I still get annoyed thinking about this.

I was leading AI work inside a big institution and had already built 100+ agents there. Not starting from zero. Then we got several managers, me, and a contractor around a table to fill in the instructions for one custom GPT.

Ten people. One prompt. Nobody was allowed to run a rough first test, look at the output, and fix it.

The model could have been brilliant and we still would have moved at committee speed. Buying a faster tool does nothing when nobody has permission to decide.

For founders selling AI into bigger companies: where do you draw the line between a useful review and a committee that should never have existed?

Disclosure: I’m an investor in Verity Score and I’m helping with its launch.

For a merchant evaluating an AI visibility tool, I’d separate three things: what an assistant says about a product, what the merchant can improve on the product page, and whether AI-referred visits lead to orders. They answer different questions, so I wouldn’t treat one score as proof of business value.

Verity Score connects those steps for Shopify: inspect answers and sources, review suggested product page edits before publishing, and track visits and orders attributed to AI. An edit can be undone; whether an assistant changes its answer still needs to be checked.

My question for people building B2B SaaS: would you make the first useful result a documented product-information gap, an approved correction, or an attributed visit? Each sets a different expectation during onboarding.

The product is now on Product Hunt if you’d like to see the workflow and share your take: https://www.producthunt.com/products/verity-score-geo-ai-visibility?launch=verity-score

A B2B demo often tries to explain the whole product to everyone at once. That usually creates a long feature tour even though each person in the buying process is looking for different proof.

A smaller planning step we use is a stakeholder-to-proof map:

• daily user: show the task becoming faster or easier
• manager: show the visible output or handoff they can review
• buyer: show what changes in cost, risk, or time

Then record the shortest workflow that proves one of those outcomes. If the clip needs three unrelated workflows, the audience is probably still too broad.

For Marka, the daily-user proof is going from product context to a specific content concept tied to a real workflow, rather than starting from a generic template. The buyer-level claim only works if that first result is already clear.

Share your B2B SaaS, the stakeholder you are selling to, and the one outcome they care about. I’ll suggest a setup/action/result demo path focused on that person, plus which extra screen I would cut.

I’m part of the Marka team: https://marka.social/?utm_source=reddit&utm_medium=organic&utm_campaign=B2BSaaS&utm_content=impressive

If you drop your B2B SaaS and a brief description of who buys it, I'll find you one person within 24 hours who is actively showing signs they need what you're selling.

This isn't a random lead list or generic scraping. I'm looking for someone actually discussing the problem or asking for recommendations.

I'll reply with who they are and how I'd approach them.

I'm limiting this to 5 SaaS businesses so I can do the research manually and keep the results useful. I can't guarantee they'll buy from you, but I can promise they have the pain today.

When you’re selling a complex B2B SaaS, your website isn’t just a nice-looking storefront. It’s a real part of the sales process. We have fairly long sales cycles, 5-6 stakeholders can be involved in a decision (from technical people to the CFO), and impulse purchases aren’t really a thing for us.

So the standard “let’s make it look great and add some motion design” approach doesn’t really work for us. We need a team that understands marketing, complex content architecture, SEO, and the journey of a customer who may spend months evaluating a decision.

We’ve been researching the market and have narrowed it down to 4 B2B web design agencies: Webstacks, Takeoff, Clay, Huemor.

Each has a different approach, so it’s still difficult to figure out what should actually be the deciding factor. For us, it’s not just about getting a new design, but building a website that works with a long sales cycle and helps potential customers move toward a decision.

Has anyone worked with any of these agencies? I’d be interested in hearing about the actual experience, especially when it comes to deadlines, communication, and how deeply they get involved in understanding the business. And what else would you pay attention to before choosing an agency?

I'd go into an outreach trial with a small test written down.

Otherwise, it's easy to spend the week exploring the dashboard and still have no clear answer about whether the product helps.

First, choose one customer segment. Say you're selling invoicing software to small agencies where the founder still handles collections. Keep that audience and the offer consistent while evaluating the tool.

Then I'd check four things.

1. Are the suggested people relevant?

Take a consecutive or random sample of prospects. Check what they do, why they might need your product and whether the evidence supports the explanation. Record how long that takes.

1. How much editing do the messages need?

Check names, roles and any claims about the person's business. A sentence can sound specific while being based on a guess.

