FEATURES
onflAI combines AI-guided configuration, visible validation and IOnbrix as its runtime. In the Studio or through MCP from ChatGPT, Codex and Microsoft Foundry: people and agents use the same integration platform. Credentials stay outside the AI context.
SCORECARD
An interpretation of typical approaches, not a ranking of individual vendors. Categories overlap: other platforms also offer AI, mapping, secure connections and approvals. What matters is how they work together for your business case; scope and implementation depend on the product and plan.
Scroll sideways on smaller screens. Choose the approach that fits your business case. When another approach fits →
| Your integration process | onflAI | Workflow Automation | Developer Automation | Enterprise iPaaS |
|---|---|---|---|---|
| Starting point | Describe a business outcome | Connect app events and actions | Design workflows with nodes and code | Model integration and API architecture |
| Role of AI | Create and validate configuration; not the production runtime | Assistance building automations, depending on product | Assistance and AI steps in customisable workflows | Development and operations assistance, depending on platform |
| Mapping & transformation | Visibly review and correct source/target fields and rules | Map and format data between actions | Nodes, expressions and code for data logic | Mapping tools and transformation logic |
| Checks before launch | Visible AI validation before human approval | Step and workflow tests | Tests and custom validation paths | Testing, deployment and governance processes |
| Human control | Deliberate approval before production activation | Review and approval steps depending on workflow | Control points in your own workflow | Roles and approval processes depending on edition |
| Credentials | Stored separately, processed with encryption, outside the AI context | Managed app connections; verify AI data paths | Credential management; verify custom nodes and AI data paths | Credential and policy management; verify AI data paths |
| Production operations | IOnbrix runtime with status, runs and error visibility | Workflow execution and history | Workflow execution with a customisable operating environment | Integration runtimes with monitoring and governance |
THE BACKBONE FOR YOUR AI
AI understands your goal and builds the integration. The dedicated IOnbrix runtime handles the approved data processing. onflAI complements your AI with reliable execution, efficient processing and less recurring token usage.
ONFLAI PLUGIN FOR OPENAI
Use ChatGPT and Codex as clients for your enterprise integrations. Through the MCP-based onflAI plugin, supported OpenAI surfaces access approved tools. Describe your source, target and rules in natural language.
„Connect Salesforce to our PostgreSQL database.“
onflAIWhich customer data should be transferred, and how often?
You can describe PostgreSQL → SAP in the same way. Approved system access and permissions determine the connection.
MICROSOFT FOUNDRY MCP INTEGRATION
Foundry Agents can use onflAI through MCP as an integration tool and runtime access point. They do not need to build their own mapping engine, database logic or file handling. onflAI configures the integration; IOnbrix executes it.
„Our process needs daily product information from system X.“
onflAIonflAI provides the required data through an approved API or database integration.
The agent decides what needs to happen. onflAI implements the integration within the approved actions.
OPTIONALLY WITH ABLEX
Automation accelerates errors, too. ableX adds Master Data Management, Data Quality and Data Governance: check structure, apply quality rules and preserve ownership. onflAI connects; IOnbrix executes.
„Synchronise new supplier data with the ERP.“
onflAICheck quality rules and approve critical changes before processing the data.
Example with ableX checks before execution. ableX is an optional architectural addition, not part of every integration or automatically included in every plan.
AFTER GO-LIVE, TOO
An agent can query status and errors, assess issues and prepare corrections. Changes go through validation and approval again, never around the controlled data process.
Describe the objective, source and desired outcome in business language. onflAI translates that intent into a technical, reviewable integration.
Business case: from web service to database →Fields, mappings, rules and error paths are built in a structured way while remaining visible and editable for people.
APIs, files and databases →A visible AI checklist examines the complete configuration and resolves identified gaps before approval becomes available.
See validation in the original product →AI prepares and validates. A person reviews the result and decides whether it is activated in production.
Understand control and approval →Secret credentials are stored separately, processed in encrypted form and never passed into the AI configuration.
How credentials remain separate →Proven IOnbrix building blocks execute the approved integration in a controlled way instead of handing production logic to an AI black box.
Explore IOnbrix as the runtime →Nested data and different formats need explicit rules: source and target fields, types, transformations and required values remain documented alongside their mapping. You review and correct the result before approval.
Mapping example: HubSpot → MySQL →Production status, run history and errors remain transparent to the responsible teams.
Monitoring and plan conditions →POSITIONING
onflAI focuses on the full path from a clear business requirement to an approved, monitored integration. The distinction is how business description, visible AI validation, human approval and the IOnbrix runtime work together – not a claim that other platforms lack AI or control.