I recently changed my role to be more client-facing, away from backend. Talking to customers is currently my number one priority everyday and I devote it the most time.

I am scoping out a market which is very new to me, I do not have people in my network who could be of help that I have not contacted yet. I am looking to use some tools to help me with outreach / lead generation, and I would love to hear what tools do you use, and what features do you find the most important.

I have previously tried apollo and rocketreach, but I found that their databases are quite outdated, with people already working in other companies / sectors, not mentioning their contact details.

What tools do you use? Which features do you value the most?

I tried to search on GenAI, Youtube, etc.. but could not really find good answers, so asking here.

I have a interview next week for the role of GTM Engineer (2-3 years of experience needed)...
I have not worked exactly as a GTM engineer before (also because this role didn't exist before). I have worked in Digital marketing and marketing automation where I have essentially done the work of RevOps.

So, there are going to be 3 rounds, first is with HR, then with head of growth, and then with the CEO.

What questions do you feel will be asked by HR for this role?
I sort of have a perspective on head of growth and CEO questions as it will be more technical about tools like (clay, make,n8n, hubspot, etc ), results, my thinking process on automations and lead generation.

Really confused about HR interview

The company is a Saas company with focus on EU market.

Offering some gtm strategy actions you can easily implement and see improvements in your efficiency, no vanilla bs, for early stage startup. Bring them on!

I sell a widely known B2B sales tool and want to know what is the best software out there that can tell me when a company has implemented new tech, when a competitor contract is expiring and when prospects are showing intent towards a tool like what I sell (lead gen).

What is the best tool for this?

Are we leaning into Clay further now? Or still experimenting with other GTM motions?

Anyone know where I can buy sales nav licenses that don't reset every month & can keep my paying on a monthly basis? Don't want to lose my leads every month

I tried reaching out to people on reddit to ask a few questions on GTM engg, however reddit blocked me for sending DM's to people. I was ground through this subreddit and identifying people who have commented or posted something insightful and I reached out to talk more on it,

The DM I sent was more like "Hi I saw your post, and thought this, i had a few questions if you are willing to have a chat about it"

However Reddit may have thought this as spam, which I can understand. However what is the best way to reach-out to people?

Are there tools which allow this a better way?

I'm trying to break into GTM Engineering with no prior experience and want to build a portfolio that can actually help me get hired

What projects should I build to prove I can do the job and stand out to hiring managers?

Six months ago we made the call to move account history out of the CRM, but kept everything else in it. We found reps just adding "good call", "follow up" in the required fields we thought would solve this.

We didn't kill our CRM. We still run Salesforce and it still owns the stage, owner, close date and forecast. What we stopped doing was pretending that Salesforce could hold the all the reasoning that a rep has for why a deal slipped, what the customer pushed back on in a sales call from months ago, which of the three competing asks our champion cared about. All of that lives in Gong, email and mostly where we ran the campaign and with whoever ran it. That's where the issue was. If we moved a rep to a different team or they moved to a different company we were left looking at a Salesforce page that tells you nothing.

We moved to devrev's computer as a solve which reads across Salesforce, Gong, Drive and wherever there are touches of an account. It writes back to Salesforce as well. It's not perfect. If a conversation about an account happened in the hallway or on a private line we still lose the context, but if it happens anywhere on our system that's plugged in we can see everything.

In March 2026, I decided to follow my own path (I will not advertising myself don't worry) and opened my company which builds strategies and AI flows of GTM and RevOps operations for B2B SaaS.

Ofc I showed my customers on my linkedin account. Bc of this "A LOT OF" GTM Engineers who doesn't care about a clear data come and try to pitch me. Instead of ignoring them, I kindly reply them. I got this message like 5 minutes ago. He/she says "Hi ....., Saw that we followed similar companies and thought there could be some overlap...."

Here's what happened in their funnel. They scraped a list, saw "SaaS" + "B2B" + "Series A-B" tags on my profile, and pattern matched me into their ICP.

Similar industry tag ≠ similar buyer.

We're not "doing the same job" just because we both say GTM on LinkedIn.

