Marketing

AI Business Assistant for Enterprise Systems

Sales, customer service and operations teams often need information spread across CRM, SAP/ERP, product documentation and internal procedures. We built an AI assistant that combines RAG with controlled business APIs, giving employees a natural-language interface to enterprise data and selected business processes.

AI Business Assistant for Enterprise Systems

One natural-language interface

Employees can describe what they need without navigating every system separately.

Controlled integrations

Predefined tools connect the assistant to authorised enterprise information and operations.

Auditable workflows

Permissions, confirmations and tool calls can be monitored and logged.

The controlled AI solution

The Challenge

A typical business request can require information from several systems.

For example, a sales employee may need to check:

  • Customer information in the CRM
  • Order status in SAP
  • Product availability in ERP
  • Product documentation
  • Pricing rules
  • Delivery conditions
  • Internal procedures

The information exists, but employees need to know which system contains which piece of information and navigate several interfaces to retrieve it.

The goal was to introduce AI as a new interface to these existing systems without giving the language model unrestricted control over business data or operations.

Enterprise assistant architecture

We combined three capabilities:

  1. Knowledge retrieval
  2. Business-system integration
  3. Controlled AI actions

The AI does not become the source of truth. SAP, ERP and CRM remain responsible for the underlying business data, while the AI provides the natural-language interface.

Business operations are exposed through predefined tools such as getCustomer(), getOpenOrders(), getOrderStatus(), getProductAvailability() and getDeliveryInformation(). This provides a controlled alternative to allowing an LLM to construct arbitrary database queries.

Permissions, human confirmations and individual tool calls are monitored and logged, so it is always possible to trace which data and operations the assistant worked with.

The controlled AI solution

Key features

1

Natural-language access to enterprise information

2

RAG for documentation and business rules

3

Controlled APIs for enterprise integrations

4

Permission-aware access to data and tools

5

Human confirmation before consequential actions

6

AI observability for retrieval, tools and errors

Result

Before

A sales employee handling a customer request had to open the CRM for customer details, SAP for the order status and ERP for product availability, and then look up documentation, pricing rules and delivery conditions as well. They also had to know which system held which piece of information.

After

An employee describes what they need in natural language, and the assistant calls the required predefined tools, for example for customer details, open orders or product availability, and adds documentation and business rules through RAG. SAP, ERP and CRM still supply the data, within the user's permissions. Before consequential actions, the assistant asks for human confirmation.

Key outcomes

  • Reduced time spent navigating multiple systems
  • Simpler access to business information
  • Faster sales and customer-service workflows
  • Reduced repetitive searches
  • Automation of selected administrative tasks
  • Unified interface across multiple enterprise systems
  • Foundation for further AI workflow automation

Key takeaway

The central architectural decision was to keep a strict separation between AI reasoning and business-system authority. The AI can determine what information or operation is needed, but predefined APIs, authorisation rules and application logic determine what the system is actually allowed to do.

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Frequently asked questions

Which enterprise systems can the AI assistant access for a business request?

The assistant works with CRM, SAP and ERP and, through RAG, with product documentation, business rules and internal procedures. It does not have unrestricted access to enterprise systems; it uses only predefined tools and controlled APIs. Which data and tools it may use for a given request is determined by the user's permissions, and consequential actions must also be confirmed by a person.

How are SAP, ERP and CRM kept as the source of truth?

In this solution the AI is not a data source but a natural-language interface. SAP, ERP and CRM continue to manage the underlying business data, and the assistant retrieves it through predefined tools for every request. The AI only determines which information or operation the user needs; what the system is actually allowed to do is decided by APIs, authorisation rules and application logic.

Why are actions exposed as predefined business tools?

Predefined tools such as getCustomer(), getOpenOrders() or getProductAvailability() are a controlled alternative to letting a language model construct arbitrary database queries or operations. Each tool has a clearly defined function and is subject to the user's permissions. Consequential actions also require human confirmation, and tool calls are monitored and logged, so the assistant's work remains auditable.

Which order and product questions can the assistant handle?

The assistant can handle requests such as customer details, a customer's open orders, the status of a specific order, product availability or delivery information. It retrieves this data through predefined tools connected to CRM, SAP and ERP. Questions about product documentation, business rules and internal procedures are answered through RAG, always within the permissions of the user asking.

Technologies

Large Language Models Retrieval-Augmented Generation AI Tool / Function Calling SAP ERP CRM REST APIs Vector Search Document Processing Vue TypeScript PHP OpenTelemetry Application Monitoring