Autonomous AI Construction Engineer Platform
Budget: $15 – $25 USD
I am building a fully autonomous system that can think and act like a veteran construction engineer—only faster. The core features I need you to deliver are:
• Project planning and scheduling: ingest drawings, specs, or even a rough scope and instantly output an optimised, resource-levelled schedule (Gantt, CPM, critical-path alerts, what-if analyses).
• Cost estimation and budgeting: generate detailed BoQs, pull live material-price feeds, model labour curves, and keep contingency and escalation logic intact throughout the life-cycle.
Everything must run seamlessly on mobile (iOS / Android), in the browser, and as a desktop install. A single code base with a shared AI engine is strongly preferred so end users can pick up work on-site, in the office, or in the field without friction.
Autonomy is non-negotiable: the system should make decisions on its own—select suppliers, adjust schedules, reallocate crews—while still logging its rationale for audit and compliance. Human review will be optional, not required, so the underlying models need to be explainable and defensible.
Typical toolkit ideas include Python or TypeScript micro-services, TensorFlow / PyTorch for the learning layer, and Flutter, React Native, or Electron for the clients, but I am open to alternative stacks so long as you justify them against scalability, maintainability, and speed to market.
Deliverables I expect:
1. End-to-end architecture diagram and tech stack proposal
2. MVP with trained models, responsive cross-platform UI, and a live demo dataset
3. Deployment scripts (Docker / Kubernetes) plus CI/CD pipeline
4. Full source code with unit and integration tests
5. Documentation that lets a midsize construction firm install, train, and extend the system without vendor lock-in
Acceptance criteria will be a real-world test where the AI plans a mid-rise build, produces a cost book within ±3 % of an experienced estimator, and adjusts the schedule after simulated supply delays without human prompts.
If you have proven experience blending advanced machine learning with robust software engineering—and can show examples of past decision-autonomy work—let’s talk specs and milestones.
• Project planning and scheduling: ingest drawings, specs, or even a rough scope and instantly output an optimised, resource-levelled schedule (Gantt, CPM, critical-path alerts, what-if analyses).
• Cost estimation and budgeting: generate detailed BoQs, pull live material-price feeds, model labour curves, and keep contingency and escalation logic intact throughout the life-cycle.
Everything must run seamlessly on mobile (iOS / Android), in the browser, and as a desktop install. A single code base with a shared AI engine is strongly preferred so end users can pick up work on-site, in the office, or in the field without friction.
Autonomy is non-negotiable: the system should make decisions on its own—select suppliers, adjust schedules, reallocate crews—while still logging its rationale for audit and compliance. Human review will be optional, not required, so the underlying models need to be explainable and defensible.
Typical toolkit ideas include Python or TypeScript micro-services, TensorFlow / PyTorch for the learning layer, and Flutter, React Native, or Electron for the clients, but I am open to alternative stacks so long as you justify them against scalability, maintainability, and speed to market.
Deliverables I expect:
1. End-to-end architecture diagram and tech stack proposal
2. MVP with trained models, responsive cross-platform UI, and a live demo dataset
3. Deployment scripts (Docker / Kubernetes) plus CI/CD pipeline
4. Full source code with unit and integration tests
5. Documentation that lets a midsize construction firm install, train, and extend the system without vendor lock-in
Acceptance criteria will be a real-world test where the AI plans a mid-rise build, produces a cost book within ±3 % of an experienced estimator, and adjusts the schedule after simulated supply delays without human prompts.
If you have proven experience blending advanced machine learning with robust software engineering—and can show examples of past decision-autonomy work—let’s talk specs and milestones.
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