Noel Desmarais
Applied AI Engineer
About
I’m Noel, a software engineer interested in how applied AI systems can solve real problems. I like taking new capabilities and stress testing them against actual workflows to see what actually holds up.
I work at Maven AGI, a Boston-based AI startup building enterprise AI agents for customer support. My work focuses on the systems and tooling that help teams design, configure, and ship production agent behavior more effectively. That includes projects like Agent Capabilities, but more broadly it sits at the layer where AI capability meets real organizational complexity.
Outside of work, I experiment with agent architectures, proactive behaviors, and the boundaries of what modern AI systems can reliably do. I’m especially interested in how agents integrate with legacy systems, operate within real constraints, and become more useful without becoming less predictable.
I’m also building Quotient, a receipt-scanning bill splitting app. It gives me a full-stack environment to apply the same ideas end to end, from messy inputs to reliable outputs.
Most of my work sits at the intersection of AI capability and practical execution—figuring out how to turn ambitious models into reliable, everyday software.
Projects
Selected Work
All[13]
Featured[5]
Web[4]
Cloud[4]
AI[3]
Embedded[1]
Earlier Work[8]
Featured
Maven AGI – Agent Capabilities
Built the self-serve tooling environment for Maven AGI customers to extend agent behaviors through custom actions and triggers.
TypeScript, React, API Design (REST), LLM Orchestration
Agent Capabilities is a self-serve build environment inside Maven AGI's Agent Designer that allows technical teams to quickly create, test, and deploy new agent behaviors. It gives organizations a structured, consistent way to define what agents do and when they do it—using custom actions and triggers. I contributed to building the tooling that makes this possible, enabling customers to extend their AI agents across connected systems without custom development or months-long engineering cycles.
Quotient – AI-Powered Bill Splitting
Built an AI-powered receipt scanning and bill-splitting app with automated Venmo links and SMS reminders.
LLM Orchestration, OCR & Document Parsing, PostgreSQL, Auth & OAuth, Tailwind CSS, Twilio
Quotient is a modern bill-splitting platform designed to eliminate friction in group payments. Users can upload or scan receipts, automatically extract line items using AI-powered OCR, and split expenses intelligently across participants. The system generates clean Venmo deep-links and optional SMS reminders to streamline repayment.
Built with Next.js 14, Supabase (Postgres + pgvector), and Tailwind CSS v4, Quotient leverages Google Gemini for receipt parsing and structured data extraction. The backend is designed for scalability and low-cost operation, with secure user authentication and a clean, mobile-first UI optimized for real-world usage. The goal is to create a frictionless financial coordination layer for everyday group transactions.
Kairos
Built a multi-agent AI planner that breaks down high-level goals into actionable calendar events.
LLM Orchestration, Multi-Agent Systems, Retrieval-Augmented Generation, Embeddings & Semantic Search, Vector Databases, Cloud Run
Kairos is an AI-powered weekly planner that intelligently breaks down your high-level goals into actionable calendar events. It leverages a multi-agent orchestration system—comprising fitness, nutrition, and scheduling agents—to generate personalized plans based on your preferences, constraints, and historical data. The front end is built with Next.js and Tailwind CSS for a responsive, glass-like UI, while the FastAPI backend handles agent coordination, RAG-based context retrieval, and integration with Google Calendar. Dockerized and deployed on Google Cloud Run, Kairos uses PostgreSQL with pgvector embeddings to store user profiles and plan references, ensuring scalable, secure, and privacy-focused time management automation.
All Projects
Fall Prevention Education iOS Application
Created an iOS application for the University of Vermont Medical Center to educate fall-prone individuals.
Swift
As part of a collaborative team of four, I contributed to the development of an iOS application for the University of Vermont Medical Center. The objective given to us by the medical professionals, was to educate individuals at risk of falling and empower them to adopt safer practices. Using SwiftUI, we blended design and functional factors to ensure the app's features were tailored to meet the needs of the end-users.
To enhance the app's functionality, we implemented efficient parsing of user data and leveraged locally-stored JSON files for seamless storage and retrieval. Throughout the project, we used an Agile approach, working closely with our team to manage workloads effectively and deliver high-quality results within the allocated semester timeframe.
RNTR - Apartment Rating Website
Created a platform that allowed renters to review their landlords and apartments to increase transparency in the rental market.
API Design (REST), PostgreSQL, Database Design
After going through the difficult process of finding an affordable apartment in Burlington, VT, I came up with an idea for a site that would help to hold landlords accountable in Burlington's captive rental market. I designed and implemented a website that enabled users to rate and review apartments and landlords. Leveraging my skills in HTML, CSS, PHP, and JavaScript, I developed both the front-end and back-end functionalities to ensure a seamless user experience. To enhance the accuracy and reliability of user-provided addresses, I integrated an address standardization API.
