Evaluating AI Vendors Without Getting Burned

The AI vendor landscape is a minefield. Every company with a GPT wrapper claims to be "enterprise-ready." Every demo looks magical. And somehow, every solution is exactly what you need—until you're six months into implementation and realize you've been sold vaporware.

Here's how to separate signal from noise.

The Demo Is Lying to You#

Not intentionally (usually), but demos are optimized for one thing: looking good. They use curated data, ideal conditions, and cherry-picked examples.

Warning

If a vendor won't let you test with YOUR data in YOUR environment, that's a red flag. A big one.

Questions to ask during every demo:

"Can we run this on our data?" Not sample data. Not anonymized data they've cleaned up. Your actual, messy, real-world data.
"What happens when it fails?" Every AI system fails sometimes. How does this one fail? Gracefully? Catastrophically? Silently?
"Show me an edge case." Ask them to demonstrate something that doesn't work well. Their response tells you everything.

The Technical Due Diligence Checklist#

Before signing anything, verify these fundamentals:

Data Handling#

typescript

// Questions your technical team should be asking:
interface DataDueDiligence {
  // Where does our data go?
  dataResidency: "on-premise" | "cloud" | "hybrid";
  dataRetention: "none" | "training" | "logs-only";

  // Who can see it?
  accessControls: boolean;
  auditLogging: boolean;

  // What happens to it?
  usedForTraining: boolean;  // Critical: is your data improving their model?
  encryptionAtRest: boolean;
  encryptionInTransit: boolean;
}

If they can't answer these questions clearly, they haven't thought about enterprise deployment.

Integration Reality#

The demo shows a beautiful UI. But you need:

API documentation - Is it comprehensive? Up to date?
Authentication options - SSO? API keys? OAuth?
Rate limits - What happens at scale?
SLAs - Uptime guarantees? Response time commitments?

Pro Tip

Ask for references from companies in your industry, at your scale. Then actually call them.

Model Transparency#

What model(s) power the solution?
How often are models updated?
Will updates change behavior? How will you be notified?
Can you pin to a specific model version?

Red Flags That Should Kill a Deal#

Walk away if you encounter any of these:

"Our AI is proprietary and we can't explain how it works." Translation: We're using the same foundation models as everyone else and hoping you won't ask.

"Implementation takes 2 weeks." For anything meaningful? No. Either the solution is trivial or they're lying about complexity.

"We don't have other customers in your industry yet." You're their guinea pig. That can be fine—if they're pricing accordingly and being honest about it.

"The current version doesn't do X, but our roadmap..." Never buy roadmap. Buy what exists today.

Reluctance to do a paid pilot. If they won't let you validate before committing, they know something you don't.

The Pilot Structure That Works#

Don't do a free pilot. Free pilots get vendor B-teams and minimal support. Pay for a proper evaluation:

Phase	Duration	What You're Testing
Data Integration	2-3 weeks	Can they actually connect to your systems?
Accuracy Baseline	2-3 weeks	Measure performance on real tasks
Edge Case Testing	2 weeks	Break it intentionally
User Acceptance	2-3 weeks	Do your people actually find it useful?

Total: 8-11 weeks. Yes, it's a real investment. It's cheaper than a failed deployment.

Pricing Traps to Avoid#

AI vendors have gotten creative with pricing. Watch for:

Per-query pricing at demo scale that becomes ruinous at production volume
Tiered pricing where the features you actually need are only in enterprise tier
"Platform fees" plus usage fees plus support fees plus...
Annual contracts with no exit clause if the product doesn't perform

Get everything in writing. Model total cost of ownership at 10x your expected volume.

The Reference Call Script#

When you call their references (and you should), ask:

What problem were you trying to solve?
How long did implementation actually take?
What surprised you—good or bad?
What would you do differently?
Would you choose them again?

Listen for hesitation. Happy customers don't hesitate.

Building Your Evaluation Matrix#

Score each vendor objectively:

python

evaluation_criteria = {
    "technical_fit": {
        "weight": 0.30,
        "factors": ["integration_ease", "scalability", "security"]
    },
    "vendor_viability": {
        "weight": 0.20,
        "factors": ["funding", "team_experience", "customer_base"]
    },
    "total_cost": {
        "weight": 0.25,
        "factors": ["license", "implementation", "ongoing_ops"]
    },
    "risk_profile": {
        "weight": 0.25,
        "factors": ["lock_in", "data_control", "exit_strategy"]
    }
}

Weight what matters to your organization. But don't skip any category.

The Bottom Line#

The best AI vendor evaluation is boring. It's spreadsheets, reference calls, technical deep-dives, and pilot programs. It's not getting excited by a flashy demo.

The companies that get AI right aren't the ones who moved fastest. They're the ones who chose vendors that could actually deliver on their promises.

Take your time. Do the work. Your future self will thank you.

Need help evaluating AI solutions for your organization? Let's talk.