AI-driven workforce compliance platform: document parsing, automated insurance validation, and autonomous background check orchestration - ON-1195

Genre de projet: Innovation
Discipline(s) souhaitée(s): Génie - informatique / électrique, Génie, Informatique, Sciences mathématiques
Entreprise: Entuitive Workforce Inc.
Durée du projet: 6 mois à 1 an
Date souhaitée de début: Dès que possible
Langue exigée: Anglais
Emplacement(s): Toronto, ON, Canada
Nombre de postes: 1 - 3
Niveau de scolarité désiré: MaîtriseDoctorat
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: No

Au sujet de l’entreprise: 

Entuitive Workforce provides software that helps companies manage workforce compliance for employees and contractors.
The platform centralizes documents like certificates, licenses, and insurance, and tracks them automatically to ensure nothing is missing or expired. It streamlines onboarding by allowing workers to submit required documents digitally and ensures consistent compliance standards across all teams.
Entuitive Workforce helps reduce risk by flagging non-compliant or incomplete records, helping companies avoid safety issues, legal exposure, and costly fines. It also gives real-time visibility into workforce compliance status, making audits and reporting faster and easier.
By combining automation and AI-driven insights, the platform shifts companies from reactive compliance management to a more proactive approach.
Overall, Entuitive Workforce enables organizations—especially in compliance-heavy industries like construction—to save time, improve oversight, and confidently meet regulatory requirements.

Veuillez décrire le projet.: 

AI Document Parsing for Insurance Certificates
We plan to build an AI-powered document parser to extract structured data from Certificates of Insurance and other workforce documents. The goal is to convert diverse, unstructured PDFs into standardized fields like policy limits, expiry dates, and named insured parties. This project will involve OCR, layout-aware AI models, integration into our software and handling multiple document formats, providing students an opportunity to work on real-world document understanding challenges.

Automated Insurance Validation Engine
This project aims to develop an AI-driven validation engine that automatically checks extracted insurance data against compliance rules. It will flag missing coverage, incorrect dates, or policy discrepancies in real time. Students will explore techniques in rule-based validation, anomaly detection, and knowledge representation, applying AI to improve accuracy and reduce manual review in a regulatory context.

Autonomous Background Check Orchestration (A2A Protocol)
We aim to implement an agent-to-agent (A2A) protocol to autonomously trigger background checks based on workforce events, such as onboarding or document expiry. Students will work on designing event-driven AI workflows, secure API integrations, and autonomous decision-making systems that reduce human intervention while ensuring compliance and scalability.

Expertise ou compétences exigées: 

1. AI & Machine Learning
• Experience with NLP, OCR, and document understanding models
• Ability to extract structured data from unstructured documents
• Knowledge of validation, anomaly detection, or rule-based systems
2. Software Development / Full Stack Integration
• Strong programming skills: Python for AI/ML pipelines, JavaScript/React for front-end integration
• Experience integrating AI services into web applications (React, Laravel/REST APIs)
• Working knowledge of SaaS architecture
3. Data Handling & Automation
• Experience handling PDF/Excel/structured and unstructured data
• Designing event-driven workflows or autonomous systems (A2A protocols)
• Familiarity with APIs for third-party services (e.g., background check services)
4. Additional Skills (Nice-to-Have)
• Understanding of cloud services (AWS, GCP) for hosting AI pipelines
• Knowledge of CI/CD, containerization (Docker), and version control (Git)
• Strong problem-solving skills to optimize AI models for real-world compliance workflows