# AI for Reviewing Customer Specifications and URS Documents Before You Quote > Source: https://netray.co/resources/ai-for-reviewing-machine-specifications-and-urs-documents > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Knowledge Base ## Short answer How machine builders use private AI to read customer URS and spec packages, list requirements and risks, and find similar past projects before quoting. ## Category ai-automation ## Content **Intro:** AI can review a customer specification or user requirement specification for a custom machine by extracting each requirement, classifying it by discipline, flagging unusual or risky clauses, and finding comparable past projects in your ERP and document store. It turns a 90-page spec package into a requirements table and an assumptions draft for the estimator. The estimator still decides the price. The value is that nothing buried on page 62 gets missed. **Sections:** #### What a Spec Package Contains **Body:** Machine RFQs arrive as a URS or functional spec, the customer's electrical, controls, and safety standards, referenced industry standards, sample part drawings, layout drawings of the area, and commercial terms. Hidden risk lives in the standards documents: a required PLC platform and version, a specific HMI template, a cybersecurity requirement, a washdown rating, a documentation language, or a mandated spare parts list. Reading all of it carefully takes hours, and it is usually done under deadline, which is exactly when items are missed. The estimator quotes what the URS says and discovers the plant standard later. **Bullets:** - URS or functional specification - Customer electrical, controls, and safety standards - Part drawings and layout constraints - Commercial terms, warranty, and acceptance clauses #### What the AI Produces **Body:** The output is a structured requirements table: requirement text, source document and page, discipline (mechanical, electrical, controls, safety, documentation, commercial), and a flag for items that historically drive cost. It identifies conflicts between documents, for example a URS that says one PLC brand while the standard names another. It drafts assumptions and exclusions the estimator can accept or edit, and it lists clarification questions to send back to the customer. Every item cites its source page so a human can verify in seconds. #### Matching Against Your Own History **Body:** Retrieval over your project records adds the context a spec alone lacks. For each major requirement the system can show similar past machines, their quoted and actual engineering hours, change orders raised, and FAT issues. If the new spec calls for in-line vision inspection and your last two vision projects ran 40 percent over, the estimator sees it at the moment of pricing. That requires access to ERP project data and document stores, which for many customers includes export-controlled or confidential information, so the pipeline should run on infrastructure the customer controls. #### Controls: Citations, Read-Only, and Approval **Body:** Hallucination is the main risk, so require citations for every extracted item and treat the output as a draft. The system should be read-only against the ERP, show the queries it ran, and respect user permissions. Nothing should be written to a quote, the ERP, or sent to the customer without a person approving. Test it first on three closed projects: compare what the AI extracted against what your engineers wish they had caught. That tells you more than a vendor demo. #### When It Is Not Worth It **Body:** If you receive a few RFQs a month, each reviewed by a senior engineer who knows the customer, a checklist may deliver most of the benefit. If your specs are short, or you build a repeat product with a fixed option list, a configurator is a better tool than AI. The case strengthens with high RFQ volume, long documents, many customer standards, or an estimator bottleneck that causes you to decline bids. **Cta:** Send Netray a closed project's RFQ package and we will show what a grounded, cited requirements review looks like running privately on your side. **Faq:** #### Can AI read a customer URS for a machine quote? **Answer:** Yes, a language model with retrieval can extract requirements, classify them by discipline, and flag risky clauses with page citations. A human estimator should verify the output and make the pricing decisions. It works best on text-based documents and needs OCR for scanned pages. #### Is it safe to upload customer specifications to AI tools? **Answer:** That depends on your NDA and any export controls. Many customer specs prohibit sharing with third parties, and some contain ITAR or EAR controlled data. Running models on your own hardware or private cloud avoids sending the documents to an external AI provider. #### How accurate is AI at extracting requirements? **Answer:** Accuracy depends on document quality and how you test it. Measure it against a few closed projects with a human-built requirements list, count misses and false flags, and adjust prompts and retrieval. Keep citations mandatory so errors are easy to catch. ## Related Slugs - multimodal-rag-engineering-drawings-guide - ai-quoting-aerospace-defense-manufacturers - engineer-to-order-erp-challenges - erp-cad-integration-engineering-data ## Search terms AI review customer specification machine builder, AI URS review, AI RFQ analysis custom machinery, extract requirements from specification AI, AI estimating engineer to order, private AI RFQ review manufacturing --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json