How do installation companies create quotes faster — without starting from scratch on every request?

Summary: The quoting process slows down because technical specialists in SMEs have to continuously retype client information and calculations between different systems. This article shows how you can collect unstructured data automatically and integrate it seamlessly into your current software, so your engineers focus on the technical work instead of administration.

Last updated: April 6, 2026 · By Jack van der Vall, AI Engineer

Related reading: see where AI automation saves time for technical installers, which business processes can be automated responsibly, and how to build an automated B2B lead generator.

Why does a technical quotation take so long?

Creating a reliable quote is rarely a simple calculation. You receive PDF drawings or emails with requirements, need to look up material prices, and then manually retype everything into your calculation or ERP software.

For installation companies, margins are structurally under pressure. According to the sector benchmark by Techniek Nederland, the average operating result for installation companies is 9% of turnover. At margins like these, every error in a quotation directly affects your profit.

Copying and manual re-entry does not just cost enormous amounts of time. It also increases the risk of costly errors. A single incorrect order code puts your later project margin at risk.

The installation sector is particularly susceptible to these problems. Quotations typically contain dozens to hundreds of line items. From piping and valves to fixtures and control components. Each item requires a correct article code, quantity, and unit price. Manual entry of these volumes is not just slow; it is a structural source of errors that only become visible when the margin has already evaporated.

What does the typical quoting process look like?

Most installation companies go through a remarkably similar process with every request:

1. Reception: A client sends drawings, specifications, or an email with requirements. The information arrives in varying formats.
2. Interpretation: A quantity surveyor reads through the documents and translates the requirements into an internal bill of materials.
3. Calculation: Material prices are looked up with suppliers. Labor hours are estimated based on experience.
4. Data entry: The collected data is manually typed into the ERP or calculation software.
5. Review: A project manager or second estimator validates the quotation before it goes to the client.

The problem is not in the individual steps. The problem is that nearly every step requires manual translation between systems that do not communicate with each other. Information is repeatedly retyped, copied, and interpreted. Each translation step introduces delay and a chance for errors.

graph TD
    A[Client Request PDF/email] -->|Manual reading| B(Estimator interprets)
    B -->|Bill of materials| C[Look up material prices]
    C -->|Manual re-entry| D[ERP / Calculation software]
    D -->|Review| E{Project manager check}
    E -->|Approved| F[Quote sent to client]
    E -->|Correction needed| B

Accessible summary: A client request is manually read by an estimator who compiles a bill of materials and looks up material prices. After manual entry into the ERP or calculation software, a project manager reviews the quotation. If corrections are needed, the process returns to the estimator.

Centralizing data flows without migration

The solution for most companies is not purchasing yet another software package. The pain points arise at the integration layer. Knowledge and requests need to arrive automatically in the right environment.

By investing in robust architecture, software acts as the necessary bridge. Solid infrastructure processes incoming complex files, isolates the required data, and delivers it as a ready-to-import format for Exact or AFAS.

Concretely, this means an incoming PDF drawing is automatically analyzed. Relevant specifications are extracted and converted to the import format your ERP expects. The estimator no longer needs to manually search and retype. They validate and correct the proposed input.

The effect of this kind of system integration is measurable. Research by McKinsey shows that contractors who digitally connected the feedback flow between site and supplier achieved a 12% reduction in rework hours. The same logic applies to the quoting process: when data no longer needs to be manually translated, the errors that lead to rework disappear.

Raw data available: Quoting Automation Benchmark Data

Why point-and-click automation stalls

Several technical companies try to solve this integration problem themselves with low-barrier no-code platforms. For perfectly standardized input, that sometimes survives. However, with complex installation work, the data varies constantly.

A heating quotation contains different fields than an electrical request. Specification documents differ from informal briefs. Suppliers use different article code systems. Fragile no-code connections crash on these edge cases.

True stability comes from scalable software engineering. By planning with version-controlled, tested code, you enforce system architecture that validates data first. If a measurement or term is insufficient, no faulty data passes through. Instead, the system requires explicit validation by an employee (human-in-the-loop).

Robust systems drive scalability

Scaling your business should not mean scaling your administrative department. Because repetitive actions are automatically handled and validated, you regain control over the quoting process.

When your architecture is solidly aligned with the shop floor, you don’t just create accurate quotations. You also accelerate response time significantly. Clients attach enormous value to speed of response. A quotation that arrives the same day instead of after three days wins projects that would otherwise go to slower competitors.

The return on better quotation quality is concrete. According to McKinsey, companies that analyze historical tender data to optimize their tender selection and pricing achieve a margin improvement of 3 to 5 percentage points. With an average sector result of 9%, that represents a substantial improvement in profitability.

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Frequently Asked Questions

Do I need to replace my current ERP or accounting system?

No. The architecture acts as a bridge. Through secure data connections, we integrate directly with existing systems like Exact or AFAS, so data is exchanged flawlessly without risky migrations.

What is the difference compared to standard no-code automation?

Standard no-code connections often break when exceptions appear in the data. Custom-built software engineering is version-controlled and validates data upfront. This prevents your process from stalling on an atypical request.

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Key Takeaways

• With an average sector operating result of 9%, every error in a quotation directly impacts profitability.
• Smart architecture can translate unstructured documents into import-ready files for existing software packages.
• Solid, integrated software engineering is safe and effective in edge cases where no-code automation breaks down.
• Data-driven quoting processes deliver a 3 to 5 percentage point margin improvement according to McKinsey.

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About the author

Jack van der Vall is an AI Engineer at Opusmatic, specializing in AI automation for technical installation companies and SMEs in South Holland. He builds AI systems that accelerate the quoting process for technical installation companies from hours to minutes.

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