An architecture designed to connect AI to the information system
FieldFlow AI aims to bring together artificial intelligence, automation, APIs and operational data in a progressive architecture that integrates with existing systems.
Adding intelligence without rebuilding the entire information system
FieldFlow AI is designed as an intelligent layer able to complement an existing information system rather than systematically replacing it.
ERP, CRM, business applications, APIs and databases can stay at the core of an organization. The platform can step in to connect information, automate certain flows and progressively add artificial intelligence capabilities.
- Progressive integration with the existing information system
- API-first architecture with decoupled components
- Automation controlled by business rules
- AI used on precise operational use cases
An architecture organized around six pillars
Each component serves a precise role: understand, automate, integrate, store, secure and evolve the platform.
AI components can assist with understanding requests, extracting information, classification and decision support.
- Analysis of business requests
- Extraction of structured information
- Classification and qualification
- RAG and business assistants where relevant
Workflows can orchestrate the different steps between a request coming in and its operational handling.
- Workflow triggering
- Business rules
- Synchronization between services
- Reduction of repetitive tasks
FieldFlow AI is designed to communicate with the applications already in place within the company.
- REST APIs
- ERP and CRM
- Business applications
- External services and webhooks
Business data stays structured and usable by operational services and AI components.
- PostgreSQL
- Intervention data
- History tracking
- Search and contextualization
Security is part of the architecture from the design stage, in particular for APIs, access and data exchanges.
- Authentication
- Access control
- API security
- Exchange traceability
The architecture aims to allow the progressive addition of new workflows, integrations and features.
- Modular services
- Progressive evolution
- Separation of responsibilities
- Industrialization
From applications to AI workflows
The goal is to keep clear responsibilities between the interface, business services, AI components and external systems.
- ApplicationsWeb, mobile and business tools
- API & integrationConnection to existing systems
- Business AIAnalysis and contextualization
- WorkflowsProcess orchestration
- DataOperational information
- Enterprise ISERP, CRM and external services
A platform designed for a professional environment
A useful enterprise AI integration must also account for security, access management, traceability and the evolution of the architecture.
Authentication
Secure management of identity and access to applications and APIs.
Access control
Features and data can be restricted based on roles and responsibilities.
Exchange protection
Interfaces between services are designed to limit unauthorized access and protect data.
Controlled deployment
The architecture can evolve toward containerized, industrialized environments.
A question about the project’s architecture?
Technical choices, integrating AI into an information system, APIs or security: I am happy to share the approach taken on FieldFlow AI in a technical discussion.