Restaurant operations platform with delivery and inventory integrations
Saipos is a restaurant-management platform serving Brazil's food-service sector with point-of-sale, inventory, and cash-flow tooling. The tech stack reveals a integration-first architecture: Node.js + Python backend on AWS (Lambda, SQS, ECS), PostgreSQL + DynamoDB for data, and heavy reliance on Zapier + n8n for third-party orchestration—50+ integrations with delivery apps (iFood, Rappi), digital menu systems, and loyalty platforms. Current hiring (4 ops, 3 sales roles) and project focus (CRM automation, messaging bot, funnel modeling) signal a shift toward customer success and growth, addressing internal pain points around onboarding ramp-up and retention.
Saipos builds restaurant-management software for mid-market food-service operators in Brazil. The platform automates core workflows: orders, inventory, cash handling, and digital menus. Differentiation comes from pre-built integrations with major regional delivery networks (iFood, Rappi, Aiqfome, Delivery Much) and a growing suite of adjacent tools (loyalty, BI, WhatsApp automation). With 51–200 employees, the company operates as a privately held entity founded in 2017. Recent hiring velocity and project roadmap indicate scaling toward operations and sales functions, alongside product work on CRM pipelines and customer onboarding.
Backend: Node.js, Python. Databases: PostgreSQL, SQL Server, DynamoDB. Infrastructure: AWS (Lambda, SQS, ECS, DynamoDB). Integration: Zapier, n8n, Workato. Frontend: Angular, Figma. Observability: Google Analytics, Pendo, Mixpanel. Customer tools: HubSpot, Zendesk, Intercom, WhatsApp.
Current projects include CRM integration automation, messaging bot development, observability for third-party integrations, growth roadmaps, funnel modeling, and cross-area business rule standardization. Focus areas address onboarding speed, retention, and identifying upsell opportunities.
Saipos | Sistema para Restaurante'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 →
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