CANIAS ERP data is prepared for OpenAI and the generated output returns as reviewable records, so an AI capability can sit on top of the ERP without anything ever writing to it unchecked.
The appealing version of this is an AI assistant that answers questions about the business. The risky version is a model with a direct line into the ERP. CANIAS ERP holds the operational record, and a generated response is plausible text, not a verified fact, so writing one straight back means confident nonsense entering the system that runs production. Connecting OpenAI and CANIAS ERP through Alumio keeps the two apart deliberately: prepared, filtered context goes out, generated output returns as a proposal for review, and every exchange is recorded.

Generated output lands as a proposal against the relevant record rather than an update, so a person approves anything that reaches the system running the business.
Each flow sends a defined field set, so operational context reaches the model while commercially sensitive detail stays inside CANIAS ERP where its permissions apply.
Prompts and responses are logged with their content, so a question about why a suggestion was made can be answered from the record rather than reconstructed afterwards.
Because context is prepared rather than raw, output quality improves while the ERP keeps the boundary that stops a plausible answer becoming an operational fact.
Alumio assembles the relevant CANIAS ERP records into a prepared payload before the request is made, so a generated answer is grounded in current operational data rather than in whatever the model infers from the question alone.
Generated content is written against the related CANIAS ERP record as a suggested value awaiting review, so somebody with the authority to accept it does so knowingly rather than discovering it already applied.
Field-level rules determine exactly which ERP attributes form part of a given payload, so cost, margin and customer identifiers can all be excluded while the model still receives more than enough context to be useful.
Alumio sits between sales channels and fulfillment systems as a governed integration backbone. Orders are routed, transformed, and validated, while status updates return to every channel.
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Once your first integration is live, adding your ERP, PIM, WMS, or CRM connects to the same hub. Existing flows keep running. No rebuilding from scratch.
More can be connected, and a document or knowledge store is the usual addition, because an ERP holds transactions while the procedures and specifications that explain them sit elsewhere. Alumio assembles context from both, which is what moves output from summarising numbers to answering the question somebody actually asked.
Yes. Alumio connects to CANIAS ERP through its available interface, as it would any reachable system, assembles the payload from the fields you have approved, and handles the request. Responses return through the same governed path, so nothing reaches the ERP except as a record marked for review by a person.
Payload assembly, field rules and response handling are configured in the Alumio interface, which takes the place of the scripts that AI pilots accumulate around them. ERP data models are individual, and prompt structures change as the use case matures, so the Code Transformer covers shaping a payload or parsing a response into something the ERP can hold.
Only after a person has accepted it, and only for fields where being wrong is recoverable. A suggested description or category is a reasonable candidate; a price, a quantity or anything driving production is not. Alumio writes output as a proposal rather than a value, which keeps that decision with someone accountable for it instead of with the model.
No half-formed response is written to the ERP. Alumio captures each prompt and response with its content, monitors the flow in real time, and notifies you as soon as a request or a write is refused, showing the record and the reason. Unattended retries handle transient faults, and anything unresolved is held in the queue rather than being silently discarded.
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