Enterprise AI

Give AI assistants access to the facts, and keep people in charge of the decisions

An AI assistant is useful when it can see your business data and limited when it cannot. We build the governed connections between SAP, other enterprise applications and the AI tools your teams use, and we design each workflow around the decisions people need to keep.

Discuss Your ProjectSee two examples
How it fits together

From a business question to an AI-assisted workflow

Every integration follows the same path. The technology changes with your landscape; the order of the steps does not.

  1. Business question

    We start from a decision someone makes repeatedly and the information they need to make it.

  2. Data access

    We expose the relevant SAP data and actions through APIs with defined authorisations.

  3. Service layer

    Python services wrap those APIs as clearly described tools, using the Model Context Protocol (MCP) so an assistant can call them in a controlled way.

  4. AI-assisted workflow

    The assistant retrieves, compares, summarises and proposes. Predictions come from models served on a platform such as SAP AI Core.

  5. Human decision points

    People approve, reject or act. Business rules stay in the system of record, so an assistant cannot bypass them.

Capabilities

What we work with

We choose from these according to the engagement's environment, licences and security requirements.

Python services
Services that call SAP OData APIs, handle authentication, tokens and errors, and return clean results to other applications.
MCP integration
MCP servers that give an assistant specific, described tools: read operations, and write operations that require confirmation.
Claude
Assistants built with Claude that answer from your business data rather than from general knowledge.
SAP AI Core and AI Launchpad
Training, deployment and monitoring of machine learning models on SAP's AI platform.
Joule skills and agents
Joule skills and agents, where they are supported by the engagement's SAP environment.
Examples

Two workflows, described the way a business owner would ask

Demonstration

Supplier risk review

Business question

Which suppliers need attention before the next approval, and why?

Data inputs

Supplier records, open risk checks, risk scores and approval status from SAP.

Proposed workflow

The assistant retrieves supplier data through MCP tools, ranks suppliers by risk and explains the drivers behind each score.

Human decision points

A reviewer approves or rejects. SAP refuses approval while a critical check is open.

Supplier Risk Review
Demonstration

Delivery delay analysis

Business question

Which open orders are likely to arrive late, and what can be done now?

Data inputs

Open orders, order confirmations, delivery history and supplier data from SAP.

Proposed workflow

A model served on SAP AI Core predicts delay risk. The assistant explains the main drivers, compares similar orders and lists mitigation options.

Human decision points

Planners choose the mitigation. Nothing is changed in SAP automatically.

Delivery Delay Mitigation
Governance

Controls we design in from the start

Enterprise AI earns trust when its limits are clear. These controls are part of the design, not an afterthought.

  • Access through technical users with only the authorisations the workflow needs
  • Read tools kept separate from write tools, with writes confirmed by a person
  • Business rules enforced in SAP, not in a prompt
  • Credentials and secrets kept out of code
  • Changes recorded in the system of record
  • Model predictions validated on your data before any operational use

Which decision would you like to support first?

Tell us the question your team keeps asking and where the data lives. We will outline how an AI-assisted workflow could answer it in your environment.

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