The Operator's AI Vendor Evaluation Checklist: 12 Questions Before You Sign

Before You Sign That Contract

You're evaluating AI vendors. They all claim:

"Transform your workflow!"
"Industry-leading AI!"
"Trusted by hundreds of companies!" -"90% time savings guaranteed!"

But which claims are real?

Here are the 12 questions operators ask before signing.

The 12-Question Checklist

Question #1: "Show me your production telemetry, not a demo."

What you're testing: Real-world performance vs. cherry-picked demos

Good answer: "Here's our live dashboard: 12,847 documents processed last 30 days, 96.3% accuracy, avg 3.2 seconds per doc, $0.04 cost per doc"

Bad answer: "Let me show you a demo with sample data"

Red flag: If they can't show production metrics, they don't have production customers

Question #2: "What percentage of your customers are in production vs. pilot?"

What you're testing: Product maturity and customer success

Good answer: "72% of customers in production, average 8 months from pilot to production, 94% renewal rate"

Bad answer: "Most customers are in pilot phase, we're iterating based on feedback"

Red flag: >50% in pilot = product not ready for production

Question #3: "Show me your worst failure. What happened and how did you fix it?"

What you're testing: Honesty, failure handling, continuous improvement

Good answer: "We missed a critical risk in a deal in Q2 2024. Root cause: edge case in contract parsing. Fix: added 200 test cases, improved recall from 87% to 94%, no recurrence in 8 months"

Bad answer: "We don't really have failures, our AI is very accurate"

Red flag: No failures = lying or not in production

Question #4: "What are your acceptance gates and thresholds?"

What you're testing: Quality governance and operator discipline

Good answer: "Four gates: ingestion (95% success), extraction (90% accuracy), synthesis (95% completeness), validation (100% reconciliation). Fail any gate, pause deployment"

Bad answer: "We use AI, it's pretty accurate, you can review the outputs"

Red flag: No gates = production disasters waiting to happen

Question #5: "What's your average customer payback period?"

What you're testing: Real ROI, not theoretical savings

Good answer: "3.2 weeks average across 84 customers, fastest was 12 days, longest was 9 weeks"

Bad answer: "ROI varies by customer, but we estimate significant long-term value"

Red flag: Can't cite average payback = customers aren't seeing value

Question #6: "Can I talk to 3 reference customers who are in production?"

What you're testing: Real customer success, not cherry-picked testimonials

Good answer: "Yes, here are 5 customers you can talk to: [names, contact info]. They've been in production 6+ months"

Bad answer: "Our customers prefer to stay confidential, but here are some case studies"

Red flag: No referenceable production customers = hiding something

Question #7: "What happens when your AI makes a mistake?"

What you're testing: Error handling, accountability, human-in-the-loop

Good answer: "Human validation at 3 checkpoints. Errors flagged in real-time. Audit trail for all outputs. We retrain on errors weekly. Error rate: 3.7% average, trending down"

Bad answer: "Our AI is highly accurate, mistakes are rare"

Red flag: No error handling process = liability when mistakes happen

Question #8: "How do you handle data security and privacy?"

What you're testing: Security, compliance, data governance

Good answer: "SOC 2 Type II certified, GDPR compliant, data encrypted at rest and in transit, zero-retention option available, customer data never used for training, annual penetration testing"

Bad answer: "We take security seriously, your data is safe with us"

Red flag: Vague security claims = compliance nightmare

Question #9: "What's your pricing model and are there hidden costs?"

What you're testing: Transparent pricing, predictable costs, no gotchas

Good answer: "$X per document processed, includes all API costs, no setup fees, no minimum commitment, cost calculator available, 90-day price lock"

Bad answer: "Custom pricing based on your needs, let's discuss your budget"

Red flag: No transparent pricing = unpredictable costs

Question #10: "How long to production and what's the failure rate?"

What you're testing: Implementation difficulty, success rate

Good answer: "Average 4 weeks from pilot to production, 87% of pilots go to production, 13% fail (mostly due to data quality issues we catch early)"

Bad answer: "Depends on complexity, we'll work with you to figure it out"

Red flag: Can't cite timelines or success rates = risky implementation

Question #11: "What's your kill-switch criteria?"

What you're testing: Risk management, customer protection

Good answer: "Automatic pause if accuracy drops below 90%, error rate exceeds 5%, or customer flags critical issue. Happened 3 times in 2024, resolved within 48 hours"

Bad answer: "You can cancel anytime if you're not satisfied"

Red flag: No automatic safeguards = no protection from bad AI

Question #12: "Do you use my data to train your models?"

