Azure-based AI Analysis Platform

Budget: 2000 USD Deadline: 28 days

Hello! I was interested to learn about your project for developing a cloud-based AI analysis platform on Azure. My 5 years of experience in creating full-stack solutions in Python (Django/FastAPI) and TypeScript (React/NestJS) is perfectly suited for implementing comprehensive data collection and analysis systems. I specialize in building scalable backends and interactive frontends capable of processing real data and integrating with external services. I am ready to develop a fully functional and real-time demonstrable platform that meets your requirements.

Budget: 2000 USD Deadline: 14 days

Good day, I have over 15 years of experience in development with Microsoft Azure and SharePoint, I have been working with machine learning and AI for more than 3 years, and I have extensive experience in other technologies, I would be happy to take on your project.

Budget: 1250 USD Deadline: 10 days

Hello! I have reviewed the requirements for the Azure & AI platform. I have experience in building end-to-end solutions on Microsoft Azure, working with Microsoft Graph, external APIs, and managed AI pipelines based on Azure OpenAI and AutoML. I am ready to take responsibility for the technical implementation and complete the project within the stated deadlines.

Budget: 1700 USD Deadline: 30 days

Hello!
I am interested in your offer and intend to join the team of professionals.

BRIEFLY ABOUT ME:
I have been specializing in creating Web applications of various types: from landing pages to large-scale solutions (such as CRM and ERP systems).
At one time, I worked on developing applications based on Microsoft ecosystem tools
Best projects:
Behance

I hope for fruitful cooperation in the future!

Best regards, Bohdan Expand

Budget: 1099 USD Deadline: 14 days

Proposed Engagement Approach (Phased Delivery)

Based on the scope described, I suggest structuring the engagement in clearly defined phases to ensure technical feasibility, predictable delivery, and a demonstrable outcome at each step.

Phase 0 — Paid Discovery & Architecture Sprint (1–2 weeks)
Objective: de-risk the concept and validate the core assumptions before full implementation.

Scope:

High-level solution architecture on Microsoft Azure (services, security boundaries, data flows).

Definition of the ingestion and processing pipeline (ingestion → normalization → AI processing → output).

Validation of the “prompt-driven” control model for AI processing (what is realistically achievable without hardcoded logic, and where guardrails are required).

Selection of one ingestion source for the PoC (e.g. SharePoint via Microsoft Graph or one external API).

Definition of success criteria for a live demo.

Deliverables:

Architecture diagram and technical design document.

Refined scope and effort estimate for Phase 1.

Identified technical risks and mitigation options.

Phase 1 — Narrow PoC (End-to-End, Live Demo)
Objective: deliver a working, end-to-end system with real data and real integrations, but tightly scoped.

Scope:

Project lifecycle basics (create / delete a project).

One ingestion source (e.g. SharePoint or Survey API).

One AI processing flow using Azure OpenAI with prompt-driven behavior.

Storage of AI outputs per project.

Minimal UI to:

View AI outputs,

Select (single / multi / select all),

Download and delete results.

Outcome:

Fully working, live-demonstrable PoC.

Clear evidence of feasibility and limitations.

Solid foundation for scaling.

Phase 2 — Expansion & Hardening (Optional)
Objective: extend the validated PoC toward the full platform vision.

Possible additions:

Additional ingestion sources (Survey platform, collaboration tools, CRM).

Project cloning / renaming.

More advanced AI flows and data types.

UX improvements and operational hardening.

Why this approach
This phased model ensures:

Early validation of the most complex and risky assumptions.

No wasted engineering effort on unproven ideas.

Transparent cost, scope, and timelines.

A demo-ready result at every phase.

If this approach aligns with your expectations, I’m happy to proceed with Phase 0 and then scale based on validated results. Expand