Project summary

Hi, I am Amrutha, an AI Engineer with experience at Samsung. I am seeking funding to develop a working prototype of an AI Model Drift Monitoring Tool for machine learning systems deployed in production environments.

I have already filed a patent related to this and am now at the stage where I need to build a demonstrable prototype that can be presented to investors and potential enterprise customers. This is currently bootstrapped and is now far exceeding the initial budget for compute. This funding will really help get to a market-ready proof of concept.

I have demo to ITEL team on July 17th, I need support to complete the development and present to them.

What are this project's goals? How will you achieve them?

• Develop a demo able live prototype for automated AI model drift detection and monitoring.

• Enable real-time monitoring of data drift, concept drift, and model performance degradation.

• Build an intuitive dashboard for model health visualization and alerts.

• Use the prototype to support fundraising discussions with investors.

• The prototype will be developed as a cloud-native monitoring platform capable of tracking model performance degradation, data drift, and concept drift across deployed machine learning systems.

How will this funding be used?

AWS compute (EC2, testing workloads, scheduled jobs) - $800

RDS -$250

S3 objects - $300

Runpod - $500

GPT 5 API costs - $1000

Pen testing - $250

IPC - $150

Who is on your team? What's your track record on similar projects?

I am currently leading this project independently.

I previously worked as an AI Engineer at Samsung, where I developed and supported multilingual NMT systems for Samsung Galaxy AI features, including Live Translate, Interpreter, Writing Assist, Note Assist, and Browsing Assist. Worked on production deployment pipelines for transformer-based sequence-to-sequence models optimized for on-device and hybrid inference across Galaxy smartphones and tablets.

What are the most likely causes and outcomes if this project fails?

In production-like environments, distinguishing meaningful model drift from normal usage variability can be challenging, especially across heterogeneous datasets and model types.

Irrelavent RCA and over triage issues are also a failure points

How much money have you raised in the last 12 months, and from where?

I have not raised any external funding for this project in the last 12 months.

Development to date has been self-funded through my own time, research efforts, and personal resources.