Summary
An AI quotation agent analyses enquiries, finds approved information, identifies gaps and prepares a traceable draft. People review the technical solution, decide on deviations and approve the quotation.
THE CENTRAL IDEA
A quotation agent replaces neither the sales engineer nor their judgement. It removes time-consuming preparation so that specialists have more time for customers, solutions and sound decisions.
Why quotation preparation becomes a time drain in midsized companies
In technical midsized companies, a quotation is much more than a price sheet. It translates an often incomplete customer enquiry into a technically suitable, commercially viable and contractually sound offering. Sales, engineering, costing and often other departments need to work together to achieve this.
The problem is not that employees make decisions too slowly. Too much time passes before they can make a decision at all. Information is scattered across ERP, CRM and document management systems, emails, product documentation and previous quotations. The right version must be found, checked for currency and transferred into the new case.
Typical time-consuming tasks include:
- manually extracting customer requirements from emails, attachments and specifications,
- searching for comparable quotations and projects,
- gathering product data, prices, lead times and text modules,
- identifying missing information and drafting follow-up questions,
- checking deviations from standard terms and approval procedures,
- repeatedly transferring information between systems and documents.
Experienced sales engineers in particular end up collecting information. Their most valuable skills — understanding customer needs, assessing technical alternatives and developing convincing solutions — come into play only late in the process.
KEY TAKEAWAY
The bottleneck often lies not in writing the quotation, but in finding, checking and combining the information it requires.
What is an AI quotation agent?
An AI quotation agent is a digital work assistant for preparing quotations. It combines language understanding with approved company sources and defined process rules. This enables it to understand an enquiry, research relevant information and prepare an initial quotation draft.
Unlike a standard text template, the agent responds to the specific case. Unlike a publicly accessible chatbot, it works with the company’s approved data, documents and rules rather than arbitrary information from the internet.
What the agent takes on
- Structuring incoming enquiries and documents,
- identifying technical and commercial requirements,
- flagging missing, contradictory or unclear information,
- finding similar previous quotations and projects,
- gathering approved product, service and master data,
- assembling suitable standard texts and quotation components,
- highlighting deviations, risks and required approvals,
- preparing a traceable draft with source references.
What remains with people
- Interpreting specific customer needs,
- selecting and assessing the technical solution,
- pricing strategy and negotiating scope,
- decisions about risks and exceptions,
- binding approvals and responsibility towards the customer.
A CLEAR DIVISION OF RESPONSIBILITIES
AI handles searching, sorting and preparation. People handle understanding, decisions and responsibility.
A realistic workflow: from enquiry to a draft ready for review
A typical example from machinery and plant engineering illustrates how a quotation agent works.
1. The customer enquiry arrives
A customer emails an enquiry with a specification, technical attachments and a requested delivery date. The agent assigns the documents to the case and identifies deadlines, contacts, requested services and special conditions.
2. Requirements and gaps become visible
The agent creates a structured list of requirements. Unclear or missing information, such as operating conditions, interfaces or acceptance criteria, is flagged. Instead of reading the documents manually several times, the sales engineer receives a prepared overview and can decide which follow-up questions are necessary.
3. Relevant experience is found
Using the approved sources, the agent searches for comparable quotations, projects and product configurations. It shows not only the result but also the case or document from which the information originated. This makes the basis of each suggestion traceable.
4. The draft is prepared
Current product data, approved service descriptions and available master data are incorporated into an initial draft. Individual assumptions, open questions and deviations are clearly marked. The agent does not invent missing facts; it identifies where clarification is needed.
5. Expert review and approval
The responsible employee reviews the solution, corrects technical details, adds the commercial strategy and initiates any necessary approvals. The quotation is sent only after this review. The final decision remains with a person.
Why a quotation agent delivers more than faster writing
The immediate benefit is the time saved on preparation and searching. But the economic effect goes further. A complete enquiry that is structured early reduces unnecessary follow-up questions. Deviations are identified before they are overlooked in the quotation. Customers receive a well-founded response sooner.
More capacity with the existing team
When recurring preparation is removed, the existing team can handle more enquiries effectively. This is particularly relevant when experienced specialists are scarce and hiring additional staff is not possible at short notice.
Higher and more consistent quality
Approved content, current documents and defined checks are taken into account more consistently. The agent helps prevent omissions without pre-empting professional decisions.
Faster responses to customers
An early, structured response conveys professionalism. Even if a full cost calculation still takes time, the customer can learn sooner which details are missing and when to expect a reliable quotation.
Knowledge becomes useful in everyday work
Corrections, approvals and documented decisions can serve as verified experience for comparable future cases. Knowledge therefore no longer remains solely in individual employees’ heads or in old quotations that are hard to find.
Which metrics a pilot should measure
Blanket promises such as “50 per cent faster” are not credible without understanding the process concerned. A good pilot therefore begins with a baseline measurement and then compares the same metrics.
- Handling time per quotation and per process step,
- time spent searching for information and transferring data,
- time to the first substantive customer response,
- number of enquiries handled by the existing team,
- proportion of complete cases at the first internal review,
- number of follow-up questions, correction cycles and data transfer errors,
- compliance with internal checks and approvals,
- employee acceptance and actual use.
Only on this basis can the company assess whether the agent saves time, improves quality and can be expanded in an economically sensible way.
Integration: the ERP remains the backbone
A quotation agent replaces neither ERP nor CRM. These systems remain the reliable source for master data, prices, customers, items, orders, permissions and defined processes. The agent complements them wherever information from multiple sources must be understood and combined for a specific case.
