Question 1

How do you assess AI-native readiness?

Accepted Answer

We look at the customer interaction data, the product event schema, the embedding-store strategy, and the privacy posture customers will require. AI-native product features are easier with a clean data foundation; harder retrofit on a messy one.

Question 2

What is the typical SaaS AI readiness gap?

Accepted Answer

Most often the customer event schema is inconsistent across product surfaces, the customer identity is duplicated between billing and product systems, and the privacy posture for AI training data is undefined. Closing these is 8 to 16 weeks of work.

Question 3

How long does AI readiness implementation take?

Accepted Answer

30-day Analytics Truth Audit ($35,000 fixed). Implementation work to close the readiness gaps typically runs 10 to 20 weeks at $130,000 to $360,000.

Question 4

What does AI readiness actually mean?

Accepted Answer

AI readiness means the data foundation is clean enough, the governance is defensible enough, and the metric layer is consistent enough that AI deployment lands without producing the kind of hallucinations or compliance issues that block production use. The work is mostly at the data layer.

Question 5

Do you build AI models or just the data layer?

Accepted Answer

We focus on the data foundation that supports AI deployment. Model selection, training, and tuning we coordinate with the client's AI team or specialist partners. The data layer is where most AI pilots stall.