What you're testing: Data ownership, competitive moat protection

Good answer: "No. Your data is never used for training. You can delete all data anytime. Zero-retention option available."

Bad answer: "We use aggregate, anonymized data to improve our models for all customers"

Red flag: Your competitive intelligence becomes their product

The Scoring System

Rate each answer: 2 points (good), 1 point (okay), 0 points (bad/red flag)

Scoring:

20-24 points: Production-ready, low-risk vendor
15-19 points: Proceed with caution, negotiate safeguards
10-14 points: High-risk, likely not production-ready
<10 points: Walk away

Example scoring:

Vendor A (AI hype):

Q1: Demo only (0)
Q2: 80% in pilot (0)
Q3: "No failures" (0)
Q4: No gates (0)
Q5: "Varies" (0)
Q6: No references (0)
Q7: "Rare mistakes" (0)
Q8: "Safe" (1)
Q9: Custom pricing (0)
Q10: "Depends" (0)
Q11: Cancel anytime (0)
Q12: Uses data (0)

Score: 1/24 - WALK AWAY

Vendor B (Operator-grade):

Q1: Live telemetry (2)
Q2: 72% production (2)
Q3: Detailed failure story (2)
Q4: Four gates (2)
Q5: 3.2 week payback (2)
Q6: 5 references (2)
Q7: Error handling process (2)
Q8: SOC 2, GDPR (2)
Q9: Transparent pricing (2)
Q10: 4 weeks, 87% success (2)
Q11: Auto pause (2)
Q12: Never uses data (2)

Score: 24/24 - GREEN LIGHT

The Deal-Breakers (Instant Disqualification)

If vendor:

❌ Can't show production telemetry
❌ Won't provide production references
❌ No documented acceptance gates
❌ Uses your data to train models
❌ No security certifications (SOC 2, etc.)
❌ No error handling process
❌ Can't cite average payback period
❌ Claims "no failures"

→ WALK AWAY

The Negotiation Leverage Points

Use these answers to negotiate:

Leverage Point #1: Pilot Terms

If scoring 15-19: "Prove it with a 3-week pilot, no commitment, full telemetry"

Terms:

Zero upfront cost
Success criteria defined (time, cost, quality)
Acceptance gates documented
Kill-switch at any gate
If gates pass, 20% discount on annual contract

Leverage Point #2: Performance Guarantees

If scoring 15-19: "Guarantee the payback period or refund"

Terms:

Vendor guarantees X week payback
If not achieved, pro-rated refund
Must show telemetry proving no value
Splits risk between vendor and customer

Leverage Point #3: Data Security Addendum

If scoring on Q8: "Add data security requirements to contract"

Terms:

Zero-retention option
Data deletion on termination
Never used for training
Annual security audits
Breach notification <24 hours

Real-World Vendor Evaluation: PE Firm Case Study

Situation: PE firm evaluating 3 AI vendors for due diligence automation

Vendor A: "AI Innovation Co"

Impressive demo with sample data
95% of customers in pilot
Can't show production telemetry
No acceptance gates defined
"Custom pricing, let's discuss"
Score: 6/24
Decision: REJECT

Vendor B: "Enterprise AI Platform"

Some production telemetry (limited)
60% production, 40% pilot
Has acceptance gates (but high failure rates)
Can provide 2 references
Transparent pricing (but expensive)
Score: 16/24
Decision: PILOT with safeguards

Vendor C: "MeldIQ" (operator-grade)

Live dashboard with 12K+ docs processed
72% production, 87% pilot success rate
Four documented acceptance gates
5 production references provided
3.2 week average payback
SOC 2 Type II certified
Transparent per-doc pricing
Score: 23/24
Decision: APPROVED, negotiate contract

Outcome: Went with Vendor C, achieved 2.1 week payback, scaled to production in 4 weeks

Next Steps: Evaluate Your AI Vendor

Step 1: Use the Checklist

Download the 12-question checklist
Interview vendors using exact questions
Score each answer (0-2 points)
Compare vendors objectively

Step 2: Test MeldIQ

Want to see how we answer all 12 questions?

