Business AI for Enterprises: Productive Agents in 4 Weeks

Business Agents can be up and running in four weeks—without a rollout that takes years.

The first agent is not a proof of concept, but a process with measurable effects. Additional use cases will follow iteratively during ongoing operations—without having to start from scratch each time.

Existing integrations are reused, and the existing Simplifier infrastructure orchestrates the agents’ runtime and memory. This results in a faster ROI—not just after months of project work.

Every LLM you need—without vendor lock-in.

Simplifier is model-agnostic: For each use case, you choose the appropriate large language model—OpenAI GPT, Claude, Gemini, Azure OpenAI, on-premises models, or your own fine-tuned models.

The platform centrally orchestrates all agents, regardless of the underlying LLM. You remain flexible—both technically and economically.

Simplifier makes LLM costs transparent and manageable.

You can see in real time:

• Which agent incurs which costs?
• Which tokens are used by which use cases?
• How do costs change over time?

Simplifier also measures adoption: Are the agents being used? Are they creating value? Or is an agent just burning through the budget without being productive?

This transparency enables you to operate Business AI cost-effectively—not just to test it.

Business AI is effective in the field—Build AI helps developers create.

Business AI agents are AI systems that autonomously execute operational business processes: verifying orders, approving invoices, comparing suppliers, and prioritizing service tickets. They make decisions as part of day-to-day operations—not as tools for developers, but as digital employees within the process.

Build AI, on the other hand, helps developers create applications, workflows, or tests. It accelerates software development, not business processes.

With Simplifier, you can use both: Business Agents for the business unit and Agentic AI in the Agent Builder for development.

Business AI makes autonomous decisions—RPA follows rigid rules.

The key difference:

RPA (Robotic Process Automation):

• Follows fixed “if-then” rules
• Aborts if there are discrepancies
• Requires fully defined processes
• Screen scraping instead of native integration

Business AI (Agentic AI):

• Makes independent decisions within defined limits
• Adapts to variations (e.g., different supplier formats)
• Learn from context and available data
• Native Integration with SAP, ERP, and CRM

Example:
An RPA bot stops when a supplier sends a new invoice format. A Business Agent still recognizes the relevant information, extracts the amount and date, and initiates the approval process.

Simplifier combines both: structured workflows for clear processes and business agents for the decision-making logic in between.

Business Agents use the same integration layer as your existing Simplifier applications.

No separate AI integration is required. If Simplifier is already connected to your SAP system (OData, RFC/BAPI, BTP), Business Agents can use these connections directly.

Specifically:

• SAP S/4HANA: OData Services, CDS Views
• SAP ECC: RFC/BAPI Integration
• SAP BTP: Optional as an integration layer (but not required)
• Non-SAP systems: REST/SOAP APIs, databases (JDBC/ODBC)

The Agent Builder integrates agents into the existing integration layer—you don’t have to build anything from scratch; you use what’s already there.

Existing permissions, security policies, and governance rules remain in effect. The Business Agent operates with the same access rights as a human user.

You define limits, rules, and escalation paths—the agent operates only within these guidelines.

Four control mechanisms:

1. Task Definition: You specify what the agent is allowed to do and what it is not allowed to do
2. Business Rules: Thresholds, Approval Limits, Validation Criteria (e.g., “Escalate orders over €5,000”)
3. Traceability: Every agent decision is logged—who, when, why
4. Escalation: In cases of uncertainty or borderline situations, the agent escalates the issue to a human

Shopping Example:

• Agent reviews purchase requisition < 2,000 € → automatically approved
• 2,000–5,000 € → Supplier comparison + approval if it’s the lowest-priced provider
• > 5,000 € → Escalate to the purchasing manager

You remain in control. The agent assists with routine tasks but does not make autonomous decisions on strategic matters.

Your data stays where you want it—on-premises, in the EU cloud, or in a hybrid environment.

Data Sovereignty by Design:

• On-Premise: Simplifier runs on your infrastructure; no data is transferred outside your organization
• EU Cloud: Hosting in Frankfurt or Amsterdam, GDPR-compliant
• Hybrid: Runtime on-premises, development in the cloud

LLM Data:
The Business Agents use Large Language Models (e.g., OpenAI, Claude, Azure OpenAI). You control what data the LLM sees:

• Option 1: On-premises LLMs (e.g., your own Azure OpenAI instance in a data center)
• Option 2: Cloud-Based LLMs with Data Processing Agreements (DPAs)
• Option 3: Simplifier anonymizes/pseudonymizes data before calling the LLM

Important: The LLM can only see what you choose to share. SAP master data, customer names, and amounts can be masked.

As a German company (headquartered in Würzburg), data protection and GDPR compliance are non-negotiable for us.