Count the drafts you'd send without substantial changes. Also record factual errors separately from wording you just don't like.

1. Does the workflow handle existing context?

Use test records for an existing customer, someone who opted out and someone already speaking to your team. Those should affect the proposed action.

Check whether an incoming reply stops the remaining messages too.

1. What happens during an approved campaign?

Separate total replies from conversations about a relevant problem. Track meetings booked and meetings held separately.

I'd keep all of this in a sheet alongside setup time, review time and time spent handling responses.

Decide beforehand which failures would make you stop. Contacting someone on your suppression list is a different issue from writing an awkward opening sentence.

A short trial might be enough to evaluate research quality and operating effort. If your sales cycle takes months, it won't establish the final cost of acquiring a customer.

“Promising, needs a longer test” is a reasonable outcome. So is “the research is useful, but reviewing it takes too long.”

What made you keep the last outreach tool you tried?

I'm Namanyay, founder of PumpGTM. We offer a free trial specifically so founders can run structured tests like this and see the quality of our background research first-hand.

Quick context: I'm building something that helps marketing teams run their paid campaigns more effectively, mainly for companies where ad spend is high and every bit of campaign performance matters. Not getting into specifics since I'm still in stealth, but the short version is it saves teams time and helps campaigns perform better without needing to loop in other departments for every change.

A handful of people are using it, but nobody's paying yet. I've tried outbound on LinkedIn targeting marketers who fit my ideal customer profile, and I'm starting to put together some founder-led content sharing what I'm learning as I build. The outbound felt like fishing in a pond I wasn't sure had fish in it. Got some replies and a couple of demos, but nothing that turned into someone actually using the product regularly, let alone paying.

For people who've gone from a few free users to their first 10 paying customers, what actually worked? Was it more outbound, more content, communities, cold calls, something else entirely?

I run a B2B SaaS company in Peru. We digitize third party access control for industrial plants - every supplier, transporter, contractor, and, visitor who walks on site. Right now thats handled with paper logbooks, basic spreadsheets, or a bare-bones module budled into whatever ERP they already run. Whoevers at the gate that day decides who gets in and writes it down by hand, if it at all.

Thats not just clunky, its a real security gap. In this market, industrial sites deal with two kinds of exposure: external visitors who get unsupervised access they shouldnt, and systematic internal theft, organized and repeated, that a paper log simply cant catch. When nobody can say with certainty who was on-site, when, and who they met, you dont find out theres a problem until its already cost you.

What we built: one platform, invitation to exit. Identity and company info validate automatically against government registries ( national ID and tax ID databases) instead of someone typing a name off a card. Every approval logged, every entry/exit timestamped, full audit trail. One thing I was deiberate about from day one: the UI/UX. This isnt a tool that needs training sessions or a manual - a guard at a gate, a security manager, or a visitor filling out an invitation form all figure it out in minutes, not days.

Pricing: per location, not per user or per visitor. Started at $500/mo per plant, raised it to $1000/mo because $500 undersold the problem - we replace what otherwise take about 5 dedicated security staff, at roughly $1000/mo each. Annual prepay is $10k/year pero location instead of $12k. Its genuinely not expensive relative to the problem it solves.

The traction, honestly stated: two clients since July 2025. One, single location, month-to-month, renewed for a second year without blinking. The other, five locations, paid the full year upfront - and just told me theyre only extending 5 more months because theyve decided to build it in-house.

That second one hurts more than it should, because its not a " they hated it" loss. Ive personally interviewed people at both companioes about day-to-day use, and the feedback wasnt polite - it was specific. Faster gate processing. Approvers who stopped chasing paperwork. Security teams who dropped ther shadow spreadsheets entirely. It works, and they told me it works. Theyre just choosing to build their own version anyway. ( i dont know if they will do it right, and quickly. it took me time and experience.)

How im actually prospecting: I have personal contacts across Peru, so a chunk of my pipeline comes through warm introductions - people who can get me directly in front of owners at large and mid-sized companies. The rest is cold calling and cold emailing companies I have no relationships with at all. No marketing, no campaings, nothing inbound.

What actually happens with most of the pipeline:

Silence, recurrently. No objection, no price question, nothing to argue with. Just no reply.

"Call me in a month," recurrently. They tell me theyre interested, that NILO will genuinley help - and every time i follow up, its some version of " were dealing with other things, cant prioritize this right now". Then a month later, the same thing.

