Dubai AI Direct Sales Platform
Budget / SalaryHourly project
TypeFreelance project
LocationRemote
Posted1 hour ago
I am looking for end-to-end development of a cloud-based AI platform that streamlines our direct-sales activities and, most importantly, drives measurable revenue growth. We are a technology company headquartered in Dubai, so the solution has to be robust enough for rapid scaling yet flexible enough to adapt to the local market regulations and multilingual user base.
Core objectives
• Harness machine-learning models to surface the most profitable prospects, recommend next-best actions, and automatically prioritise follow-up.
• Provide a real-time dashboard that lets my sales team track performance against targets, view pipeline health, and drill into individual deal histories with a single click.
• Offer clean APIs or webhooks so the engine can plug seamlessly into our existing CRM, marketing-automation stack, and payment gateway.
Tech expectations
Python, Node.js, or another modern backend is fine as long as the architecture is secure, modular, and easy to maintain. If you prefer TensorFlow, PyTorch, or similar frameworks for the predictive layer, mention that in your proposal. A microservices approach on AWS, Azure, or GCP is welcomed; containerisation with Docker or Kubernetes is a plus.
Deliverables
1. Fully functional web application (front-end, back-end, and AI layer) deployed to our cloud account
2. Source code in a private Git repo with clear commit history
3. Installation and user documentation sufficient for our in-house engineers to maintain and extend the system
4. Walk-through session (remote or on-site in Dubai) to demonstrate key workflows and answer technical questions
Acceptance criteria
• Platform demonstrates statistically significant uplift in conversion rate during a two-week A/B pilot we will run internally.
• Average API response time under 300 ms for standard queries.
• Codebase passes our automated security scan with zero critical vulnerabilities.
If you have previously built AI-driven sales tools or analytics dashboards—especially for technology firms—please include links or brief case studies in your bid. Feel free to outline your preferred tech stack, timeline, and any suggestions that could accelerate time-to-value.
Core objectives
• Harness machine-learning models to surface the most profitable prospects, recommend next-best actions, and automatically prioritise follow-up.
• Provide a real-time dashboard that lets my sales team track performance against targets, view pipeline health, and drill into individual deal histories with a single click.
• Offer clean APIs or webhooks so the engine can plug seamlessly into our existing CRM, marketing-automation stack, and payment gateway.
Tech expectations
Python, Node.js, or another modern backend is fine as long as the architecture is secure, modular, and easy to maintain. If you prefer TensorFlow, PyTorch, or similar frameworks for the predictive layer, mention that in your proposal. A microservices approach on AWS, Azure, or GCP is welcomed; containerisation with Docker or Kubernetes is a plus.
Deliverables
1. Fully functional web application (front-end, back-end, and AI layer) deployed to our cloud account
2. Source code in a private Git repo with clear commit history
3. Installation and user documentation sufficient for our in-house engineers to maintain and extend the system
4. Walk-through session (remote or on-site in Dubai) to demonstrate key workflows and answer technical questions
Acceptance criteria
• Platform demonstrates statistically significant uplift in conversion rate during a two-week A/B pilot we will run internally.
• Average API response time under 300 ms for standard queries.
• Codebase passes our automated security scan with zero critical vulnerabilities.
If you have previously built AI-driven sales tools or analytics dashboards—especially for technology firms—please include links or brief case studies in your bid. Feel free to outline your preferred tech stack, timeline, and any suggestions that could accelerate time-to-value.
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