How to evaluate AI SDR alternatives
Nearly every “best AI SDR tools” list you will find was written by an AI SDR vendor. That does not make them useless, but it does mean the evaluation criteria have been chosen by people who already know which criteria they win on. Here is a vendor-neutral checklist instead — the questions worth asking whoever you end up buying from, including us.
1. Ask where the data comes from
There are two models. Either the tool resells a contact database it licenses in bulk, or it searches the live web when you ask. Databases are cheap, instant and stale — the same records are sold to everyone in your category, and contact data decays a few percent every month. Live search is slower to build and produces fresher, more defensible lists.
What to ask: “If I search for a company founded last month, will it be in your system?”
2. Ask to see the receipts on a buying signal
Intent scores are the easiest thing in this category to fake, because a number is unfalsifiable unless you can trace it. Ask the vendor to open a warm account and show you, for each contributing signal, the exact sentence and the URL it came from.
What to ask: “Show me the source of that score. Now click through to it.”
3. Ask what stops fabrication
Every AI writer will occasionally invent an award, a funding round, a mutual connection or a statistic. The question is not whether the model hallucinates — it is what sits between the model and your prospect’s inbox. Look for a grounding rule that limits the writer to facts in the evidence, plus an automated validation pass before a human ever sees the draft.
What to ask: “What happens to a draft that contains a claim you cannot source?”
4. Ask what the autonomy settings actually are
“Fully autonomous” and “human-in-the-loop” are both marketing positions. The useful middle is granular: each capability set to off, draft or auto independently, a quality floor nothing gets under, daily caps, and one switch that stops everything. Beware tools that only offer all-or-nothing, in either direction.
What to ask: “Can I let it send follow-ups automatically but keep first-touch behind approval?”
5. Ask what happens when something fails
Research passes fail. Enrichment comes back empty. A page 404s. Find out whether you are billed for the attempt, and whether the system guesses or escalates when it is unsure. Guessing is how fabricated emails get sent.
What to ask: “Do I pay for a failed action, and what does the AI do when it does not know?”
6. Ask how you are billed
Per-seat pricing is a hangover from software people use. If the software is doing the work, seats are a poor proxy for value — you end up paying for logins rather than output. Pricing per unit of work is more honest but only if the rates are published; a “credits” system with unpublished burn rates is worse than either.
What to ask: “Show me the published cost of one researched, personalised email.”
7. Ask about LinkedIn specifically
Some tools automate LinkedIn through unofficial means. It works until the account gets restricted, and it is your rep’s account, not the vendor’s. A compliant approach has the AI draft and a human send.
What to ask: “Is your LinkedIn integration official, and who carries the risk if an account is restricted?”
8. Ask the compliance questions properly
If you sell into the UK or EU you need a lawful basis for processing, suppression and do-not-contact handling, a working one-click unsubscribe, and a physical address in commercial email. Ask to see all four in the product rather than in a PDF.
Ask every vendor the same question: “What is the most common way customers get a bad result with your product?” Vendors who have watched real deployments answer immediately and specifically. Vendors who have not will tell you there isn’t one.
Scoring sheet
| Criterion | Weak answer | Strong answer |
|---|---|---|
| Lead source | Licensed database, refreshed quarterly | Live web search at query time; data is yours |
| Intent evidence | A proprietary score | Quote plus source URL on every signal |
| Fabrication control | “Our model is very accurate” | Grounding constraint plus a validation pass |
| Autonomy | On or off | Off / draft / auto per capability, caps, kill switch |
| Failure handling | Unclear; billed anyway | Refunded; escalates instead of guessing |
| Pricing | Per seat, or unpublished credits | Published rate per unit of work |
| Unofficial automation | AI drafts, human sends |
Where GTMHack sits
We built the product around those answers, so we will obviously score ourselves well — take the checklist to our competitors and make us earn it. The parts we are confident about: signals carry a quote and a URL, leads come from live web search, drafts are grounded and validated, autonomy is per-capability behind a kill switch, failed actions are refunded, and the cost of every action is published. The parts we are not claiming: we do not hold SOC 2 or ISO 27001 certification today, and we say so on our security page rather than implying otherwise.
Run the checklist on us
Bring the seven questions to a demo. We will answer them on the call, in the product, not in a deck.
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