Automate your model input checks with an AI agent

What is your training data actually saying?

A companion takes the records marked won and lost and checks them against reality: were the wins really wins, are the losses actually still open, and does the field mean the same thing in 2023 as it does now.

That audit decides whether the whole exercise works. A model trained on a field that changed meaning halfway through will still produce a number, and the number will be trusted.

Features that come from outside your database

Whether the company is hiring for a role your product serves, whether they use a technology that implies a fit, whether they just opened a market you sell into. None of it is in your CRM.

A companion gathers those per account and adds them as columns before training, which is the way to improve a model without more historical data. That research pass is enrich contact data automatically.

• External signals gathered per account with the source recorded, so a feature can be traced.
• Rows the companion could not research reported as blank rather than imputed.
• Predictions read back and written where the team actually works.

How do you sanity-check a prediction?

By reading the top and bottom of the list.

A companion opens the twenty highest-scoring records and the twenty lowest and checks each company.

If the top twenty include three competitors and a university, the model has learned something about your data collection. That is a fifteen-minute check that saves a quarter of misrouted leads, and it costs forty tabs nobody wants to open.

What a prediction does not give you

A reason.

A score of 0.82 says the model found this record similar to past wins. It does not tell a rep what to say.

A companion writes the second half: a short research note on why that specific account looks promising, attached to the score. The account-level version is research a prospect in five minutes.

Frequently asked questions