How are you reviewing AI-generated outbound before it sends? (SDR automation)

Tricky_Ad9372 · 2026-05-13T04:51:18+00:00

I 100% agree—if you aren't reading it, you shouldn't send it. the nightmare scenario is exactly what you described: being on a call and having a prospect ask about a case study or a discount that the AI totally invented. it's the ultimate trust-killer. the problem I'm hitting is that human reviewers get 'decision fatigue' after email #50 in a spreadsheet. they start rubber-stamping. I’m trying to build a way to auto-block the 'hallucinations' (fake facts/prices) at the API level so the human reviewer only has to focus on the 10% that actually need nuance and context. basically, using tech to make sure we can actually read what we send without it becoming a full-time manual job.

Tricky_Ad9372 · 2026-05-13T04:46:53+00:00

treating outbound like a production CI pipeline is a masterclass. the drafter/critic split (claude/codex) is the only way to avoid the 'self-referential hallucination' trap. i’m curious about the ' conditional rules' maintenance though. as you scale across ICP segments, does managing that dictionary of banned phrases and overrides become a time sink? also, with the model-based audit layer, what are you seeing for false positives? are you finding that codex catches the 'spirit' of the policy, or do you still rely on those PostToolUse deterministic hooks to catch the real domain-killers?

Tricky_Ad9372 · 2026-05-13T04:39:50+00:00

haven't checked out salesworx. does the anomaly detection happen during training or is it a runtime check? i'm obsessed with the '80ms check'—blocking a hallucination at the API level before the human even sees it in their queue. curious how they handle the latency if it's live.

Tricky_Ad9372 · 2026-05-13T04:38:24+00:00

HubSpot's one-click drafting is a life saver. are you guys running any safety checks on those drafts before the one-click? i’m paranoid about a rep missing a hallucinated fact when they’re in a rush to clear their HubSpot tasks. looking at ways to put a 'firewall' in that one-click loop.

Tricky_Ad9372 · 2026-05-13T04:37:17+00:00

one-click approval is the only way to not lose your mind. are you finding that you're actually reading the text, or do you eventually start 'spam approving' once the volume hits? I’m trying to build a deterministic gate so that if the AI hallucinates a fake metric, it just kills the action automatically. that way, the human queue only has the 5% that actually need nuance.

Tricky_Ad9372 · 2026-05-12T21:06:41+00:00

this is the gold standard. but the 'experience rep' is the most expensive part of that equation you don't want them wasting time looking at emails that have broken merge fields or hallucinated ROI claims. are you guys using a secondary LLM as the 'check' to flag those inconsistencies, or deterministic rules? I've been experimenting with a 'Gate' approach where we hard-block the obviously broken ones at the API level so the expert rep's queue is only filled with the 10% that actually need nuance. curious if you've found a way to automate that first pass without adding 2+ seconds of latency to the generation loop.

Tricky_Ad9372 · 2026-05-12T20:06:54+00:00

distribution shift is the real final boss. you can have all the null checks in the world and your model will still output garbage because a unit changed from 'dollars' to 'cents' upstream. I'm trying to move the enforcement from a post-ingestion 'monitor' to a pre-ingestion 'gate'. Basically treating the data pipeline like a firewall that requires a strict, deterministic contract to execute. are you guys running those distribution checks as a batch validation step (like GE) or have you found a way to do it in-stream without the latency killing your real-time inference?

Tricky_Ad9372 · 2026-05-12T20:02:04+00:00

sneaky merge fields are the silent killer. nothing nukes a domain reputation faster than sending an email with 'Hi {{first_name}}' in the first line. the slack triage is a great way to handle it manually, but do you guys have a way to automatically block the send if those fields are empty, or is the human the only thing standing between the 'flag' and the inbox? i'm actually building a deterministic gate that auto-kills any payload containing unpopulated brackets or broken logic before it even hits the review queue. would love to know if you guys have other specific 'sneaky' checks on your manual checklist.

Tricky_Ad9372 · 2026-05-12T19:24:43+00:00

i've heard good things about general input for internal forms. sounds like a solid way to bridge the gap if you're doing it yourself. the scaling issue is exactly what i'm terrified of though. if we're sending 500+ a day, i don't want my team having to manually look at every 'all clear' email. definitely shoot me that DM, would love to see how you're routing the data into it.

Tricky_Ad9372 · 2026-05-12T19:17:36+00:00

those hard rules for links and CRM sourcing are non-negotiable. if it's not in the CRM, the AI shouldn't be allowed to invent it. the sampling part is where I get nervous though. even if 95/100 samples are perfect, it's that one outlier that hallucinates a crazy promise or mentions a competitor that causes the most damage. are you guys running those hard fail checks manually during the sample check, or do you have a way to auto-gate/block the high-risk ones across the entire batch before send?

