← Case studies
CNP Dongyang

RFI answers for structural review

RFIs, structural drawings, and site photos, structured into one knowledge base. It finds similar past cases and drafts answers that cite the governing code clauses.

Draft RFI answers from similar past cases

Customer
CNP Dongyang
Areas
Review
RFI analysis view with review points marked on a structural drawing
  1. 1Problem

    Engineers searched drawings, codes and past cases manually to answer each RFI. Reviews were time-consuming, and response quality varied by engineer.

  2. 2Input

    RFI questions, structural drawings, site defect photos, KDS/KCS code documents, past review cases

  3. 3How it works

    OCR and vision AI structure the documents and photos, split them into meaningful chunks and tag them with metadata, building a knowledge base on a vector DB and GraphRAG. A review agent reads the intent of each question, judges defect type and severity, and recommends actions. Engineers' corrections flow back in to improve similar-case matching.

  4. 4Output

    Draft RFI answers citing the governing code clauses, defect diagnosis reports, and the supporting evidence

Results

  • Draft RFI answers generated from similar cases
  • Engineers make the final call; AI cuts the drafting time

Could this work on your projects?

Tell us about your challenges, and we'll explain how a similar approach could apply.

Let's talk about AI for your team

Tell us about your workflows and challenges. We'll help you explore suitable products and implementation options.

We collect your name, company, work email, phone, department and role, plus visit data (page URL, referrer, UTM and time of visit) to see how you found us. We use it to respond and send materials, keep it for one year, then delete it.

We use your information to respond to your inquiry. See our Privacy Policy for details.