Enterprise AI for financial, real estate, HR, and workforce decisions
Nakisa builds decision-intelligence and operational-planning software for large enterprises across finance, real estate, HR, and strategy. The stack reveals a hybrid-cloud, infrastructure-heavy architecture (AWS, Azure, GCP, Kubernetes, Kafka, Spring Boot, Vue) designed for regulated, multi-tenant environments at scale. Current hiring is operations-first (4 roles), followed by sales (3), signaling aggressive account expansion, while active projects emphasize AI-driven workflow adoption and compliance automation—addressing the company's stated pain point of compliance-heavy, audit-intensive workflows.
Nakisa is an enterprise AI software provider headquartered in Montreal, serving more than 1,100 clients and 6.6 million users across 135 countries. The product portfolio spans decision intelligence (agentic AI for faster, auditable decisions), lease accounting (IFRS 16 and ASC 842 compliance), integrated workplace management systems (IWMS), and workforce planning. The company targets finance, real estate, HR, and operations teams at large organizations. Built on a cloud-native microservices architecture (Kubernetes, Kafka, CI/CD), Nakisa is currently focused on reducing ticket volume, automating IT operations, and modernizing hybrid infrastructure while closing high-value contracts.
Nakisa runs on AWS, Azure, and GCP with Kubernetes, Kafka, Spring Boot, and Vue. Infrastructure includes Active Directory, Dynamics 365, VMware (vSphere/vCenter), and PowerShell/Python for automation. The stack prioritizes hybrid-cloud scalability and compliance at enterprise scale.
Nakisa has 201–500 employees and is headquartered in Montreal, Quebec, Canada. The company was founded in 1990 and is privately held.
Nakisa's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.