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Mechanical engineering · AI and agents

Gemini Enterprise in sales: reviewing tenders and preparing quotes in mechanical engineering

A machine builder with around 1,200 employees, about 100 of them in sales, introduces Gemini Enterprise for the sales team. One agent reviews incoming tenders and flags requirements, risks and open questions; a custom agent on the Gemini Enterprise Agent Platform creates a first draft quote from configuration and SAP data. The project plan allows around four months.

  • Sales organisations with many tenders and technical quotes
  • Companies with Microsoft 365 and SAP that want to use AI without switching platforms
  • Management teams that want AI with measurable benefits instead of isolated pilots

Why does a machine builder use AI in sales?

Every week, the sales team responds to several tenders and prepares technical quotes. Much of the time goes into reading extensive specifications, checking them against the company's own product range and gathering prices and delivery times from SAP. Management wants to win back this time. What matters is not another AI pilot but noticeable relief in day-to-day business. At the same time, Microsoft 365 and SAP are to stay as they are.

Knowledge is scattered.

Previous quotes sit in SharePoint, customer data in the CRM, prices and configurations in SAP. Anyone preparing a quote searches several systems at once.

Approvals stay with people.

Every quote has to be checked technically and commercially before it goes out. AI is meant to prepare, not decide.

Project profile

1,200
Employees
around 100 people
Sales
Mechanical and plant engineering
Industry
Microsoft 365, SAP, CRM
Systems
several per week
Tenders
Technologies used
Gemini EnterpriseGemini Enterprise Agent PlatformAgent DesignerAgent Development KitGemini and Claude
Approach

How do Gemini Enterprise and GEAP work together?

Gemini Enterprise is the workspace for the entire sales team. It searches SharePoint and the CRM with the existing permissions, and SAP data is added via a custom connector. This lets it answer questions about previous quotes, customers and products. A tender agent, built without code in Agent Designer, reviews new specifications, checks them against the product range and flags requirements, deviations and open questions.

A toolkit isn't enough for preparing quotes: this needs configuration rules, prices and delivery times from SAP. That's why a custom quote agent is built on the Gemini Enterprise Agent Platform. It creates a first draft quote, which sales checks and approves. For analysing very long documents, another model can also be selected in Model Garden, such as Claude from Anthropic.

01

Concept and architecture

Target picture for the AI platform: identities and permissions, access to SharePoint, CRM and SAP, governance rules, model choice and cost framework. Two weeks.

02

Use cases and data

Capture sales processes, measure the tender and quote workflow, clarify data sources and permissions. Two weeks.

03

Gemini Enterprise for sales

Connect SharePoint and CRM via ready-made connectors and SAP via a custom connector, pilot with 20 sales staff, training. Four weeks.

04

Tender agent

Built in Agent Designer, tested with previous tenders, then released to the entire sales team. Four weeks.

05

Quote agent on GEAP

Developed with the Agent Development Kit, connected to SAP, safeguarded with test cases and an approval step. Six weeks.

06

Operations and expansion

Monitoring, cost control and further agents, for example to prepare customer meetings.

Which technology is used?

Gemini Enterprise

AI workspace for the sales team with enterprise search across SharePoint, CRM and SAP data.

Agent Designer

Builds the tender agent without code.

Gemini Enterprise Agent Platform

Development, operation and monitoring of the quote agent.

Agent Development Kit

Development framework for the quote agent with SAP connection.

Gemini and Claude

Model choice per task via Model Garden.

Processing can be restricted to European Google Cloud regions; customer data is not used to train the models.

How can the benefit be measured after four months?

Starting point

  • Specifications are read and checked manually
  • Knowledge spread across SharePoint, CRM and SAP
  • Draft quotes are written from scratch every time
  • Microsoft 365 and SAP are a given

Measures after four months

  • Time from receiving a tender to deciding whether to submit a quote
  • Share of quotes that start from an agent draft
  • Weekly use of Gemini Enterprise in the sales team

Find out in 30 minutes where AI can relieve your sales team

  • Assessment of your starting point
  • Rough estimate of scope and duration
  • Next steps
Jonas Schwarz, CEO and co-founder of GMinds GmbH

Frequently asked questions