Why is it important to know a vendor's underlying AI model?

Understanding the underlying AI model helps you assess performance, data security, cost structure, and future scalability. It differentiates between true AI innovation and simple API integration.

What is the difference between a proprietary model and an API wrapper?

A proprietary model is developed and owned by the vendor, offering unique capabilities. An API wrapper simply uses a public API from a major provider like OpenAI or Anthropic without significant custom development.

How does fine-tuning an open-source model affect its performance?

Fine-tuning an open-source model allows vendors to adapt it to specific sales tasks and data, potentially improving accuracy and relevance over a generic model. This requires significant data and expertise.

Can a vendor refuse to disclose their model provider?

Yes, a vendor can refuse, often citing intellectual property. However, this lack of transparency should be a red flag during your evaluation, prompting deeper questions about their technical capabilities and roadmap.

What are the cost implications of different model providers?

Vendors using public APIs often have variable costs tied to usage, which they pass on. Those with proprietary or heavily fine-tuned models might have higher upfront costs but potentially more predictable pricing and specialized performance.

What Questions Reveal a Vendor's Real Model Provider

When evaluating AI sales tools, understanding the underlying model provider is crucial. Many vendors present their solutions as proprietary AI, but some are simply wrapping public APIs from major providers like OpenAI or Anthropic. Asking the right questions helps you distinguish between a truly innovative solution and a thin layer over existing technology. This distinction impacts performance, cost, data security, and your long-term AI strategy.

Key takeaway: To reveal a vendor's real AI model provider, ask direct questions about their model's origin (proprietary, open-source, or third-party API), how it's trained, and their data security protocols. This clarifies whether you are buying a unique AI solution or a re-packaged service, affecting performance, cost, and data handling.

Why Model Transparency Matters for Sales Teams

The core of any AI tool is its model. This model dictates its capabilities, limitations, and how it processes your data. Without transparency, you cannot properly assess the tool’s fit for your specific sales challenges. A vendor using a generic large language model (LLM) via an API might not offer the specialized performance a sales team needs.

Understanding the model also helps you evaluate the vendor’s long-term viability. A company that builds and maintains its own models demonstrates deeper technical expertise. A company relying solely on external APIs might be more vulnerable to changes in those providers’ pricing or service terms. This directly impacts your investment.

Direct Questions to Uncover the Model Provider

Start with straightforward questions that demand specific answers, not marketing jargon. Frame these as part of your technical due diligence. You are assessing their engineering depth, not just their sales pitch.

1. “What is the foundational model powering this solution?”

This question is direct. The vendor should be able to name the specific model or model family. Possible answers include:

“We use our own proprietary model, developed in-house.”
“We leverage a fine-tuned version of [specific open-source model, e.g., Llama 3, Mistral].”
“Our solution integrates with [specific third-party API, e.g., OpenAI’s GPT-4, Anthropic’s Claude 3].”

If they hesitate or give vague answers like “advanced AI” or “our own algorithms,” press for specifics. A lack of clarity here is a significant red flag.

2. “How was this model trained, and on what data?”

This question probes the model’s relevance to sales. If it’s a proprietary or fine-tuned open-source model, they should describe the training data. For sales AI, this ideally includes:

Sales conversations (calls, emails, chat transcripts).
CRM data (deal stages, outcomes, customer profiles).
Product documentation and sales playbooks.

If they use a public API, the answer will likely be “it’s pre-trained by the provider.” This means it’s a general-purpose model, not specialized for sales out-of-the-box. This impacts the quality of outputs for sales-specific tasks.

A vendor’s ability to articulate their model’s training data and methodology is a strong indicator of their technical depth and commitment to specialized AI.

3. “Do you fine-tune or customize the underlying model for our specific use case?”

This question differentiates between a generic API wrapper and a value-added service. Even if they use a public API, some vendors might fine-tune it with customer-specific data. This can improve performance for your unique needs.

If yes: Ask about the fine-tuning process, data requirements, and how often models are updated.
If no: Understand that you are getting a standard output. This might be sufficient for some tasks but less effective for nuanced sales interactions.

For more on assessing output quality, see How to test an AI tool’s output quality before buying.

4. “Where is the model hosted, and how is our data handled?”

Data security and privacy are paramount. The hosting location and data handling practices vary significantly based on the model provider.

Model Type	Hosting Location	Data Handling Practices
Proprietary (in-house)	Vendor’s cloud	Vendor controls all data. Ask about encryption, access controls, and data retention policies.
Fine-tuned Open-source	Vendor’s cloud	Vendor controls fine-tuning data. Ask about base model’s data privacy and vendor’s policies.
Third-party API (e.g., OpenAI)	API provider’s cloud	Data sent to API provider. Ask about their data usage policies (e.g., for training) and vendor’s anonymization.

Ensure their answers align with your company’s data governance policies. Pay close attention to whether your data is used to train the underlying model, especially with public APIs.

5. “What is your roadmap for model improvements and updates?”

A vendor with a proprietary or heavily fine-tuned model should have a clear roadmap for improving their AI. This demonstrates ongoing investment in their core technology.

Are they planning to update to newer versions of open-source models?
Are they researching new architectures or training methods?
How do they incorporate customer feedback into model development?

If they rely on a third-party API, their roadmap might simply be “we’ll upgrade when [API provider] releases a new version.” This indicates less control over their core product’s evolution.

Recognizing Red Flags and Vague Answers

Be wary of vendors who:

Use buzzwords without substance: “Cutting-edge neural networks,” “advanced machine learning,” “proprietary algorithms” without specific details.
Avoid direct answers: Redirecting to marketing materials or general statements about “AI capabilities.”
Claim 100% accuracy: As discussed in How to spot inflated accuracy claims in a demo, no AI is 100% accurate. This suggests a lack of understanding or transparency.
Cannot explain the technical architecture: The sales rep might not know everything, but they should be able to connect you with someone who can.

If a vendor cannot clearly explain the technical foundation of their AI, it suggests either a lack of transparency or a superficial understanding of their own product.

The Impact on Your Sales AI Strategy

The answers to these questions directly influence your AI strategy.

Performance: A specialized, fine-tuned model will likely outperform a generic API for sales-specific tasks.
Cost: API-based solutions often have usage-based pricing, which can become unpredictable. Proprietary solutions might have more stable, subscription-based costs.
Data Security: Understand where your data goes and who has access. This is non-negotiable.
Future-proofing: A vendor with strong in-house AI capabilities is better positioned to adapt to future market changes and develop new features.

Before approving any AI vendor, ensure you have a clear understanding of these technical underpinnings. Refer to What your legal team should check before approving an AI vendor for further considerations. Your due diligence here will save time and resources in the long run.