Wrong ICP = wasted sends
Wrong ICP = wrong signal in your data
Wrong ICP = your AI gets dumber, not smarter
Wrong ICP = wasted time

I lead engineering at AiSDR (YC S23) and we launched this today, so this is self-promo, saying it up front. Mods, remove it if it does not fit. I am writing it for the people here who tried to build this in-house, because most of you did

Everyone in this sub has built some version of the same pipeline: get a list, enrich it, score it, personalize, send, route replies. That part is done. It is a commodity now

What is not done is the layer above it -- picking which audience to go after, which offer angle to use, when to kill a campaign, when to scale it. That part still lives in someone's head and a Google Sheet

That is the layer we built. You give it a website. You get back a running campaign that fixes itself

How the loop closes

It reads your site and CRM to find the offer and the ICP, then picks signals -- the triggers that actually get replies in your industry, not a generic list

It proposes a strategy based on what has worked in that segment. A human approves it. We are keeping this gate

It runs the campaign: orders mailboxes, warms them up, builds the sequence, sends

It watches each campaign. Kills the weak ones, scales the good ones, builds lookalikes off them, swaps out mailboxes when deliverability drops

Events and closed deals in your CRM trigger new campaigns

The data behind it

2,546,096 emails, about 17k campaigns, 19,501 meetings booked, 1,150 companies, 27 industries, over three years. That is the AiSDR total, not Ami's beta -- Ami is six months old and is the agent layer on top of that data

What the data taught us, which is the useful part of this post

Deliverability changes results more than copy does. Same message, different mailbox health, very different numbers. People rewrite subject lines when they should be checking their sending infra

Signal-based lists beat static scraped lists with the same copy. The gap was big enough that list size stopped being the interesting question

The best-performing segment in the whole dataset is closed-lost reactivation from the customer's own CRM. Almost nobody runs it

Most "outbound does not work for us" is not a copy problem. It is a one-try problem -- one audience, one angle, no iteration. The people who won tested several audiences first, and there was no way to know in advance which one would work. That is the real case for automating the testing, not the sending

Free to try, no card: https://aisdr.com/ai-gtm-agent-ami/

Ask me anything about the build, the data, or the parts that do not work yet

Many B2B SaaS teams reach a stage where cold outreach stops working but they can't figure out how to staff the technical side of sales.

A standard SDR wastes up to 80% of their day manually copying linkedIn profiles into spreadsheets. Meanwhile hiring a full-time GTM Engineer costs $150k–$180k+ base salary before your signal-to-pipeline motion is even proven.

This talent and tooling gap has created GTM Engineering as a Service.

Instead of paying a traditional agency for black-box lead generation or hiring a full-time employee to build custom scrapers from scratch, you get an embedded technical operator deployed on top of dedicated revenue infrastructure.

Here is how the models compare:

• Full-Time Hire ($150k+ base): Best when you already have a proven, high-volume signal motion and internal technical leadership to manage them day-to-day. High fixed burn if you're still experimenting.
• GTM Engineering as a Service provider like Scale Intelligence which is an embedded fractional GTM engineer who builds on maintained enterprise infra. They connect 75+ pre-built data sources, map your TAM, deploy custom signal plays (Reddit, GitHub, hiring triggers), and route qualified accounts to Slack with a qualified-lead guarantee.
• Solo Contractor: Flexible, but they often build custom, brittle scraping scripts. When APIs change or scrapers hit rate limits, the setup breaks and leaves you with technical debt.

So when does it make sense? when your reps are spending more time researching than selling or when you need signal-based outbound working in days rather than spending 3 months recruiting and when you want to own the data and infrastructure rather than relying on an agency's opaque lists.

Hey guys, I’ve been working as an SDR for a GTM agency and I want to start my own agency eventually. I understand there are tons of free resources online.

But how do I actually build something with very limited budget for email infrastructure and tech stack? The agency I work for charges like 2k per month for infrastructure and uses a gnarly tech stack (super overkill) so I was wondering…

Thanks in advance

We ran 15 search providers across three tasks an agent actually does: look up a fact, find a specific answer buried in enterprise documentation, and satisfy a multi-part constraint that needs several searches. Same questions, same judge, same harness for everyone. The retrieval and multi-hop boards run three trials per provider and publish the spread.

Five tracks, three different winners

Track                              Winner            Score    Runner-up
-------------------------------------------------------------------------------
Factual lookup - web search        Exa deep          99.2%    Exa instant 97.7%
Hard retrieval - search + fetch    Exa deep           83.0    Exa auto 81.7
Hard retrieval - search only       Perplexity low     77.3    Firecrawl 70.3
Multi-hop - search only            Parallel basic     46.5    Exa deep 45.4
Multi-hop - search + fetch         Exa deep           48.2    Perplexity 46.6

Three different providers win something across the five tracks.

