AI Analytics Automation SaaS Build
Budget: ₹12,500 – ₹37,500 INR
I am planning a cloud-based platform that uses AI to enhance data analytics for businesses. The core of the product will connect to three distinct data streams—our own internal databases, selected third-party APIs, and live user-generated data—and surface insights through automated dashboards, anomaly detection, and predictive models.
Here is the shape of the engagement I have in mind. You would design and implement the full SaaS stack: data ingestion pipelines, the model layer (classical ML or LLM-powered where it makes sense), and a multi-tenant web interface. Clean architecture, scalable micro-services, and secure authentication are must-haves because I want to take this to market quickly without rewriting the foundations later.
I value proof over promises, so please share links or screenshots of past work that demonstrates you have already shipped AI or analytics products at production scale. I will review those examples closely; elaborate proposals are optional, the results you can point to are what matter most.
If we proceed, the deliverables will be:
• A working MVP deployed to my cloud account
• Source code in a private repo with commit history
• Technical documentation: data flow, model training process, and setup instructions
• A short video walkthrough showing the product in use
Once the MVP meets these criteria and passes a live data test with all three data sources, we can discuss an ongoing roadmap together.
Here is the shape of the engagement I have in mind. You would design and implement the full SaaS stack: data ingestion pipelines, the model layer (classical ML or LLM-powered where it makes sense), and a multi-tenant web interface. Clean architecture, scalable micro-services, and secure authentication are must-haves because I want to take this to market quickly without rewriting the foundations later.
I value proof over promises, so please share links or screenshots of past work that demonstrates you have already shipped AI or analytics products at production scale. I will review those examples closely; elaborate proposals are optional, the results you can point to are what matter most.
If we proceed, the deliverables will be:
• A working MVP deployed to my cloud account
• Source code in a private repo with commit history
• Technical documentation: data flow, model training process, and setup instructions
• A short video walkthrough showing the product in use
Once the MVP meets these criteria and passes a live data test with all three data sources, we can discuss an ongoing roadmap together.