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Elm AI · Practical guide

A simpler way to review supplier questionnaires

AI can help compare a supplier’s answers with their documents, spot gaps and prepare follow-up questions. Your team decides what the evidence supports.

For sourcing teams reviewing supplier submissions. Use these examples as a starting point, then adapt the checks to your program.

From an answer to a decision
  1. Read the answer
    Keep the supplier’s words and attachments together.
  2. Check the evidence
    Look for the right facility, dates and source.
  3. Decide what’s next
    A reviewer accepts it or asks for clarification.
AI can help gather and compare information. Your team sets the rules and owns the review decision.Elm AI

Start with one question

Pick a question your team already reviews. What would you need to see before accepting the answer?

Agree which supplier or facility it covers, which dates matter and what documents count. A policy describes what should happen; an attendance sheet records who took part. The right evidence depends on the question.

Keep the original answer and attachments. Save the review notes beside them so someone can follow the reasoning later.

A “yes” still needs the right document
The supplier’s answer
“Yes, training is complete.”
Facility:
A
Period:
January–June 2026
The attached record
Training attendance sheet
Facility:
B
Period:
November 2025
Keep this answer open
The facility and dates do not match. Ask for the relevant record, or an explanation of how this one applies.
Fictional example. The facilities, dates and training question are illustrative, not a customer result or a legal requirement.Elm AI

Ask about the gap, without jumping to conclusions

In the example above, the attachment does not support the answer for Facility A. That does not prove the training never happened.

A useful follow-up would be:

“Could you share the training record for Facility A for January–June 2026, or explain how the attached record covers it?”

Your program may accept a group-wide record. If so, check that it includes the relevant facility and people. Set that rule before asking an agent to apply it.

Give each problem a clear next step

A reviewer should be able to see why an answer needs attention. These are useful starting points:

  • Nothing attached? Ask for the missing evidence.
  • Wrong facility or dates? Ask how the document applies.
  • The answer and document disagree? Show both to the reviewer.
  • The scan or wording is unclear? Request a clearer copy or specialist help.

When the evidence fits, keep the source reference and decision. A link to a document helps someone check the work; it does not prove the document is authentic.

Make the handoff clear
AI prepares
  • Finds the relevant passage
  • Shows what does not match
  • Drafts a specific question
A person reviews
  • Checks the original source
  • Approves the follow-up
  • Records the decision and reason
Start with a person checking follow-up messages before they are sent. Agree permissions before allowing any automatic messages or record updates.Elm AI

Try a small batch before expanding

Use examples your reviewers can check themselves. Include good answers, missing files, wrong dates and hard-to-read scans. Add different languages or questionnaire versions if your team uses them.

Agree the expected decisions first, then compare them with the agent’s work. Check the source passages too: a correct-looking answer can still point to the wrong page.

Look for missed problems and unnecessary follow-ups. Both create work. Measure the time spent finishing a review, including corrections and supplier replies.

Before expanding, agree where decisions are saved, who can change them and how mistakes are corrected. Recheck the workflow when your questions or program rules change.

Bring us a questionnaire your team uses

Supplier questionnaires are one of the workflows covered in Elm AI’s deployment approach. Elm works with teams to scope, build and test AI workflows, with human review points and a way to measure the result.

Start with a redacted or made-up example and explain where reviews get stuck. The checks, integrations and follow-up permissions can then be designed around your team.

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