We have built an automated customer onboarding system using large language models, but the buyer is worried about model drift and accuracy. How do we prove that our probabilistic AI workflows are stable and transferable assets during their operational due diligence?

Question

Tyler Smith · Accepted Answer

To convince a buyer that your automated customer onboarding system is a valuable, stable asset rather than a liability, you must address the core concern of AI engineering: the probabilistic nature of language models. Buyers fear that a system predicting token likelihoods can behave unpredictably, leading to operational errors or customer dissatisfaction post-sale.

To de-risk this asset during due diligence, present three concrete elements:

- Evaluation datasets and benchmarks: Show the buyer your historical test sets. Prove that you have run thousands of completion tasks through your system and tracked the accuracy and response quality over time. This data demonstrates that you are measuring and managing model drift systematically.
- Guardrails and monitoring: Document the specific software guardrails you have built around your language models. Explain how you filter prompts and validate outputs before they reach the user. This proves that you have mitigated the risks of hallucinations and security breaches.
- Standard operating procedures: Show how these AI tools are integrated into your Accountability Chart. A buyer needs to see that a human is still in the loop, managing the system and resolving anomalies through structured Level 10 Meetings™.

By presenting a structured, engineered approach to your AI workflows rather than just treating them as black-box magic, you transform a perceived risk into a highly transferable operational asset that justifies a premium multiple.