How is a GTM eval different from an A/B test?

An A/B test needs the output to ship and an audience to see it, which costs weeks and real pipeline. An eval grades the output before it ships, against evidence you already have. The two compose: evals decide what is worth testing, A/B tests confirm it in market.

We already have RAG over our CRM. Do we still need evals?

Yes, and that is precisely the gap. Retrieval answers questions; it does not judge output. A team with retrieval and no evals has agents that can look things up and still cannot say whether a draft is good. The eval layer is the half of the stack retrieval cannot provide.

Who grades the output?

A judge model, constrained to a rubric and to retrieved buyer evidence, with a binary verdict per rubric line. The judge never grades from general knowledge: every verdict cites the quotes it was graded against, so a human can check any line in seconds.

GTM evals

GTM evals are automated checks that grade go-to-market output (messaging, outbound, content) against real buyer evidence before it ships.

Evals are how software teams keep AI systems honest: define what good looks like, run every output against that definition, ship only what passes. GTM evals apply the same discipline to go-to-market work. A draft (a cold email, a landing page, a battle card) is graded against evidence from real won, lost, and stalled deals, and the verdict cites the exact calls and CRM records it was graded against.

The reason this matters now is agents. A GTM team running agents in production has solved retrieval (the agent can look things up) but usually has nothing that checks the output. The result is confident drafts nobody can vouch for. An eval layer closes that loop: every output gets a pass/fail verdict per rubric line, with the failing lines pointing at the evidence that contradicts them.

The grading is binary per check, not a vibe score. A rubric line either passes or it does not, and the overall grade is the fraction that passed. That keeps the verdict auditable: a reviewer can open any failed line and read the buyer quote that failed it.

The Amdahl view

Amdahl is the search and evals API for GTM: agents and workflows call it over REST (or connect over MCP) and get back cited answers from buyer data plus eval verdicts graded against it. Retrieval is the half most teams have already built. The eval half is the one nobody has, and it is the half that decides whether agent output can be trusted. That is why evals lead our story rather than search alone.

In practice

What GTM evals actually looks like in real product work.

01
An SDR agent drafts 50 openers overnight; the eval gates them, and only the ones grounded in a real pain quote reach the rep.
02
A launch page is graded against every competitive mention from the last quarter's calls before the CMO reads it.
03
A prompt change is judged by re-running the same eval, so the team knows the rewrite improved the output rather than just changed it.

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