Real-time property operations platform connecting owners, operators, and service providers
Plentific operates a marketplace-style platform for property operations with a modern Python + Django + PostgreSQL + React stack deployed on AWS/Kubernetes. The hiring mix is heavily engineering-focused (11 of 16 active roles) with senior and lead-level positions dominant, while projects reveal a dual effort: scaling transactional throughput (data pipelines, large-volume handling, payments) and embedding AI across the product surface (agentic tooling, RAG, ML features). The pain-point list mirrors the project roadmap — integrating AI, automating repetitive work, and expanding European coverage — suggesting engineering is tightly aligned with product direction.
Notable leadership hires: Service Delivery Lead
Plentific connects property owners, operators, service providers, and tenants on a single platform to streamline operations and drive tenant satisfaction. The company manages a network spanning 1 million+ properties and 20,000 service providers globally. The product surface includes a service marketplace, operational dashboards with data-driven insights, compliance tooling, and procurement. Plentific is based in London and was founded in 2012; it operates as a private company with 201–500 employees across UK, Germany, and Italy.
Plentific's core stack is Python, Django, PostgreSQL, and React on AWS infrastructure (Kubernetes, Docker). Data and automation layers include Celery, Redis, Elasticsearch, and integration platforms (n8n, Tray.io, Zapier). CRM and operations are powered by Salesforce, HubSpot, NetSuite, and Jira.
Current projects include contractor onboarding, ML-powered product features, service catalogue creation, spec-driven development adoption, agentic tooling improvement, AI integration strategy, European expansion, and online payments infrastructure.
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Plentific'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.