We'll show you:

Live production telemetry
Customer references (in production)
Documented acceptance gates
Average payback period (telemetry-proven)
Security certifications
Transparent pricing

Evaluate MeldIQ →

Don't sign based on demos. Evaluate with the 12-question checklist. See operator-grade AI →

Before You Sign That Contract

You're evaluating AI vendors. They all claim:

"Transform your workflow!"
"Industry-leading AI!"
"Trusted by hundreds of companies!" -"90% time savings guaranteed!"

But which claims are real?

Here are the 12 questions operators ask before signing.

The 12-Question Checklist

Question #1: "Show me your production telemetry, not a demo."

What you're testing: Real-world performance vs. cherry-picked demos

Good answer: "Here's our live dashboard: 12,847 documents processed last 30 days, 96.3% accuracy, avg 3.2 seconds per doc, $0.04 cost per doc"

Bad answer: "Let me show you a demo with sample data"

Red flag: If they can't show production metrics, they don't have production customers

Question #2: "What percentage of your customers are in production vs. pilot?"

What you're testing: Product maturity and customer success

Good answer: "72% of customers in production, average 8 months from pilot to production, 94% renewal rate"

Bad answer: "Most customers are in pilot phase, we're iterating based on feedback"

Red flag: >50% in pilot = product not ready for production

Question #3: "Show me your worst failure. What happened and how did you fix it?"

What you're testing: Honesty, failure handling, continuous improvement

Good answer: "We missed a critical risk in a deal in Q2 2024. Root cause: edge case in contract parsing. Fix: added 200 test cases, improved recall from 87% to 94%, no recurrence in 8 months"

Bad answer: "We don't really have failures, our AI is very accurate"

Red flag: No failures = lying or not in production

Question #4: "What are your acceptance gates and thresholds?"

What you're testing: Quality governance and operator discipline

Good answer: "Four gates: ingestion (95% success), extraction (90% accuracy), synthesis (95% completeness), validation (100% reconciliation). Fail any gate, pause deployment"

Bad answer: "We use AI, it's pretty accurate, you can review the outputs"

Red flag: No gates = production disasters waiting to happen

Question #5: "What's your average customer payback period?"

What you're testing: Real ROI, not theoretical savings

Good answer: "3.2 weeks average across 84 customers, fastest was 12 days, longest was 9 weeks"

Bad answer: "ROI varies by customer, but we estimate significant long-term value"

Red flag: Can't cite average payback = customers aren't seeing value

Question #6: "Can I talk to 3 reference customers who are in production?"

What you're testing: Real customer success, not cherry-picked testimonials

Good answer: "Yes, here are 5 customers you can talk to: [names, contact info]. They've been in production 6+ months"

Bad answer: "Our customers prefer to stay confidential, but here are some case studies"

Red flag: No referenceable production customers = hiding something

Question #7: "What happens when your AI makes a mistake?"

What you're testing: Error handling, accountability, human-in-the-loop

Good answer: "Human validation at 3 checkpoints. Errors flagged in real-time. Audit trail for all outputs. We retrain on errors weekly. Error rate: 3.7% average, trending down"

Bad answer: "Our AI is highly accurate, mistakes are rare"

Red flag: No error handling process = liability when mistakes happen

Question #8: "How do you handle data security and privacy?"

What you're testing: Security, compliance, data governance

Good answer: "SOC 2 Type II certified, GDPR compliant, data encrypted at rest and in transit, zero-retention option available, customer data never used for training, annual penetration testing"

Bad answer: "We take security seriously, your data is safe with us"

Red flag: Vague security claims = compliance nightmare

Question #9: "What's your pricing model and are there hidden costs?"

What you're testing: Transparent pricing, predictable costs, no gotchas

Good answer: "$X per document processed, includes all API costs, no setup fees, no minimum commitment, cost calculator available, 90-day price lock"

Bad answer: "Custom pricing based on your needs, let's discuss your budget"

Red flag: No transparent pricing = unpredictable costs

Question #10: "How long to production and what's the failure rate?"

What you're testing: Implementation difficulty, success rate

Good answer: "Average 4 weeks from pilot to production, 87% of pilots go to production, 13% fail (mostly due to data quality issues we catch early)"

Bad answer: "Depends on complexity, we'll work with you to figure it out"

Red flag: Can't cite timelines or success rates = risky implementation

Question #11: "What's your kill-switch criteria?"

What you're testing: Risk management, customer protection

Good answer: "Automatic pause if accuracy drops below 90%, error rate exceeds 5%, or customer flags critical issue. Happened 3 times in 2024, resolved within 48 hours"

Bad answer: "You can cancel anytime if you're not satisfied"

Red flag: No automatic safeguards = no protection from bad AI

Question #12: "Do you use my data to train your models?"

