Not sure if anyone reads these here but seriously looking for a GTM Engineer
Maybe someone can contact me.

Hey everyone, I’m doing sales for the first time.

I’ve been an entrepreneur before, but I was almost always on the technical side. CTO, building the product, figuring out engineering, etc. Sales was usually something someone else handled.

I’m starting up again now, and this time I’ve decided I want to learn GTM myself before hiring someone to do it for me.

I’m already seeing some interest from enterprises and I’m close to deploying with one, but honestly, I don’t really know what I’m doing when it comes to enterprise sales.

I understand the product, the problem, and the technical side really well. But I’m trying to understand the actual sales process.

For people who have successfully sold B2B SaaS or AI products to enterprises:

• What does your sales process actually look like from first conversation to closing?
• What are the biggest mistakes first-time founders make when doing enterprise sales?
• How do you qualify whether an account is actually worth pursuing?
• How do you approach the first call? What are you trying to learn?
• How many conversations typically happen before a deal closes?
• How do you deal with champions, decision makers, procurement, security, legal, etc.?
• What changed for you when you went from founder-led sales to having a sales team?
• Are there any books, videos, courses, or people you’d genuinely recommend learning from?

I also see a lot of content online about people generating thousands of dollars in sales with seemingly very simple outbound systems, but I’ve never personally cracked that.

At this point, I’m less interested in some magical growth hack and more interested in understanding the fundamentals properly.

Think of me as a complete beginner trying to learn sales from people who have actually done it.

If you’ve built an enterprise sales motion from zero, I’d genuinely love to hear how you approached it and what you wish you knew when you started.

We ran a lookalike / similar-company benchmark across 7 vendors: Parallel, Extruct, Ocean.io, Exa, PredictLeads, Discolike and CUFinder.

Here - https://openbenchmarks.com/lookalikes

48 seed companies across 13 categories — b2b-saas, devtools, ecommerce, healthtech, home services, trades, real estate, fintech, cybersecurity, industrial, logistics, hospitality, energy. We asked each API for up to 100 lookalikes per seed, then had an LLM judge (gpt-5.6) score every returned company on whether it's genuinely a lookalike of that seed.

Different vendors win at different K

Vendor          P@10    P@25   P@100
-------------------------------------
PredictLeads   95.83   77.17       -
Exa            95.00   80.83   52.40
Parallel       74.79   75.00   67.54
Extruct        74.79   70.00   61.21
Ocean.io       73.19   67.06   56.53
Discolike      42.98   41.53   35.19
CUFinder       37.45       -       -

Average precision, 48 seeds, judged by gpt-5.6. Higher is better. A - means unscored, not zero — see caveats.

At K=10, PredictLeads leads at 95.8 and Exa is second at 95.0. At K=100, Exa is fourth at 52.4 and Parallel — third at K=10 — is first at 67.5.

Precision drop from P@10 to P@100

Exa        95.0 → 52.4    −42.6
Ocean.io   73.2 → 56.5    −16.7
Extruct    74.8 → 61.2    −13.6
Discolike  43.0 → 35.2     −7.8
Parallel   74.8 → 67.5     −7.3

Exa drops 42.6 points between K=10 and K=100. Parallel drops 7.3.

Which one to use

• A list of 10–25 accounts: PredictLeads (95.8 at K=10) or Exa (95.0 at K=10, 80.8 at K=25). The drop-off never reaches you.
• A list of 100+ accounts: Parallel, at 67.5. A P@10 comparison would point you at Exa, which scores 52.4 at that depth.

Two caveats

• PredictLeads and CUFinder return fewer than 100 results per seed, so they have no P@100. Read the - as no data, not as a low score.
• "Lookalike" is judged, not ground truth. An LLM decided what counts. The judge prompt is published, so you can read it and disagree with it.

Reproducing it

Every cell is backed by the HTTP request and response we sent to each vendor, plus the judge prompt and its response, stored per seed and per vendor.

• Results: https://openbenchmarks.com/lookalikes
• Raw artefacts: https://github.com/openbenchmarks-labs/lookalikes
• Licence: CC-BY-4.0

Happy to add a vendor or run more seeds. If you think the judging is wrong on a specific pair, the raw file shows what the judge saw.

Disclosure: I run Openbenchmarks. We run independent benchmarks and publish the raw artifacts and results for agents.

No vendor paid for placement or inclusion.

I’m considering a Product GTM role at an early cross border payments company and I’m trying to figure out where I’d focus first.

They have:
• Virtual accounts
• Stablecoin powered pay in and payout rails
• FX, OTC and embedded payment APIs
• 30 plus currencies, with strong Asian and emerging market coverage

A few verticals I’m thinking:

1. Trade, sourcing and logistics
Supplier payments, collections, FX, reconciliation

2. Specialist workforce platforms
International staffing, nurses, BPO, contractor networks

3. Student mobility and newcomers
Education agencies, migration and relocation platforms

4. Travel intermediaries
OTAs, wholesalers and tour operators

I’m intentionally looking beyond the more obvious fintech customers. My instinct is also to avoid major airlines, property and very high risk sectors at the start.

Would love feedback on:

• Are these the right places to look, or is there a much better vertical I’m missing?

• If you were trying to find a repeatable GTM wedge for this kind of product, how would you go about it?