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AutoCOI—AI-Powered Vendor Insurance Compliance System
AutoCOI is an AI-powered system designed to automate and streamline the vendor insurance compliance process. It helps companies to eliminate manual paperwork, reduce errors, and ensure compliance with vendor requirements. The system uses OCR technology to extract data from insurance certificates and automatically matches it to the vendor requirements. It also provides automated notifications and alerts to ensure timely policy renewals. The solution offers secure data storage and easy access to vendor insurance details and risk assessment and also provides an audit trail to allow easy compliance.
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Hey everyone, this is my submission for the @Rive HMI Challenge: NEURAL ROBOT CONTROL.
This project explores a system where a human can remotely operate a maintenance robot inside a radioactive reactor core — an environment too dangerous for direct interaction. The robot acts as a physical extension of the operator.
The experience is built as a clear flow:
connection → system sync → diagnostics → navigation → scan → operation
The AR interface serves as the main point of view, overlaying essential information directly onto the environment. It shows navigation guidance, target location, and system feedback while keeping the view minimal and unobstructed.
The robot includes a self-diagnostic HUD for maintenance teams, displaying real-time data such as battery, CPU temperature, mechanical stability, and radiation exposure. It also features a small onboard display that communicates system states like connecting, loading, warnings, errors, and movement direction.
The focus of this project is how motion communicates system behavior. Each transition reflects a state change, helping users understand what the robot is doing at any moment.
Built using State Machine, Data Binding, and Layout in Rive.
so good!! 🔥
WIP. Here's what I'm working on.
My original idea was to simulate 3-5 different car HMIs that each represented a diferent era. While gathering references I decided to focus on how speed was represented, what made you feel it. Because I wanted to create a driving simulator webapp where the user could really feel the adrnaline of going fast.
With a better idea about the history of HMIs and representation of speed, I wanted to challenge myself a bit more by creating a scenario for a future where only EVs are allowed (so bye-bye cool tachometers!) and where autonomous agentic driving is the new norm. I put myself in the shoes of someone that has nostalgia about the past, the thrill of driving a car yourself and the adrenaline of going fast.
Agentic driving (when you choose to take over the autonomous mode) would allow all drivers the ability to go ultra fast, safely, so you can arrive earlier to your target. So I focused on this journey: how to go from autonomous driving mode to manual mode and add a bit of gamification to the driving manually experience. The idea was to design 3 different manual driving modes: Analog Soul (essence of sports 70s and 80s cars), Digital Craze (the frenzy of flashy digital 80s and 90s car clusters) and Smart Drive ( 2030s. How does it look like to drive alongside an AI assistant?).
For now, I have the autonomous driving state and the Analog Soul. I'm working on the transition between them. I don't think I'll be able to have the 4 different screens for the end of the challenge, but let's see. I wasted way too much time on the design phase... 🥲
I'll be posting the webapp at the last minute... I'm learning along the way. First time using Rive and, so far, I'm loving it! I don't find it very complicated, the tutorials from their site are great.
Most teams we work with want to say their product is AI-powered. Few have asked whether their organisation is ready to support that claim.
We have observed a recurring pattern where a team commits to building a machine learning system and, after months, discovers that their data isn't clean enough to train on. The infrastructure then fails to reliably serve a model.
In such cases, our diagnosis has often concluded that the foundation is the problem. Machine learning readiness is a precondition, and one must begin with a straightforward question - "Do we have the data pipeline, infrastructure and cross-functional alignment to make this sustainable beyond a PoC?"
That question is what prompted our first article titled "The Machine Learning Readiness Checklist".
We cover everything a team needs, from data and infrastructure to the right kind of team members. All of this must be done before committing serious engineering time and budgets.
If you're a founder, engineering lead or a decision maker within your organisation who wants to build things correctly, we encourage you to read the full piece here:
https://www.algorithmic.co/blogs/the-machine-learning-checklist
We'll be sharing more each week. Follow along if this is relevant to what you're building.
P.S. This article is from late 2025 but more than ever relevant.
Love the vibe of this!
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