What you're testing: Data ownership, competitive moat protection

Good answer: "No. Your data is never used for training. You can delete all data anytime. Zero-retention option available."

Bad answer: "We use aggregate, anonymized data to improve our models for all customers"

Red flag: Your competitive intelligence becomes their product

The Scoring System

Rate each answer: 2 points (good), 1 point (okay), 0 points (bad/red flag)

Scoring:

20-24 points: Production-ready, low-risk vendor
15-19 points: Proceed with caution, negotiate safeguards
10-14 points: High-risk, likely not production-ready
<10 points: Walk away

Example scoring:

Vendor A (AI hype):

Q1: Demo only (0)
Q2: 80% in pilot (0)
Q3: "No failures" (0)
Q4: No gates (0)
Q5: "Varies" (0)
Q6: No references (0)
Q7: "Rare mistakes" (0)
Q8: "Safe" (1)
Q9: Custom pricing (0)
Q10: "Depends" (0)
Q11: Cancel anytime (0)
Q12: Uses data (0)

Score: 1/24 - WALK AWAY

Vendor B (Operator-grade):

Q1: Live telemetry (2)
Q2: 72% production (2)
Q3: Detailed failure story (2)
Q4: Four gates (2)
Q5: 3.2 week payback (2)
Q6: 5 references (2)
Q7: Error handling process (2)
Q8: SOC 2, GDPR (2)
Q9: Transparent pricing (2)
Q10: 4 weeks, 87% success (2)
Q11: Auto pause (2)
Q12: Never uses data (2)

Score: 24/24 - GREEN LIGHT

The Deal-Breakers (Instant Disqualification)

If vendor:

❌ Can't show production telemetry
❌ Won't provide production references
❌ No documented acceptance gates
❌ Uses your data to train models
❌ No security certifications (SOC 2, etc.)
❌ No error handling process
❌ Can't cite average payback period
❌ Claims "no failures"

→ WALK AWAY

The Negotiation Leverage Points

Use these answers to negotiate:

Leverage Point #1: Pilot Terms

If scoring 15-19: "Prove it with a 3-week pilot, no commitment, full telemetry"

Terms:

Zero upfront cost
Success criteria defined (time, cost, quality)
Acceptance gates documented
Kill-switch at any gate
If gates pass, 20% discount on annual contract

Leverage Point #2: Performance Guarantees

If scoring 15-19: "Guarantee the payback period or refund"

Terms:

Vendor guarantees X week payback
If not achieved, pro-rated refund
Must show telemetry proving no value
Splits risk between vendor and customer

Leverage Point #3: Data Security Addendum

If scoring on Q8: "Add data security requirements to contract"

Terms:

Zero-retention option
Data deletion on termination
Never used for training
Annual security audits
Breach notification <24 hours

Real-World Vendor Evaluation: PE Firm Case Study

Situation: PE firm evaluating 3 AI vendors for due diligence automation

Vendor A: "AI Innovation Co"

Impressive demo with sample data
95% of customers in pilot
Can't show production telemetry
No acceptance gates defined
"Custom pricing, let's discuss"
Score: 6/24
Decision: REJECT

Vendor B: "Enterprise AI Platform"

Some production telemetry (limited)
60% production, 40% pilot
Has acceptance gates (but high failure rates)
Can provide 2 references
Transparent pricing (but expensive)
Score: 16/24
Decision: PILOT with safeguards

Vendor C: "MeldIQ" (operator-grade)

Live dashboard with 12K+ docs processed
72% production, 87% pilot success rate
Four documented acceptance gates
5 production references provided
3.2 week average payback
SOC 2 Type II certified
Transparent per-doc pricing
Score: 23/24
Decision: APPROVED, negotiate contract

Outcome: Went with Vendor C, achieved 2.1 week payback, scaled to production in 4 weeks

Next Steps: Evaluate Your AI Vendor

Step 1: Use the Checklist

Download the 12-question checklist
Interview vendors using exact questions
Score each answer (0-2 points)
Compare vendors objectively

Step 2: Test MeldIQ

Want to see how we answer all 12 questions?

We'll show you:

Live production telemetry
Customer references (in production)
Documented acceptance gates
Average payback period (telemetry-proven)
Security certifications
Transparent pricing

Evaluate MeldIQ →

Don't sign based on demos. Evaluate with the 12-question checklist. See operator-grade AI →