Operations & Compliance

Supplier code of conduct compliance review

Review supplier self-assessment submissions and audit evidence against your code of conduct requirements. Flags gaps and assigns a compliance score before human review.

AugmentationPattern-matchingAccuracyTime saving

50–65%

reduction in compliance document review time

Opportunity assessment

Business Impact
2

Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.

Feasibility
4

Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.

Data Readiness
4

Light data requirements. Uses straightforward inputs — documents, product descriptions, customer records — that are usually accessible with minimal prep.

Risk Exposure
2

High risk. Significant external exposure or regulatory implications. Requires robust oversight and clear accountability.

Change Complexity
4

Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.

Tooling required

Standard LLMRAG system

Things to consider

  • The code of conduct must be digitised and clearly structured in the RAG system. Vague or aspirational code language produces vague AI assessments.

  • Supplier submissions vary in format and completeness. The AI needs exposure to the range of document formats you receive before it can reliably extract compliance evidence.

  • Human ethics, sourcing, or compliance team review before any supplier-facing decision is essential — particularly for decisions affecting ongoing business relationships or supply chain audits.

  • Code of conduct compliance reviews often reveal systemic gaps (suppliers consistently weak on a particular area) as much as individual supplier issues. Build the analysis to surface these patterns, not just individual flags — the systemic insights drive the most valuable interventions.

  • Experiment starter: Take the last 20 supplier compliance submissions already reviewed by your team. Run them through an LLM against your code requirements and compare the AI's gap flags against the team's manual findings. If recall on material gaps exceeds 85%, proceed to a pilot where AI provides the first-pass review and the team handles only flagged items.

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