In a well-designed architecture, the agent reads only data that the respective user is authorised to access. Changes or binding actions take place under control and according to defined approvals. Wherever possible, the agent works in employees’ familiar working environment rather than creating another isolated system.
KEY TAKEAWAY
The ERP records reliable business data. The quotation agent connects it with documents, context and verified experience.
Security for sensitive quotation and customer data
Quotations often contain prices, margins, technical solutions, customer data and confidential project details. Security therefore cannot be added as an afterthought. It must be part of the solution.
The following principles are particularly important for maintaining data sovereignty:
- Processing and storage within the controlled company environment,
- self-hosted AI models for confidential content,
- no use of company data to train public models,
- adoption of existing identities, roles and access rights,
- logging of relevant sources, suggestions, changes and approvals,
- clear limits on automated actions and binding decisions.
For employees, this technical foundation should largely remain in the background. They need a reliable assistant in the quotation process, not another major IT project.
How to start with a manageable pilot project
The best pilot does not immediately cover every type of quotation. It focuses on a frequent process that can be sufficiently standardised, with engaged specialist users and clear success criteria.
- Select the pilot process. Define a product group, region or quotation type with sufficient volume and clear responsibility.
- Measure the baseline. Record handling times, search effort, errors and follow-up questions before starting.
- Limit sources and rules. Connect only the systems, documents, templates and approvals genuinely needed for the pilot.
- Involve employees. Sales and engineering jointly define which preparation the agent should take on and which decisions remain with people.
- Test with real cases. Review, correct and improve suggestions using real but controlled cases.
- Evaluate benefits and quality. Compare the agreed metrics before deciding on expansion or further integrations.
How to recognise a well-scoped pilot
- A clearly designated process owner is available.
- The number of document types and variants remains manageable.
- Results can be checked by experienced employees.
- Benefits become visible in everyday work within a few weeks.
- The pilot does not change binding core processes without approval.
The most common mistakes and how to avoid them
Too many variants in the first step
If the pilot is expected to cover all product lines, countries, languages and contract models at once, complexity grows faster than value. A clearly defined starting area followed by expansion is a better approach.
Expecting the agent to decide autonomously
Especially at the beginning, the system must provide suggestions, show sources and make uncertainty visible. Binding decisions about prices, risks and approvals remain with the responsible employees.
Asking employees only at the end
The best requirements come from the people who prepare quotations every day. They know where time is lost and which support actually helps. Their corrections make the solution work in practice.
Automating only text generation
A well-written draft does not solve the real problem if information still has to be found and checked manually. The greatest opportunity lies in structured preparation and controlled access to the right sources.
From a quotation agent to a learning organisation
A successful quotation agent does more than provide short-term relief. It establishes a reusable foundation: secure access, system connections, verified knowledge sources, roles, approvals and traceable decisions.
A complaints and warranty agent, for example, can build on this later. It connects the original customer promise with experience from delivery, operation and service. Findings from complaints can then inform future quotations.
KEY TAKEAWAY
The quotation describes what is promised to the customer. The complaint shows what became of that promise. Connecting the two makes experience available for the next decision.
Further agents can subsequently support contract review, order clarification, technical documentation, supplier enquiries or certificate checks. Each new use case must first demonstrate its own economic benefit. At the same time, a shared, secure AI foundation grows in the background.
Conclusion: enable good quotation work, not just automated quotations
Preparing quotations with AI does not mean handing responsibility to a machine. It means relieving qualified employees of work that unnecessarily ties up their expertise.
The quotation agent analyses, searches, structures and prepares. Sales and engineering understand, assess, decide and take responsibility. This clear division of work makes the starting point manageable and the benefits measurable.
For hidden champions, this represents a particular opportunity: they can make better use of their accumulated knowledge, respond to customers faster and deploy scarce specialists where human experience makes the greatest difference.
THE FIRST STEP
Do not begin with the complete automation of your sales organisation. Begin by asking which preparatory work your best employees should no longer have to do themselves for the next quotation.
Checklist: is quotation preparation a good AI pilot for your company?
- Do you regularly prepare technically or commercially demanding quotations?
- Do employees search multiple systems and documents for the information they need?
- Are similar contents frequently assembled or written again?
- Do missing details, follow-up questions or internal coordination delay the process?
- Can experienced employees reliably review a prepared draft?
- Can time, quality and turnaround be measured before and after the pilot?
- Should sensitive quotation data remain under full company control?
If you answer yes to several questions, quotation preparation is very likely to be suitable as a clearly defined first AI use case.
Frequently asked questions about preparing quotations with AI
Can AI create complete quotations independently?
Technically, it can prepare many of the components. In a responsible business process, however, technical assessment, pricing strategy, exceptions and binding approval remain with the responsible employees.
Does the ERP need to be replaced for a quotation agent?
No. The ERP remains the primary source of reliable business data. The agent complements it by understanding information from approved sources and combining it for the specific case.
What data does a quotation agent need?
The exact scope depends on the pilot process. Typical sources include customer enquiries, approved product documentation, quotation templates, comparable previous cases and selected master, pricing and project data.
How can invented or incorrect information be prevented?
The agent should use only approved sources, show where information was found, flag missing facts and make uncertainty visible. Every draft is reviewed by a specialist before it is sent.
How quickly does a pilot show benefits?
That depends on the process, data access and the range of variants. What matters is a narrowly defined starting scope in which real cases and previously agreed metrics can be compared early.
Is an on-premises solution possible?
Yes. Sensitive content can be processed using self-hosted models, application servers and data servers within the controlled company environment.
