OpenAI 2026 hackathon

Employee portal for Ambulance Service

Customizable employee portal including a schedule system, clockin/out payroll system, employee tracking, certification tracking, and recertification management.

Solo project by Adam Kinsey · 0 likes · 0 comments

View the project on Devpost

Archive position — measured, not model output

0 likes on Devpost

2,263 of the 7,850 archived projects have more likes, and 5,587 share exactly 0 — so this project's #3,918 place in the like-ranked listing is a tie-break inside that group, not a ranking.

Projects (log scale)

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Likes on Devpost. ▲ marks this project's group.

Show the figures
LikesProjectsShare of archive
05,58771.2%
11,75822.4%
22843.6%
3–41321.7%
5–9751.0%
10+140.2%
Devpost like counts for all 7,850 archived projects, captured when this archive was built.

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Executive Summary

The description states that the "Employee Portal for Ambulance Service" is a self-hosted web application built with Node.js, Express, and PostgreSQL to manage employee workflows in EMS organizations. The author, Adam Kinsey, describes it as a configurable platform supporting onboarding, scheduling, payroll, certification tracking, learning management, and operational logs. It was developed using AI-assisted tools like OpenAI Codex and deployed via Docker Compose with Caddy for HTTPS.

The project appears to be an early-stage prototype or proof-of-concept built by one person (Adam Kinsey) in the context of a hackathon submission. There is no evidence of revenue, customers, or traction beyond the author's own account. The system is described as self-hosted and configurable but lacks any indication of adoption or market validation.

The single most important open question is whether this platform has been tested in real-world EMS environments and how it scales beyond a single developer’s use case.

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What The Product Actually Is

The description states that the portal is a "self-hosted web application built with Node.js, Express, and PostgreSQL" designed to manage employee workflows in ambulance services. It includes features such as:

  • Configurable onboarding processes
  • Role-based approval workflows
  • Certification and recertification tracking
  • Learning management system (LMS)
  • Scheduling and payroll systems
  • Document submission and review
  • Operational run logs and audit histories

The system uses Docker Compose for deployment, Caddy for reverse proxy and HTTPS support, and stores data in PostgreSQL. Uploaded documents and certificates are stored separately using managed storage solutions like S3-compatible storage or a backup server.

It is described as configurable so that different EMS agencies can define their own roles, onboarding phases, requirements, training programs, branding, and operational processes.

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Positioning & Claim Evolution

The description states that the project began as an employee onboarding tool but evolved into a broader "employee lifecycle and operations platform." The author claims it addresses fragmentation across paper forms, spreadsheets, emails, shared folders, and separate software systems in EMS environments.

It positions itself as a secure, practical portal designed around actual ambulance service operations. The author emphasizes that the system supports workflows from application through ongoing employment and includes features like automated backups, recovery tools, and mobile compatibility.

The claim evolution suggests an expansion from a narrow problem (onboarding) to a more comprehensive solution covering multiple aspects of employee management and operational workflow in EMS.

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Target Customer & ICP

The description states that the target customer is "ambulance services" or "EMS organizations." The system is described as being built around how an ambulance service actually operates, with features tailored for EMS-specific needs such as:

  • Onboarding processes
  • Certification tracking
  • Scheduling and payroll systems
  • Operational run logs
  • Vehicle, station, and equipment check sheets

The author notes that the portal is configurable so that different agencies can adapt roles, phases, requirements, training programs, branding, and workflows to their specific needs.

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Business Model & Pricing Evidence

Not evidenced. The description does not contain any information about pricing models, revenue streams, or business model details beyond the fact that it is a self-hosted application built by one developer.

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Technical & Delivery Signals

The description states that the portal is a "self-hosted web application" built with:

  • Node.js
  • Express.js
  • PostgreSQL
  • Docker Compose for deployment
  • Caddy for reverse proxy and HTTPS support
  • OpenAI Codex as an engineering collaborator

It uses role-based access control, stores workflow data in PostgreSQL, and keeps uploaded documents and generated certificates in managed storage outside the source repository. The system includes backup tools that can send copies to S3-compatible storage or a separately hosted backup server.

The author mentions using AI-assisted development throughout the project, including exploring codebases, designing database tables, building backend routes, debugging problems, creating validation scripts, improving mobile layouts, and documenting procedures.

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Traction & Maturity Signals

Not evidenced. The description contains no information about revenue, customers, user adoption, or market traction beyond the author's own account. It is described as a prototype built by one person in a hackathon context with no indication of real-world usage or scaling beyond the developer's own environment.

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Competitive Context

Not evidenced. The description does not mention any existing competitors or competitive landscape in the EMS employee portal space. No information is provided about other platforms or tools used by ambulance services for similar purposes.

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Open Questions & Risks

  • Single Developer Dependency: The system was built by one person (Adam Kinsey), raising concerns about scalability, maintenance, and institutional knowledge.
  • Self-Hosted Deployment Model: The description states the portal is self-hosted, which may limit adoption among organizations without technical infrastructure or expertise.
  • Lack of Market Validation: There is no evidence of customer feedback, user testing, or real-world deployment beyond the author's own experience.
  • AI-Assisted Development Risks: While Codex was used as a collaborator, there is no indication of quality control processes or verification of AI-generated code beyond manual testing.
  • Limited Feature Scope: The system appears to be focused on specific EMS needs and may not easily adapt to other industries or use cases without significant customization.

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Questions the description leaves open

  1. Has the platform been tested in real-world EMS environments?
  2. What is the actual adoption rate among users beyond the developer's own organization?
  3. How does the system handle data security and compliance requirements in regulated environments?
  4. What are the specific operational challenges faced by different types of EMS agencies using this platform?
  5. Can the system be easily deployed and maintained by organizations without technical expertise?
  6. What is the roadmap for expanding beyond the current feature set to support additional workflows or integrations?
  7. How does the system handle edge cases in scheduling, payroll, and certification tracking that are common in EMS operations?
  8. What are the long-term sustainability plans for ongoing development and maintenance?

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Overall Assessment

Not evidenced. The description provides no information about funding rounds, valuations, or investment interest beyond the fact that it was submitted to a hackathon. There is no evidence of commercial traction, revenue, or market validation that would support an investment or partnership decision at this stage.

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Source

Submitted to the OpenAI 2026 hackathon on Devpost. Project home on Devpost. Back to the full gallery on Devpost.

This competitive analysis was produced by top-founders.com (opens in a new tab)

Generated by a language model from this project's public Devpost page — its tagline, the author's own write-up and any built-with tags listed there — as captured in August 2026. Short quotations are the author's words. It is an opinion on the idea as a business, not on the people behind it, and it reflects the description as published then. If the project has moved on, request a correction below. The analysis is not independent research and contains no verified traction, revenue or customer data.

An independent guide by KFG International LLC — not affiliated with, endorsed by, or connected to Devpost or OpenAI.

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