Stopping AI SDR Tools From Inventing Pricing and Features

Most hallucinated pricing or feature claims happen because the AI was asked to write persuasively with no constraint on what it can assert. Instead of prompting for a compelling email, feed it a short, current, and specifically approved list: exact pricing tiers, features that have actually shipped, and the two or three claims you're comfortable standing behind.

Anything the model wants to say that isn't drawn from that list gets flagged for a human to check before it sends, rather than generated freely and trusted.

Structured personalization variables (the prospect's company, role, and a specific trigger event pulled from verified contact data) are far safer than letting the model freely describe the prospect's business in its own words, which is where invented details creep in. A platform like Apollo can supply verified firmographic data as merge fields, and a sending tool like lemlist can slot those into a template without the model improvising the surrounding facts.

The rule of thumb: let the AI choose which proven angle to lead with, but don't let it invent the specifics of what the prospect's company does or what your product costs.

A weekly spot-check of sent emails catches problems too late; the damage to that prospect relationship is already done. Instead, route any AI-drafted email that references a price, a specific feature, or a claim about a competitor through a same-day human review before it sends, at least until you've built enough confidence in the guardrails to sample-check instead of review everything.

This review step is the single most effective guardrail available, because it catches errors before a prospect ever sees them rather than after. Track how many drafts get corrected each week: a rising correction rate usually means the approved facts list has drifted out of date, not that the model is getting worse.

A few patterns show up repeatedly:

• Pricing drift: the model states an old tier price or invents a discount that was never authorized.
• Feature invention: it describes a capability that's on the roadmap, not in the product, as if it already ships.
• Competitor claims: it states something false or outdated about a competitor's product to make a comparison land harder.
• Over-personalization: it infers something about the prospect's company, such as funding or headcount, that wasn't actually verified, just plausible-sounding.

Building a checklist from these four patterns and reviewing against it directly is far more effective than a general read-through for tone.

Despite guardrails, something will eventually slip through. Have a plan ready: a same-day correction email acknowledging the error plainly, sent from a real person rather than the automated sequence, and a note in the CRM against that contact so no one repeats the claim to them later.

Trying to quietly let it go usually costs more trust than a direct correction. Buyers evaluating software tend to forgive an honest, fast fix far more readily than they forgive a claim that turns out to be false during a later demo.

The facts list works only if one person is responsible for keeping it current, not a shared document nobody remembers to update after a pricing change. Whoever owns pricing or product marketing should update it the same day anything changes, and the AI SDR tool's prompt or configuration should pull from that document directly instead of a copy someone pasted in months ago.

A stale facts list is arguably worse than no list at all, because it gives the model, and the humans reviewing its output, false confidence that a claim has already been checked.