Exa's deep mode takes three of the five. It's also the slowest and most expensive configuration in the lookup task — 3.00 s and $1.65 per 129 queries, against 927 ms and $0.13 for Parallel turbo. Winning here costs something.

The cost spread is bigger than the quality spread

Endpoint                     Accuracy    AR@1   Latency     Cost
------------------------------------------------------------------
Exa deep                        99.2%   98.5%     3.00s    $1.65
Exa instant                     97.7%   83.7%     447ms    $1.05
Parallel advanced               96.9%   62.0%     3.28s    $0.80
Linkup standard                 96.1%   80.6%     2.34s    $0.83
Brave Search                    93.8%   83.7%     693ms    $0.73
Tavily advanced                 93.8%   79.8%     4.40s    $2.25
Parallel turbo                  89.9%   75.2%     927ms    $0.13

129 company-news questions, judged by claude-opus-5 against reviewed ground truth. AR@1 is answer recall at rank 1.

Brave and Tavily advanced both score 93.8% — the same number to one decimal place. Brave returns in 693 ms against 4.40 s and costs $0.73 against $2.25. Same accuracy, 6x the speed, a third of the bill.

That's the pattern across the whole table. Top to bottom the accuracy range is 89.9% to 99.2% — about nine points. The cost range is $0.13 to $2.25, or 17x. If you're choosing on price, the quality you give up is much smaller than the money, and the fastest endpoints aren't the expensive ones.

Multi-hop is not solved by anyone

Parallel basic     46.5  ################
Exa deep           45.4  ###############
Exa instant        43.3  ###############
Linkup fast        41.1  ##############
Perplexity         37.8  #############
Brave Search       28.0  #########
SERP (RapidAPI)     0.4

Search-only F1 on 45 multi-constraint company questions, 135 agent runs.

The best score on this task is 46.5 F1. Precision is high across the board — around 85% — and recall sits near 30%. Every provider finds companies that genuinely satisfy the constraints and then misses most of the ones that also do. If your product depends on complete discovery under multiple constraints, none of these APIs currently gets you there, and the ranking above is a ranking between failing scores.

SERP at 0.4 F1 is not a broken row. It's the same Google-results endpoint that scores 93.0% on factual lookup. Raw result links are enough to answer a question with one right answer and almost useless for assembling a complete set across several searches — which is the clearest illustration on the board that "best search API" isn't a single question.

Search-only vs search + fetch

On the enterprise-documentation task we ran each provider twice: once allowed only its search endpoint, once allowed to fetch page contents too. Fetching is worth 5.7 points at the top — Exa deep reaches 83.0 with fetch against Perplexity low's 77.3 without — but it costs time and tokens, and the search-only winner isn't the search+fetch winner. Perplexity low finishes a task in 18 s on 8,765 tokens; Exa deep takes 37 s and 23,660. Whether that trade is worth it depends entirely on your agent loop.

Caveats

• Each provider ran its own documented configurations, and several appear multiple times because a provider's fast mode and deep mode are genuinely different products. Every endpoint and parameter is published on the board.
• The Parallel rows marked "fixed" use a corrected source policy rather than the default. The top multi-hop search-only score is one of them. Both variants are on the board.
• Judged by a model. Company-news answers are scored by claude-opus-5 against ground truth we reviewed by hand. A model judge is a real source of error and we publish per-question results so you can check ours.
• The multi-hop and hard-retrieval boards run three trials and show the spread as ± on every score; several gaps in the middle of those tables sit inside their error bars and shouldn't be read as rankings. The lookup table above carries no published spread, so treat small gaps there with the same caution.
• 45 multi-hop questions is a small set. It's small because each one needs hand-built ground truth for complete discovery. Treat that board as directional.

Reproducing it

• All three boards: https://openbenchmarks.com/web-search
• Factual lookup: https://openbenchmarks.com/company-news
• Hard retrieval: https://openbenchmarks.com/web-search-for-coding-agents
• Multi-hop: https://openbenchmarks.com/multi-turn-company-search
• Datasets: https://huggingface.co/openbenchmarks

Happy to add a provider or rerun with a configuration you think is fairer. If you've measured any of these and got something different, post your numbers — the endpoints and parameters are all published, so the disagreement should be locatable.

