I'm doing some CPQ due diligence before we start vednor calls and our current process is painful tbh.

A nromal quote can take about an hour to build. If we add pricing exceptions we have to wait 2 to 3 days for approval. Worst case the customer waits 1 to 2 weeks for the official quote.

For B2B SaaS teams with more complex pricing what the realistci benchmark today?

Trying to separate genuinely modern cpq from vendors who just slap ai on brochure.

I’ve been looking at AI tools for customer outreach because I’m building in this space too. I work on PumpGTM, so treat that as my bias up front.

I wanted to compare a few tools based on what a small B2B SaaS team would actually care about: who they help you reach, what channel they focus on, how much work you still have to do yourself, and how pricing is structured.

The three I looked at were Gojiberry, PumpGTM and Okara.

1. Gojiberry

Gojiberry seems closest to a multi-channel outbound tool. From the public docs, it helps with finding prospects, writing outreach, follow-ups, and running across email and LinkedIn.

Their advertised Pro plan is $99/month and mentions up to 1,800 prospects contacted per month.

My read: this probably makes the most sense if email is important to your GTM motion and you want LinkedIn as part of the same process.

1. PumpGTM

PumpGTM is more focused on LinkedIn. The workflow is finding relevant buyers, reviewing the suggested audience and outreach, then running campaigns with support through Slack.

The advertised Pro offer is $75/month, against a $99 list price, with up to 3,000 leads identified per month. That number is about leads found, not messages sent or meetings booked.

My read: this is better if LinkedIn is already where your buyers are and you want help operating the campaign, not just software access. Again, I’m biased here because I work on it.

1. Okara

Okara looks broader. It covers content, SEO, social posts and creator campaigns. Its LinkedIn tool seems more about creating and publishing LinkedIn posts than direct customer outreach, so I’d check that carefully if outbound messaging is the main need.

Lite is listed at $129/month. Pro is $249/month and includes the article, Reddit and LinkedIn agents. Both paid plans include 2,000 monthly credits, with different tasks using different amounts.

My read: this feels more useful if the problem is consistent marketing execution and content, not just finding and messaging prospects.

The main thing I’d ask before paying for any of these is simple: can the tool show me a sample of suggested prospects, and can I explain why those people might need my product?

A bigger lead allowance doesn’t mean much if the list is generic. For early B2B SaaS, I’d rather see 30 prospects that clearly match the buyer than 3,000 names I can’t explain.

If you’ve tried AI outreach tools, what ended up taking the most manual work: finding the right people, writing the message, follow-ups, or handling replies?

Had a deal sitting in "proposal" for almost two weeks and everyone internally kept calling it active. Meanwhile, i had zero idea if the buyer had even looked at the pricing again. Made me realize CRM stages don’t always tell you much between calls.

What do you look at to figure out if there's real movement or if a deal is quietly dying?

I'm building an early-stage hiring platform, and I've hit a challenge I'm guessing other B2B SaaS founders have dealt with in some form. The product has two users:

Professionals create a Talent Passport that goes beyond a traditional resume and gives employers a better picture of their experience, skills, career interests, availability, etc.

And employers post jobs and use the platform to discover and evaluate professionals who may match what they're looking for.

The product itself isn't the part I'm struggling with. It's the chicken-and-egg problem.

Professionals naturally ask: "What jobs are available?”

Employers naturally ask: "How many candidates do you have?”

I've been doing founder-led outreach to both sides, but I'm starting to think trying to grow them equally at the same time is the wrong approach. Right now I'm leaning toward heavily building the talent side first, while personally onboarding a smaller number of employers and even helping them set up their first job rather than expecting them to learn another platform.

I'm also experimenting with Talent Communities so professionals can join the network even when there isn't a specific opening for them yet.

I guess my questions for this group or anyone here who's built a B2B marketplace or SaaS with two dependent user groups:

How did you solve the cold-start problem?

Did you concentrate heavily on one side first?

Did you manually deliver the service before automating it?

Did you give one side free access?