To maximize the website's functionality, I leveraged SQL to execute advanced queries on the underlying database, enabling users to search and filter through the vast repository of apartment and landlord data. Moreover, I devoted efforts to optimize server response times, resulting in improved website performance and enhanced user satisfaction. Additionally, I employed SEO best practices to increase the website's visibility and reach.
Unfortunatly this site is currently decommissioned due to funding limitations.
SG-FECC 2023 Website
Redesigned and implemented an SEO-optimized website, resulting in an estimated 300% increase in page-visits.
As the Senior Media & Communications Chair for the Schlesinger Global Family Enterprise Case Competition, a prestigious global business competition focused on family businesses, I identified the need to enhance our online presence. With participants from 27 countries over the past decade, it was crucial to develop a website that accurately represented the competition's scale and prominence.
Drawing on my web development experience, I quickly mastered WordPress to create a professional and visually appealing website for the current year's competition. This ensured both immediate impact and long-term maintainability, providing a seamless user experience for participants, judges, coordinators, and visitors. I also implemented effective search engine optimization (SEO) techniques, resulting in an estimated 300% increase in website traffic. By optimizing the website's search engine ranking, we significantly expanded the competition's global reach and recognition.
In addition to the website I came up with new ways to promote the competition and engage participants.
Evolutionary Robotics Simulated Robot
Optimized a simulated robot for locomotion in diverse environments using evolutionary algorithms.
Python
Under the guidance of Professor Josh Bongard, I worked on a project to evolve the locomotion capabilities of a simulated robot in various environments. Through evolutionary algorithms, I evolved a neural network to achieve optimal performance. Throughout the semester, I explored different strategies and techniques to continually improve the robot's adaptability and efficiency.
Custom Compiler
Developed a simple compiler enabling translation of high-level programming language into x86 assembly language
Python
For my final project in the course on compiler design and construction, I focused on extending the language's capabilities by implementing simple dataclasses. This project was a culmination of the various topics covered throughout the course, which provided a comprehensive understanding of the compilation process.
Throughout the semester, I explored the representation and analysis of code and how to translate a high-level programming language into Intel x86 assembly language. We learned about essential concepts such as register allocation, static type checking, and handling mutable data. The course also emphasized the significance of garbage collection for efficient memory management in compiled languages.
By extending the language with dataclasses, I not only demonstrated my proficiency in implementing new language features but also showcased my grasp of the entire compilation process. This project solidified my knowledge of code analysis, code generation, and the crucial role of language features in creating powerful and expressive programming languages.
Automated Plant Care System
Using Arduinos, sensors, and pumps connected via Bluetooth, created a device to keep plants watered and fertilized.
C++, Python
After failing to keep my plants alive, I used an Arduino Nano and an ESP8266 module, along with sensors for soil moisture, light, and air quality, to automate the process of watering plants and create a more nurturing environment.
By continuously monitoring important factors like soil moisture, light levels, and air quality, my system provided valuable insights into the specific needs of my plants. With the help of a peristaltic pump, the system automatically watered the plants, ensuring they received the appropriate amount of water at the right times.
This project allowed me to explore the integration of hardware components and programming skills, resulting in a practical solution to improve plant care. In the end, I have less dead plants!
Plants vs. Zombies Recreation
Collaborated with a small team to recreate the game Plants vs. Zombies
Python
For this project, I collaborated with an agile team to recreate the immensely popular game, Plants vs. Zombies, using Python and the Python Arcade library. As part of the team, I played a pivotal role in organizing the program flow and backend development. I focused on designing the structure of the game, ensuring seamless transitions between different states and screens. Additionally, I took charge of implementing the spawning mechanics for zombies, creating algorithms that combined randomness with the timing patterns seen in the original game. This resulted in a challenging and engaging gameplay experience.
Another significant contribution I made was developing a dynamic system for level progression. I designed a flexible framework that allowed for the easy addition and modification of levels, accommodating three waves of zombies per level, each increasing in difficulty. This approach added replayability and a sense of progression for players. By working on this project, I enhanced my skills in game development, algorithm design, and collaboration.
Enigma Recreation
Developed a fully-functional version of the WWII German encyphering machine Enigma in Python.
Python
As my first open-ended project during my Introduction to Programming course, I had the ambitious goal of recreating the WWII enciphering machine, Enigma, using Python. Despite being new to programming, I wanted to challenge myself.
Through self-guided learning, I successfully developed a fully functional version of Enigma. Going beyond the original scope, I even added an additional feature that allowed for the translation of enciphered messages into Morse code. This project not only showcased my ability to tackle complex programming tasks but also demonstrated my dedication to expanding my skill set and exploring innovative solutions.
