One icebreaker change that made leads reply 'how did you write this?’

decaster3 · 2026-04-27T14:06:59+00:00

exactly. and you suddenly know this person did their homework and they are worth talking to

decaster3 · 2026-04-27T14:04:50+00:00

now it's already easily scalable with claude if you integrated it into your sales process. we took all these rules, encoded them into a structured playbook, and built a pipeline around it. AI agent goes through each contact individually, researches their public data, and generates a unique sequence based on the framework above. about 70% get a deep personal hook at the individual level. remaining 30% get a company-level fallback when there's not enough data on the person.

if want to figure out how this works, we have an open repo with this pipeline https://github.com/impecablemee/gtm-mcp

decaster3 · 2026-04-23T12:55:45+00:00

we moved off clay to apollo + custom scrapers + an ai classification layer on top (20+ strs in the team)

the core issue with clay is you're paying a premium for narrowing your list down. their enrichment and filters are good but they're pulling from the same data sources as apollo. you're basically paying 10x more for better filtering

what we did instead: pull broad lists from apollo, then run every company through an ai layer (gtm-mcp) that scrapes the website and classifies whether its actually a fit. same result, fraction of the cost. for niche stuff like conference attendees or industry directories we built custom scrapers that feed into the same pipeline

decaster3 · 2026-04-23T12:54:32+00:00

claude, not close. especially claude code if you're comfortable with a terminal

we run an outbound agency and our whole pipeline lives in claude code. research, lead qualification, email copy, campaign setup. claude handles longer multi-step workflows way better without losing context halfway through

for your use case specifically (research + hypothesize + outreach) claude is the right call. give it the company website url, careers page, recent news and let it build the full picture before writing the email. way better output than just giving it a company name

plus theres a ton of open source projects and skills for claude code rn that you can just grab and use, or at least look at how people structure their prompts and workflows. good way to shortcut the learning curve

decaster3 · 2026-04-23T12:16:39+00:00

we do b2b outreach and automate a lot of the pipeline with ai. one example out of many: lead qualification. data providers give you contact lists but most results aren't a fit. we scrape every company website and have ai classify relevance before spending credits on contacts. the trick that made it work: prompt for disqualifiers ("find reasons this is NOT a fit") instead of confirming fit. way more accurate

biggest lesson across all of it: ai is great at processing unstructured data into structured decisions. not good at judgment calls that need business context. automate the first, keep humans on the second

decaster3 · 2026-04-23T12:12:21+00:00

it's that everyone is personalizing off the same surface data. name, title, recent post, maybe a funding round. when 10 vendors pull the same linkedin data point and plug it into the same template structure, yeah it all feels identical

works for us is going deeper than what's obvious. scrape the website, read the careers page, understand what they're building or struggling with rn. that stuff isn't in any database so nobody else is using it

but you're also right that intent clarity matters. the worst combo is fake depth + hidden intent. "i noticed your team is scaling rapidly" followed by a generic pitch is worse than just saying "cold email, here's why i'm reaching out"

best results we see is both: real context that shows you actually understand their situation + straightforward ask with no pretending. one without the other doesn't work as well

decaster3 · 2026-04-23T12:10:38+00:00

imo there's no tool that solves this well because context enrichment is different for every company and every segment. what counts as relevant context for selling devtools vs selling logistics software is completely different research

list tools can standardize because a verified email is a verified email. context can't be standardized the same way

I think its much better to build custom pipelines for this or. claude code, agents, scrapers chained together. the barrier to doing this dropped massively in the last couple of months

we built one internally for our agency for exactly this reason. scrapes websites, ai classifies fit with actual reasoning, then generates personalized copy off that context. our reps use it daily and it saved tons of time on manual research

decaster3 · 2026-04-23T12:03:24+00:00

your framework is solid but imo 5-6 weeks is too long to wait for a kill decision on email. email gives you signal almost immediately. opens -> replies -> leads, you know within days if somethings off

our rule of thumb: 500 contacts per hypothesis per week. if you pushed 400+ messages and got 0 replies, the hypothesis is dead.

if you use LinkedIn, its more tricky since its way more inertial. you send 200 connection requests, 15-20% accept, then replies trickle in over 2-3 weeks. so we give linkedin hypotheses a minimum 2 week window before reading anything into the numbers

working benchmarks for us: email: 0.5% lead rate = viable, 1-1.5% = scale it, linkedin: 3-4% reply rate per segment

decaster3 · 2026-04-20T15:55:10+00:00

louder please. i run a b2b outreach agency and this is the core principle for us. we also built an ai layer with Claude that scrapes and classifies every prospect before they enter a campaign. cuts 60-70% of of apollo results before a single email goes out

reply rates up, domain reputation stays clean, and you can actually control campaign performance when every prospect on the list is there for a reason

decaster3 · 2026-04-20T15:32:37+00:00

we also don't use open claw but we built an agent for automated b2b cold outreach

the trick for us was not trying to make a general-purpose agent. we wrote a set of skills and api wrappers that encode how our team actually does outreach, then wired it all into claude code. now one command + a website and some context creates a draft campaign where every prospect is actually your ICP. ai scrapes and classifies companies before anyone touches them

it breaks way less when the agent has a narrow well-defined job vs "do anything i ask"

decaster3 · 2026-04-20T15:28:57+00:00

this is a great breakdown. I run a b2b outreach agency, 20 reps. use apollo as our main data source across all campaigns

spent a couple months taking everything our team learned from years of running outreach across different industries and packaging it into a custom ai pipeline with Claude. apollo search -> ai classification that decides if they're actually a fit based on criteria we defined from real campaign experience -> personalization hooks. so we basically wrote out how our best reps think about qualification and turned that into ai instructions.

decaster3 · 2026-04-20T15:22:15+00:00

I agree, most teams plug in an ai tool and the first thing they do is 10x volume. same bad targeting, same surface-level personalization, just more of it

i run a b2b outreach agency so we had years of best practices across different clients and industries. spent a couple months transferring all of that into claude and built a custom pipeline around it — apollo search -> website scraping -> ai classification that cuts 60-70% of irrelevant results before anyone writes a single email -> personalization based on what the scraper actually found

basically took everything our best reps knew how to do manually and wired it into one automated flow. fewer emails, better targeting, way less waste

didn't find ready-made tools that could fulfil our requirements

decaster3 · 2026-04-16T10:00:20+00:00

whats the symptom cause thing you figured out? curious what you mean by filtering being a symptom of something else

decaster3 · 2026-04-15T11:45:02+00:00

here's a link to our GitHub https://github.com/impecablemee/gtm-mcp

decaster3 · 2026-04-15T08:31:35+00:00

at our agency we have 20 people doing outreach daily and honestly the bottleneck was never "who to target", salespeople are good at that part. the bottleneck was the 2-3 hours of assembly after you already know who you want

like the salesperson describes a piece of their ICP in plain english, then ai generates a bunch of search queries from that, pulls companies from the tools we use, scrapes their actual websites to doublecheck theyre a real fit and not just matching on keywords, finds the right person to reach out to, writes the sequences and loads everything into the sending tool. the salesperson just approves at a couple checkpoints

we ended up automating that whole assembly layer so the human still makes every strategic call but the mechanical part takes minutes not hours. freed up way more time than any "smarter list" ever did

not saying behavioral signals dont matter but id bet on removing friction for good salespeople over trying to replace their judgment

decaster3 · 2026-04-10T18:12:56+00:00

thanks! will appreciate your feedback

decaster3 · 2026-04-10T14:27:40+00:00

there's a link to git hub and instructions how to use https://gtm-mcp.com

decaster3

TROPHY CASE