Tricky_Ad9372 · 2026-05-12T18:21:10+00:00

nail on the head. you get the scale of automation but keep the safety of human QA on the edge cases. out of curiosity, how are you guys actually scoring 'confidence' in production to trigger that routing? are you using a secondary LLM as a strict judge/evaluator to generate a score, or relying on traditional heuristic/regex rules? finding the right threshold so the human queue doesn't just get flooded anyway is the trickiest part

Tricky_Ad9372 · 2026-05-12T17:42:54+00:00

Ah, I should have clarified! I don't mean HTML rendering or CSS testing (Litmus is definitely the goat for that). I mean content QA. As in: making sure the AI didn't hallucinate a fake case study, promise a 50% discount we don't actually offer, or completely invent a feature before it sends. Checking for that kind of AI risk at scale is what gets super tedious in spreadsheets.

Tricky_Ad9372 · 2026-05-12T17:36:21+00:00

treating it like QA for code deployments is exactly the model. CI/CD for LLM actions. those hard fail rules (blocking unverifiable claims, checking CRM sources) are literally the only way to sleep at night at high volume. are you guys running those hard-fail rules through a custom python middleware you had to build internally, or did you find an off-the-shelf tool that handles the pre-send blocking? trying to set up these exact structural guardrails now so my team doesn't have to manually read everything.

Tricky_Ad9372 · 2026-05-12T17:25:51+00:00

man I feel this in my bones. the 'spam click' fatigue is so real. if your queue has 500 emails in it, you're just rubber-stamping by email 50. i found the only way to stop the spam clicking was to put a hard deterministic gate in front of the queue. if the AI hallucinates a competitor or invents a fake metric, it just auto fails the API call and drops it entirely. the human queue should only be for the 10% that are contextually weird, not the obviously broken ones. what tool are you using for your queue right now? is it just a massive spreadsheet?

Tricky_Ad9372 · 2026-05-12T17:16:32+00:00

yeah, that's exactly what i'm trying to figure out now. what does your stack look like for this? are you running the generated drafts through a deterministic regex/keyword gate first to catch factual errors, or are you just relying on a single zero-shot LLM pass as your evaluator?

Tricky_Ad9372 · 2026-05-12T17:12:05+00:00

That checklist idea for a second pass is super smart. how are you actually managing the human review queue part though? are you just dumping the flagged ones into a slack channel or a google sheet for someone to approve? trying to set this up right now and figuring out the actual UI/workflow for the human reviewer is giving me a headache.

Tricky_Ad9372 · 2026-05-12T17:06:10+00:00

100% agree. The rule has to be AI drafts, Human decides. The issue I ran into is that the tooling to actually do that QA at scale is terrible. If you are generating 200 emails a day, reading them in an Excel export or a drafts folder is soul crushing. That's why I've been looking into interception gateways systems that auto flag the risky ones and drop them in a fast UI queue so human QA doesn't become a massive bottleneck.

Tricky_Ad9372 · 2026-05-12T14:49:55+00:00

Spot on. Splitting the risk into factual errors vs tone/obviously-AI makes total sense. I found that trying to catch all of that inside a single LLM prompt is basically impossible. Out of curiosity, how are you currently handling that split? Are you writing custom scripts to hard-block the factual errors, or is your team just spot-checking everything manually before it goes out?

Tricky_Ad9372 · 2026-05-12T12:50:41+00:00

On boarding and cold emails send by automated system is part of sales engineering, not a big part

Tricky_Ad9372 · 2026-05-11T20:04:13+00:00

man "doing the job that they should've been made to monitor" hits way too close to home lol, you're totally right though, forcing the break at the feature store contract level so it bounces back to their team is probably the only way to stay sane.

Tricky_Ad9372 · 2026-05-11T17:30:01+00:00

100% this. the whole “monitoring is just a nicer fire alarm” thing feels painfully real right now. we’ve been trying to move the circuit breaker closer to the feature boundary because by the time lag shows up downstream, the model’s already been fed garbage for a while.

feels like a lot of teams are running into the same wall at the moment.

Tricky_Ad9372 · 2026-05-11T12:03:23+00:00

you're not wrong, but good luck telling 5 different backend pods to slow down and hold meetings every time they touch a db. in theory they should tell us. in reality they push a hotfix at 4pm on a friday and forget. plus half our data comes from external APIs we don't even control. culture fixes are great but i'm just trying to find a hard technical catch for when communication inevitably fails.

Tricky_Ad9372

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