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It can find information, prepare a record or use tools within a defined scope. The right choice depends on the work to be done: a rule or a workflow is sometimes enough. Understand the differences.

Not necessarily. Saphan looks at the possible connections with your software, its APIs and its exports. The aim is to keep what works and to deal with the handoffs that require too much re-entry. Prepare an integration.

Installation on a workstation or on company infrastructure is one of the services offered. The model, the hardware, the access and the maintenance are chosen according to the real workload. See the steps.

Yes, a hybrid architecture can split the tasks. You need to define which information may be sent, to which service and for what purpose. “Hybrid” is not in itself a guarantee of confidentiality. Compare the options.

We first look at your equipment and at the models suited to the need. Memory, context, concurrency and expected response time matter as much as the name of the hardware. Size it before buying.

Retrieval-augmented search can pull out the useful passages and give them to the model together with their sources. Access rights, versions and questions without an answer need to be tested. Study a corpus.

Document search and training are two different processes. How the data is used and which providers are involved are defined before the project. Search does not require changing a model’s weights. RAG, fine-tuning and memory.

Adapting an existing model can be considered for a targeted behavior, using authorized examples and tests kept separate from the training. It is not the same as creating a foundation model from scratch. The levers of customization.

Sometimes a shared method, a better-prepared source or a way to get the result into the right tool. A workshop on a use case may be enough before considering a new integration. Support how it is used.

It exposes data or functions to an AI application through a common protocol. The access rights and the permitted actions remain to be defined. MCP explained simply.

The two can be combined. Observation on site is useful when the need depends on hands-on tasks, equipment or several teams. The arrangements are agreed according to the project. Engineering embedded in the field.

Starting from a scope: task, data, software, access, deliverables and constraints. The price and the steps are agreed before work starts. Hardware purchases, external services and maintenance needs are listed separately where they exist. Present the project.

We compare similar cases through to the accepted result, including preparation, correction and re-entry. Generation speed alone is not enough. A measurement method.

This is prepared with interfaces, exportable formats, documentation and replacement tests. Technical compatibility does not guarantee the absence of regressions. Prepare a switchover.

No. It transforms examples using rules that run in your browser, with nothing sent to a model. It illustrates the connection between input, checking and export. Try it.