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Vettique · Supplier due diligence · Evidence-backed AI

Improving supplier risk assessments

I improved Vettique's supplier-assessment system, adding evidence sources and separating missing information from signs of risk.

Data integration and scoring milestone delivered.

What the business needed

A language-model-only score could label a legitimate manufacturer as unsafe because evidence was missing.

What I worked on

  1. 01Connected seven sources of supplier information.
  2. 02Changed the reports to distinguish missing information from evidence of risk and show which checks completed.
  3. 03Removed shipment records that had been assigned to the wrong suppliers.

KEY DECISIONS

Separate unknown from unsafe

Keep data gaps in their own labeled section so missing evidence cannot manufacture risk.

Expose coverage

Show which checks ran, were blocked, or found nothing, rather than presenting every result as equally complete.

What the project delivered

77 sources of supplier informationCustoms records, supplier profile, category pricing, business listing, domain age, scam and safe-browsing checks, web presence.
85 to 25One supplier’s risk score changed from 85 to 25After correcting the evidence used in that assessment. This is one recorded example.
396,339misattributed shipments removedCaught and removed before launch.

“Patrick is an excellent developer and a genuinely trustworthy partner. He delivered a complex data integration milestone involving 7 external sources, built a clean pipeline architecture, and was honest about technical limitations rather than overpromising. He communicates proactively, fixes issues swiftly, and I'm continuing to work with him on a second project. Highly recommended.”

Ziad Ayad, Founder, Vettique

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