners, and governance teams.

The problem

Development risk is fragmented across people, contracts, finance, sales, and ESG.

Housing projects are often managed through disconnected spreadsheets, manual reports, separate contract files, and delayed human judgment.

This makes it difficult to see early warning signals before they affect cost, delivery, investor confidence, or regulatory compliance.

The solution

A connected governance mesh for real estate development decisions.

Map accountability

Stakeholders become accountable project nodes with roles, deliverables, values, and milestones.

Connect cash and contracts

Payments, earned value, sales cash-in, and contract obligations are analyzed together.

Forecast risk earlier

Python analytics estimate schedule pressure, cost exposure, ESG risk, and future shortfall scenarios.

Product modules

One system, multiple governance lenses.

Governance Dashboard

Stakeholder Network

Smart Contracts

Sales & Inventory

Risk Forecast

Python AI Lab

ESG Audit

Audit Reports

Research foundation

Built from peer-reviewed housing risk governance research.

EVMesh AI is inspired by Mohammad Amin Gharehgozli's published article in Emerald Publishing's International Journal of Housing Markets and Analysis:

The contribution of AI-based stakeholder network modeling to risk governance in housing development

DOI: 10.1108/IJHMA-05-2025-0119

Business model

B2B SaaS with consulting-assisted onboarding.

Early customers

Housing developers, real estate investment groups, project owners, and ESG-conscious development teams.

Revenue paths

Project subscription, company plans, pilot implementation fees, audit reports, and enterprise licensing.

Next milestone

Developer interviews, pilot validation, landing page testing, and accelerator or innovation program outreach.

Next step

Request a live walkthrough of EVMesh AI.

The current MVP is ready for product conversations, pilot discovery, and early validation with housing developers.

Founder Mohammad Amin Gharehgozli

amin.gharehgozli1988@gmail.com LinkedIn: Mohammad Amin Gharehgozli

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Public overview only. Source code, algorithms, datasets, internal scoring logic, and implementation details are confidential and are not included in this public demo.