The AI Vendor Evaluation Scorecard & Scoring Rubric
7 questions from the blog piece expanded into a full scoring rubric a CTO can use to compare multiple vendors side by side.
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Table of Contents & Section Overview
12 Pages TotalVendor Assessment Overview
Key parameters for qualifying technical capabilities.
Category 1: Technical Depth & Code Ownership
Evaluating IP transfer agreements and repository access.
Category 2: Security & Privacy Compliance
Auditing zero-retention policies and private deployment options.
Category 3: Fixed-Scope vs Open-Ended Pricing
Scoring contract transparency and milestone structures.
Appendix: Interactive Vendor Scorecard
Fillable scoring grid for evaluating up to 4 vendors.
Problem Statement
"Without a quantitative scoring rubric, vendor selection often devolves into evaluating sales presentation slickness rather than engineering rigor and contract safety."
Section 4 Excerpt: Contract Transparency & Pricing Models
Weight vendor proposals heavily based on milestone clarity. High-scoring proposals define clear acceptance criteria for each phase (e.g. 'RAG pipeline achieves >90% precision on benchmark test set') before payment release.
Penalize vendors who fail to provide explicit SLAs regarding post-deployment maintenance or who treat AI prompt engineering as proprietary trade secrets withheld from clients.
Weighted Category Scoring Rubric
| Evaluation Category | Weight | Passing Benchmark | Red Flag Condition |
|---|---|---|---|
| Code & Data Ownership | 25% | 100% Repository Transfer | Vendor Retains IP |
| Fixed-Scope Milestones | 25% | Clear Acceptance Gates | Pure Hourly T&M Only |
| Security & Data Isolation | 25% | SOC2 / Private VPC Option | Third-Party API Data Share |
| Post-Launch Maintenance | 25% | Defined Support SLA | No Written SLA |
Related Engineering Resources & Articles
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