AI Workflow Automation SaaS Platform
Budget: $250 – $750 USD
I’m building a multi-tenant, white-label SaaS that reimagines service management through AI. The immediate focus is on the workflow automation and knowledge management modules, so I need an engineer or small team that can take these two pillars from concept to production-ready code while keeping the rest of the platform architecture extensible.
Core scope
• Workflow automation: drag-and-drop builder, branching logic, SLA timers and AI-driven task recommendations.
• Knowledge management: versioned articles, smart search, semantic tagging and feedback loops that train the AI.
• Incident & asset tracking hooks: the data model and APIs must already support real-time tracking, detailed reporting and smooth hand-offs to external systems.
• Integrations: first-class connectors for CRM systems, email services and popular project-management tools.
• Security & tenancy: role-based access, isolated tenant data, SSO (OIDC/SAML) and audit logging baked in from day one.
Tech snapshot
Prefer modern, cloud-native tooling (TypeScript/Node or Python, containerised micro-services, PostgreSQL, Redis, message queue, Terraform/Kubernetes). If you have a different stack that delivers the same scalability and speed, I’m open to it. AI components can leverage OpenAI or similar LLM APIs; vector search through Pinecone or an equivalent is welcome.
Acceptance criteria
1. Functional, tested workflow-automation and knowledge modules deployed to a staging environment.
2. API documentation (OpenAPI) plus quick-start Postman collection.
3. Working connectors for one CRM, one email service and one project-management tool.
4. Secure, multi-tenant architecture review signed off after penetration test.
5. Clean hand-over: source code, CI/CD scripts, infra as code.
If you’ve shipped SaaS platforms at scale and can demonstrate expertise in AI-augmented workflows, let’s talk timeline and milestones.
Core scope
• Workflow automation: drag-and-drop builder, branching logic, SLA timers and AI-driven task recommendations.
• Knowledge management: versioned articles, smart search, semantic tagging and feedback loops that train the AI.
• Incident & asset tracking hooks: the data model and APIs must already support real-time tracking, detailed reporting and smooth hand-offs to external systems.
• Integrations: first-class connectors for CRM systems, email services and popular project-management tools.
• Security & tenancy: role-based access, isolated tenant data, SSO (OIDC/SAML) and audit logging baked in from day one.
Tech snapshot
Prefer modern, cloud-native tooling (TypeScript/Node or Python, containerised micro-services, PostgreSQL, Redis, message queue, Terraform/Kubernetes). If you have a different stack that delivers the same scalability and speed, I’m open to it. AI components can leverage OpenAI or similar LLM APIs; vector search through Pinecone or an equivalent is welcome.
Acceptance criteria
1. Functional, tested workflow-automation and knowledge modules deployed to a staging environment.
2. API documentation (OpenAPI) plus quick-start Postman collection.
3. Working connectors for one CRM, one email service and one project-management tool.
4. Secure, multi-tenant architecture review signed off after penetration test.
5. Clean hand-over: source code, CI/CD scripts, infra as code.
If you’ve shipped SaaS platforms at scale and can demonstrate expertise in AI-augmented workflows, let’s talk timeline and milestones.