The 3 Questions Every AI Vendor Should Answer (Before You Buy)

You're in a demo with an AI vendor.

The sales rep is clicking through slides. Everything is "powered by advanced machine learning." The UI looks slick. The case studies sound impressive. Your team is nodding.

Then someone asks: "But how does it actually work?"

The rep smiles. "Our proprietary AI engine uses state-of-the-art natural language processing and deep learning to deliver industry-leading accuracy."

Translation: "I have no idea, and neither do you."

You're about to spend $50K-$500K on a system that will touch your customer data, make decisions about your revenue pipeline, and become critical infrastructure for your team.

And you have no idea what's under the hood.

Here are the three questions every AI vendor should be able to answer clearly—before you sign anything.

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Question 1: "When Your AI Is Wrong, Will I Know?"

This is the question that separates real infrastructure from expensive demos.

Why This Matters

AI isn't magic. It's statistics. It makes predictions based on patterns, and sometimes those predictions are wrong.

The scenarios that should terrify you:

What Good Vendors Say

"We show you confidence scores on every prediction."

Not just "this lead scored 87/100" but "this lead scored 87/100 with 94% confidence."

That 94% tells you: "The model is very sure about this score. You can probably trust it."

If confidence was 62%? "The model is guessing. You should review this manually."

"We flag low-confidence decisions for human review."

This way, the AI never makes a high-stakes decision when it's not confident.

"We log every decision so you can audit what went wrong."

Without logs, you're flying blind. With logs, you can fix the model.

What Bad Vendors Say

"Our AI is 95% accurate."

"Our advanced models ensure reliability."

That's not an answer. That's marketing.

"Trust the AI."

No. Trust requires transparency.

What You Should Ask

1. "Show me a real example where your system got something wrong. What happened?"

If they can't show you a failure, they're either lying or they've never tested their system properly.

2. "How do I know when the AI isn't confident about a prediction?"

If the answer doesn't include "confidence scores" or "uncertainty thresholds," walk away.

3. "Can I see the audit log for a decision your system made?"

If they can't show you input → model → output → action in a traceable format, you can't debug when things break.

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Question 2: "What Happens to My Data?"

This question sounds paranoid. It's not. It's the most important question you can ask.

Why This Matters

What could go wrong?

What Good Vendors Say

"Your data stays yours. We don't use it to train our models."

"We support on-premise or private cloud deployment for sensitive workloads."

If you're in a regulated industry (finance, healthcare, government), you might need your data to never leave your own infrastructure.

Good vendors offer that option.

"We're SOC 2 compliant and can sign a DPA."

SOC 2 = they've been audited on security practices. DPA (Data Processing Agreement) = legally binding terms on how they handle your data.

If they don't have these, they're not ready for enterprise.

"You can export or delete your data anytime."

What Bad Vendors Say

"All data is encrypted."

"We take security very seriously."

Everybody says this. It means nothing without specifics.

"We're compliant."

Compliant with what? Show me the certification.

What You Should Ask

1. "Is my data used to train your AI models?"

The correct answer is: "No. Your data is only used to provide services to you."

2. "Where is my data stored, and who has access to it?"

3. "Can you sign a Data Processing Agreement and provide your SOC 2 report?"

If they hesitate, can't provide these, or say "we're working on it," you're taking a risk.

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Question 3: "What Happens When My Needs Change?"

AI systems aren't "set it and forget it." Your business changes. Your ICP changes. Your team changes. Your data changes.

If the AI system can't adapt, it becomes expensive shelfware.

Why This Matters

The reality of business:

Month 1: You're scoring leads based on company size, industry, and form responses.

Month 6: You just signed three massive deals from a vertical you didn't target. Now that vertical is your top priority. Your scoring model still thinks it's low-priority.

Month 12: You hired a new sales leader who wants different routing logic. Your AI system is hardcoded to the old structure.

Month 18: You acquired a competitor. You need to merge two CRMs, two lead sources, two scoring models.

If your AI infrastructure can't adapt to these changes, you're stuck rebuilding from scratch every time something shifts.

What Good Vendors Say

"You can update rules and logic without involving us."

"We have APIs and webhooks so you can integrate with anything."

Today you use Salesforce and HubSpot. Tomorrow you might use a different CRM, a new data source, or a custom internal tool.

If the AI system only integrates with a fixed set of tools, you're locked in.

"You can A/B test different models and roll back changes."

What if your new scoring model is worse than the old one?

"We have staging environments so you can test before going live."

You should never test changes in production.

What Bad Vendors Say

"Our AI automatically adapts."

"Automatic" can mean "magic" or "out of your control." Be suspicious.

"Just let us know what you need and we'll update it for you."

Now you're dependent on their engineering backlog. Changes take weeks. You lose agility.

"Our system is purpose-built for [your industry]."

That sounds good until your business model shifts slightly and now you're stuck.

General-purpose infrastructure that you can configure beats special-purpose tools you can't change.

What You Should Ask

1. "Can I change scoring rules, routing logic, and thresholds without involving your team?"

If the answer is "involve our team," you're buying a consulting engagement, not infrastructure.

2. "What happens if I want to integrate a new data source that's not on your pre-built list?"

3. "Can I test changes in a staging environment before deploying to production?"

If they don't offer this, you're testing on live data. That's reckless.

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The StratumIQ Standard

We built our platform around these three questions because we've seen too many teams burned by black-box AI systems.

Question 1: When Our AI Is Wrong, You Know

Confidence scores on every prediction: "This lead scored 87/100 with 92% confidence."

Thresholds you control: "Only auto-route if confidence ≥ 85%. Flag everything else for review."

Full audit trails: See input → model → score → decision → action for every item processed. Export as JSON for debugging.

Question 2: Your Data Stays Yours

No model training on your data: Your workflows, data, and insights are yours. We don't use them to improve other customers' systems.

SOC 2 ready, with DPAs available: We're built for regulated industries. Security and compliance from day one.

On-premise and private cloud options: Need your data to never leave your infrastructure? We support that.

Export and delete anytime: Get your data in JSON or CSV. Leave whenever you want. No lock-in.

Question 3: You Control the Logic

Change rules without us: Update scoring weights, routing logic, and thresholds in our UI. No support tickets needed.

API-first architecture: Connect any data source, any CRM, any tool. Webhooks, REST APIs, custom functions.

Staging environments: Test changes with historical data before deploying live. Roll back instantly if needed.

Deterministic fallbacks: When AI isn't confident, you define what happens next. No surprise behaviors.

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Your Pre-Demo Checklist

Before you take another AI vendor demo, send them this email:

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Subject: Three Questions Before Our Demo

Hi [Vendor],

Before we meet, I'd like to understand three things:

Please send written answers before our call. This will help us use our time efficiently.

Thanks, [Your Name]

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If they can't answer these questions clearly, in writing, before the demo—cancel the meeting.

You just saved yourself from months of regret.

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The Bottom Line

AI vendors love to sell you on "innovation" and "cutting-edge technology."

The companies getting real value from AI aren't the ones using the flashiest models. They're the ones using infrastructure they understand, control, and trust.

Ask hard questions. Demand clear answers. Walk away from vague promises.

Your business depends on it.