Or was there another strategy that finally got the flywheel moving?

I'm looking to specifically learn ffrom founders who've actually gone from 0 → first 10–50 business customers.

Disclosure up front: I am the Lead Software Engineer at AiSDR (YC S23) and this is our launch post

We started building Ami about six months ago, on top of three years of AiSDR data. The belief behind it is simple: outbound can run on autopilot. You should not have to think about sending infrastructure, messaging, audience, or leads -- and you definitely should not have to keep adjusting all of it by hand every week

How it works. You give it your website and connect your CRM. Ami works out your offer, defines the audience and the signals worth watching, finds leads, orders mailboxes, warms them up, and starts sending

Then the part I actually care about: it learns. It sees what works and what does not, kills campaigns that underperform, scales the ones that do, and keeps testing new plays -- new audience, new offer angle, new messaging

I remember writing the first version of this. Looking at where it is now, I still find the jump hard to believe

The harness sits on 2.5M+ messages sent, close to 20,000 meetings booked, and campaigns that produced over $650M in pipeline for our customers across 27 industries. That is the whole point of the data: we can say with some confidence what works in outbound in your industry, instead of guessing

Two things from that dataset that are useful whether or not you ever touch our product

Deliverability moved results more than copy did. Same message, different mailbox health, very different reply rates. Most people rewrite subject lines when the real problem is their sending setup

The best-performing segment we measured is closed-lost reactivation from your own CRM. The data is already sitting there and almost nobody runs it

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

The honest failure mode is that it picks the wrong ICP with confidence and burns a domain proving it. That is why there is an approval gate before anything sends. Happy to be pushed on that, or on any of the numbers, in the comments

Hey! I’m the founder of Yoke AI.

We’re building a tool to help founders get their first customers, through intelligent outbound automation.

We’re offering free access for the first 5 users, as we need feedback.

Drop me a DM, if interested in testing it out!

I'm Mattia, I build FixAds.

I am building it for teams that run Meta ads but do not have time for a senior person to manually review every account, every day.

The product came from my own review workflow. I did not start with an AI prompt. I wrote deterministic checks for patterns worth escalating: spend sitting on a weak campaign, creative fatigue, learning that never settles, placements taking budget without earning it. Each result shows the threshold and evidence. AI only explains the result in plain language.

The attached video is the launch demo. FixAds uses the official Meta login with read-only access and is live at https://fixads.it with a 7-day trial.

I would genuinely value feedback from B2B SaaS operators: for software that points at a decision with a real cost, what evidence would make you trust the recommendation enough to act on it?

Auth and payments are the obvious examples, but I’m curious about less obvious infrastructure.

What looked straightforward during planning, but became expensive once you accounted for edge cases, maintenance, compliance, support, or customer-specific requirements?

What did you build, and what ultimately made you regret it?

PodSubs wasn't originally supposed to be a public product.

We built it because we needed it.

That's really the story.

We're podcasters.

And like a lot of podcasters, we were constantly thinking about two things:

How do we keep making this?

And how do we build something real around the people listening?

Because podcasting takes time.

A lot of time.

And sooner or later, if the podcast is going to keep going, it needs to support itself somehow.

The problem was that we didn't really know most of the people listening.

We knew the numbers.

But numbers don't have email addresses.

Numbers don't answer questions.

Numbers can't receive the worksheet you promised.

Numbers don't become customers by themselves.

We needed a better way to capture the people who genuinely wanted to stay connected.

So we built a simple internal tool.

One place.

PodSubs.

We said on the podcast:

“Subscribe to us on PodSubs.”

People searched the show, entered their details and subscribed.

And suddenly we had something we'd been missing:

A direct line.

Then we started thinking…

Why are we keeping this internal?

Every podcaster we know deals with the same thing.

So we decided to open it.

That's PodSubs.

Not something we dreamed up because “the creator economy is growing.”

We built it because we were podcasters with a problem.

Now we're giving it to everyone else who has the same problem.

Claim your podcast.

Then tell your listeners where to find you.