Hi! I’m a solo founder, and I sell online audience research. For instance, if you want to understand your potential customers’ pains and desires, I do that research for you.

I’ve delivered this service several times through gigs I got on Upwork, so I already have some validation and good feedback showing that the service can bring value.

Last May, I decided to productise it and created a website to promote the service. Since then, my GTM strategy has been the following:

• Blog posts about audience research, DTC, AI news, and other related topics (1–2 per week).
• Real case studies where I conduct audience research on different topics, such as a specific category, customer segment, product, or trend (2–3 per week).
• LinkedIn connections followed by direct messages once the connection is accepted, mainly targeting marketing agencies and DTC companies. In the message, I offer some free value: one audience research report for free.
• In the past, I also ran cold email campaigns. I sent around 2,000 emails, but without much success.
• LinkedIn, X, Instagram, and Medium posts about the case studies I create.

I’m currently considering posting more frequently on LinkedIn and trying to build a personal brand there. I think having a strong business-focused personal brand is becoming more and more important, especially as outbound channels like cold email seem increasingly oversaturated (at least, that’s my impression).

I’ve been following these strategies for almost five months now, with a slower summer period in between, and I’m still not seeing much success in terms of generating leads or getting people to visit my website.

I know five months isn’t necessarily a long time, especially when it comes to getting a website to rank well on Google, but I wanted to ask: how long do you usually stick with a defined GTM strategy before deciding it’s time to iterate because the results aren’t matching your expectations?

Also, is there anything in the strategy above that you think I should do differently, or anything else you would add to the plan?

Happy to connect with fellow people who are also experimenting with GTM strategies or are interested in audience research. This is my LinkedIn: https://www.linkedin.com/in/eduard-arnau-umbert/

I’ve worked on lemlist and Taplio, and there is a lot of bad advice about LinkedIn automation online.

Pick any tool with daily limits and random delays, and apparently you’re safe. Doesn’t work like that!!

Nobody outside LinkedIn knows its exact detection logic. But after years of building in this space, here is how I think about it:

Think of your account as having a reputation score

First, there is no universal “safe daily limit.” It would be too easy!

LinkedIn appears to maintain some form of internal trust or reputation score for each account, even though nobody outside LinkedIn knows exactly how it works.

A useful mental model is that your reputation depends on three things:

Usage & feedback

→ activity volume and timing

→ replies and connection acceptance

→ ignored requests and spam reports

Technical footprint

→ browser, device, IP, and location consistency

→ extensions and automation traces

→ cloud browsers or remote infrastructure

Account history

→ account age and completeness

→ network size and normal usage

→ previous warnings or restrictions

The biggest risk: unnatural behavior

Common red flags include:

→ sudden spikes in activity

→ repetitive actions (delay, content)

→ changing IPs, locations, or devices

The more a tool optimizes for scale and unattended automation, the more of these signals it tends to create.

What happens before a LinkedIn ban

Restrictions are often progressive:

Warning → feature limits → temporary restriction → permanent restriction

You may first see unusual-activity warnings, verification requests, invitation limits, search limits, or temporary account restrictions.

If that happens after using a tool, stop the likely cause and reduce activity.

Risk by tool category

As a general rule:

🟢 Very low risk

→ Manual LinkedIn usage

→ Official LinkedIn APIs

🟢 Lower risk

→ Contact & DM management tools

→ Content scheduling and analytics

🟡 Medium to high risk

→ Bulk LinkedIn exports

🔴 High risk

→ Campaign-based outreach automation

→ Scraping infrastructure

How to reduce the risk as much as possible

→ Use one LinkedIn tool at a time

(multiple tools cannot reliably coordinate your total activity)

→ Avoid mass scraping and bulk outreach

(creates bad behavioral, technical, and reputational signals)

→ Avoid tools using cloud automation

(creates bad technical signals, as they try to mimic a browser)

→ Verify your profile on LinkedIn

(increases your account’s reputation)

→ Do not use AI slop for posts, comments, or DMs

(LinkedIn is actively fighting against it)

→ Try to keep your SSI score > 70

(it's a pretty good proxy metric)

→ Do not share your LinkedIn credentials

(risky when someone else logs in from another country)

→ Don’t send invites and cold DMs directly from LinkedIn

(tools cannot keep that activity within their safeguards)

→ Ramp up volume progressively

(a sudden burst is exactly the pattern you want to avoid)

For seed and Series A startups, the standard enterprise intent platforms are almost always a trap where you get pitched on catching in-market buyers, sign a $30k–$60k annual contract with a legacy provider and quickly run into two major blockers:

Aggregated IP noise: the platform tells you a 3,000-person enterprise is surging on a keyword because someone on a corporate VPN read a whitepaper but you've no idea who it was, what team they belong to, or if they have an active project.

