End-to-End AI Work agents & Dashboard Development
Budget / Salary₹12,500–37,500
TypeFreelance project
LocationRemote
Posted1 hour ago
I want a single, robust platform that serves two separate divisions of my company—metal trading and real-estate development. The vision is a web-based dashboard with a built-in workforce of AI agents that streamlines every stage of our operations, from sourcing deals to closing sales.
Scope of work
• Build the core dashboard UI/UX, accessible on desktop and mobile, with role-based views for Admin, Manager, Sales Agent, as well as procurement, construction, marketing, supply teams and the various VPs.
• Wire up three data pipelines—our internal SQL/NoSQL stores, selected third-party APIs (market prices, permitting, logistics, etc.), and routine spreadsheet uploads—so that live metrics appear without manual effort.
• Implement Data Analytics & Reporting that lets each role dig into KPIs, trend charts, and predictive insights, exporting to PDF or Excel on demand.
• Layer in Workflow Automation: trigger tasks, approvals, and notifications when inventory dips, a bid is won, or a construction milestone is reached.
• Provide a secure User Management module for onboarding, permission handling, and audit logs.
AI agent workforce
I need specialized agents that can:
– scan incoming quotes or leads, classify them, and draft responses;
– forecast commodity or land prices using historical data and current feeds;
– generate weekly executive summaries in natural language;
– flag anomalies (price spikes, schedule overruns) and propose corrective actions.
Deliverables
1. Deployed cloud application with role-based dashboards for both business lines.
2. Documented data integrations and ETL scripts.
3. Library of AI agents with clear prompts, model specs, and retraining instructions.
4. Admin guide plus a recorded hand-off session.
Acceptance criteria: every listed role must be able to log in with the right permissions, see real-time data from all three source types, and interact with at least five functioning AI agents that pass a live demo using our own datasets.
Tech stack is flexible—React or Vue for the front end, Python or Node.js for the back end, and whichever LLM framework best suits the agents—as long as it’s well-documented, test-covered, and containerised for easy deployment.
Scope of work
• Build the core dashboard UI/UX, accessible on desktop and mobile, with role-based views for Admin, Manager, Sales Agent, as well as procurement, construction, marketing, supply teams and the various VPs.
• Wire up three data pipelines—our internal SQL/NoSQL stores, selected third-party APIs (market prices, permitting, logistics, etc.), and routine spreadsheet uploads—so that live metrics appear without manual effort.
• Implement Data Analytics & Reporting that lets each role dig into KPIs, trend charts, and predictive insights, exporting to PDF or Excel on demand.
• Layer in Workflow Automation: trigger tasks, approvals, and notifications when inventory dips, a bid is won, or a construction milestone is reached.
• Provide a secure User Management module for onboarding, permission handling, and audit logs.
AI agent workforce
I need specialized agents that can:
– scan incoming quotes or leads, classify them, and draft responses;
– forecast commodity or land prices using historical data and current feeds;
– generate weekly executive summaries in natural language;
– flag anomalies (price spikes, schedule overruns) and propose corrective actions.
Deliverables
1. Deployed cloud application with role-based dashboards for both business lines.
2. Documented data integrations and ETL scripts.
3. Library of AI agents with clear prompts, model specs, and retraining instructions.
4. Admin guide plus a recorded hand-off session.
Acceptance criteria: every listed role must be able to log in with the right permissions, see real-time data from all three source types, and interact with at least five functioning AI agents that pass a live demo using our own datasets.
Tech stack is flexible—React or Vue for the front end, Python or Node.js for the back end, and whichever LLM framework best suits the agents—as long as it’s well-documented, test-covered, and containerised for easy deployment.
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