AI-powered procurement platform automating sourcing workflows and spend analysis
Fairmarkit operates a procurement automation platform built on a modern TypeScript + Python + Kafka + PostgreSQL stack, designed to handle high-volume sourcing events at scale. The tech foundation—event streaming via Kafka, async task processing via RabbitMQ, and containerized deployment on AWS EKS—supports the core claim of processing 10x more sourcing events per buyer. Hiring velocity is accelerating across engineering and sales with leadership-level openings, suggesting the company is scaling to address pain points around cycle-time reduction and revenue expansion from existing customers.
Notable leadership hires: Sales Director, Account Director
Fairmarkit builds an AI-powered sourcing platform for procurement teams at mid-market and enterprise organizations. The product automates competitive sourcing workflows, helping buyers manage supplier interactions, RFQ processes, and spend analysis at scale. The company was founded in 2017 and is headquartered in Boston, with 51–200 employees. Engineering is built on Angular and TypeScript for the frontend, Python/Django for the backend, with infrastructure on AWS (EKS, RDS, ElastiCache). Active projects include notifications systems, admin panels, customer advisory boards, and integration with ERP systems—reflecting both platform maturity and the complexity of connecting to legacy enterprise software.
Frontend: Angular, TypeScript, RxJS, Angular Material. Backend: Python, Django, FastAPI. Data layer: PostgreSQL, Redis, Elasticsearch, Kafka. Infrastructure: AWS (EKS, RDS, SES, SQS), Kubernetes, Docker, Vault.
United States, Poland, Ireland, and United Kingdom.
Fairmarkit'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.