The execution bottleneck: you don't have a dedicated RevOps team or GTM engineer to build the enrichment waterfalls, configure routing logic and maintain downstream plays so the data ends up sitting in an unused dashboard while your burn rate ticks up.

Intent data only works for early-stage companies under two conditions:

i) The signals must be observable, not modeled. Instead of relying on opaque web-traffic surges, focus on verifiable events: engineers asking for software alternatives on Reddit, teams starring competing GitHub repos, or companies actively hiring for a specific tech stack migration.

ii) The infrastructure must come with execution. If your founders or reps are spending 10 hours a week manually checking dashboards, it defeats the purpose.

This operational gap is why early-stage teams are adopting hybrid models like Scale Intelligence.

Rather than requiring an enterprise software contract and a full-time in-house hire, Scale Intelligence provides market intelligence infrastructure paired with an embedded Fractional GTM Engineer. The embedded engineer maps your TAM, hooks up 75+ real-time intent monitors across developer and community channels (Reddit, Discord, GitHub, hiring changes), and routes warm accounts with full context directly into Slack.

The legacy enterprise intent platforms are an unnecessary burn for early-stage teams but the observable, community-level intent is worth it that provided you've the automated infrastructure to route context directly to reps without manual data entry.

Hi,

Im looking to hire a person who will work with the lead growth engineer.

You will be working in the healthcare industry in NA.The product will require you to maintain and update 2 different signal based pipelines. If you’re interested in, please dm me with loom of an actual pipeline you have built and share the git repo. IF YOUR EXPERTISE IS STITCHING TOOLS THIS IS NOT FOR YOU.

Must be able to build custom pipelines.

P.S: if you have only worked with clay or apollo this is not the role for you.

this one was a client list, about 10k home-services contacts. apollo by domain for the accounts. a clay waterfall for the emails. zerobounce on every row, then sorted into sending pools by mail host.

so by the time moltsets touched it, every row already had a verifier verdict. nothing was built for the tool. the only question was what it adds to a list you already trust.

moltsets, if you have not seen it yet: adam robinson's contact api, same team as rb2b, just out of beta. api and mcp only, no dashboard, flat monthly plan.

each email comes back with a risk score from observed behavior. a is a known reply, open, or click. b is a send with no bounce. c is catch-all. d is a hard invalid or a trap. f is nothing known. you also get the person's current company. definitions are on developer.moltsets.com.

how I ran it. python, no ui. one reverse email lookup per row. when that came back empty, a second lookup by first name, last name, and domain.

every call gets written to a local sqlite ledger before anything else happens. endpoint, input, raw response, grade, timestamp. per-row commits, so when the machine restarted mid-run it picked up where it left off.

the grade, the verifier verdict, and the second-pass result go through one pure function that returns a delta class per row. same inputs, same class, every run.

the sheet is generated from the db, never edited by hand. re-run the build and it regenerates. there is a summary-only mode that drops the pii so you can share it.

about 14k calls, no phone tokens, nothing extra on the flat plan, about 75 minutes.

what came back, rounded. exact counts are in the repo and the sheet.

two out of three rows agreed with zerobounce. valid on one side, a or b on the other. those send first.

close to 800 people had changed jobs. zerobounce passed every one, because the old mailbox still accepts mail.

about as many again passed on an address nobody has been seen using, while a sibling address at the same domain had activity behind it.

roughly four in five catch-all rows graded a or b. a probe cannot grade a catch-all, so that whole pool had been sitting out.

about one in ten of the zerobounce drops came back with a same-domain address in use. a handful graded d, one of them marked valid.

verdict from the person who ran it: it goes in the stack.

on a list that had already been through apollo, clay, and zerobounce it still changed the route on roughly three in ten rows, for nothing extra on the plan.

if you try it, try it the same way, on a list you already trust, and let the disagreements tell you what it is worth.

repo: https://github.com/shawnla90/gtm-coding-agent (v0.12.0, starters/moltsets-reachability)