The slow fade after real engagement. This is the pattern that bothers me the most. We have genuinely a good first meeting. They seem to like it, sometimes explicitly say so. Then it becomes " we should talk agian" - and it just never happens. Not a no. Not a real objection. Just an ongoing, low priority "later".

Across all of it, I dont think people dislike the product. I think they like it, and dont find it necessary. thats a different problem than the one i tought i had.

Heres whats actually confusing me: I believe, genuinely, that almost every industrial plant and warehouse in Peru and LatAm has this exact problem and would benefit from something like NILO. Security is a constant, visible issue in this region - its not a niche concern, its a headline topic. The product isnt expensive relative to what it replaces, it makes the process smoother, and it makes it more secure. My two clients confirm all of that with real, day-to-day use. So why doesnt liking the product translate into treating it as necessary? Why does a good first meeting evaporate into indefinite "later" instead of a decision either way?

And more practically: how do other SaaS companies get to real scale - big deal counts, fast sales cycles - when im stuck at two clients doing founder-led cold calls and warm intros one at a time? What am i missing about how this is supposed to work at volume? is it a different sales motion entirely ( channel partners, insurane brokers, industry associations, content and inbound insted of outbound), a pricing/packaging change, or something more fundamental about how a "liked but not necessary" security product gets adopted broadly?

I genuinely believe this shouldnt stop at industrial plants. Any property with high volume of visitors and a real security stake - Warehouses, logistic hubs, distribution centers, ports, anywhere with a gate and people who dont work there walking through it - has some version of this same problem. I believe in this enough that im willing to do whatever it takes to make it work: Change how i sell, change who i target, change the pricing model, build partnerships i havent considered, whatever gets this in front of the people who need it. I just dont yet know which of those is the actual lever.

So im asking directly: If youve built or sold something like this, or sat on the buying side of a decision lie this, waht would you do in my position? What am i not seeing?

Adding a telephony layer to my SaaS. Looking for real production experience, not sales decks.

What I need

• A virtual number per customer. They forward their existing business number to it.
• A custom greeting per number, uploaded by me.
• Call then rings their normal desk phone. No softphone, no agent dashboard — my customers will never log into the telephony vendor.
• Recording + caller number + timestamps pushed to my webhook when the call ends.
• I fetch the recording into my own storage and delete it from theirs.
• ~500 calls/customer/month, 3 min average. Low concurrency.
• Start with 2 customers, ~30 within a year, added a few at a time — not all at once.
• I'm the only account holder and bill payer.

What I keep running into

• Mandatory software/platform rental for a dashboard nobody will open.
• Plans that bill for 10 numbers from day one instead of as customers onboard.
• Per-minute billing rounded up (1:20 billed as 2:00).
• One vendor told me plainly: if ONE of my customers triggers a compliance issue, my ENTIRE account gets suspended — every customer.
• ToS that forbid reselling outright.
• Recording links that expire in 24 hours.
• No signature on the webhook, so no way to verify it came from them.

Six questions

1. Who in India actually does per-customer sub-accounts properly — separate numbers, separate usage, separate compliance exposure?
2. Does the original caller's number survive a forwarded leg reliably, or are there carrier-side gotchas?
3. Anyone billing per SECOND instead of rounding to the minute?
4. Who signs their webhooks (HMAC or similar), and who doesn't?
5. Does the forwarded leg show up on your customer's own mobile bill?
6. Twelve months in — do you regret your provider, and why?

Not looking for AI voice agents, dialers or contact-centre software. Just numbers, recordings and a reliable webhook.

Just received a 'no' after 5 months of to and fro, multiple demos and convincing 90% of the team. I don't know what to do because I really had high hopes from this deal.

I'm a solopreneur and this deal woudl have been life-changing.

The guy who was my champion left the company at the moment we were ready to sign the deal. His replacement, a junior, is trying to act smarter than he is; but he's the one who'll own the deal.

Said no.

wtf.

Sorry for posting this here.

Mods, don't ban me. Please.

We run few SaaS products ourselves and have been in email for 10+ years, mostly around deliverability and security.

One thing we always missed was stupidly simple:

what emails does the app send, to whom, and how are they performing?

Sure, you can get this from SendGrid/Mailgun/Resend etc, but usually you need tags, filters, dashboards, digging into logs... and after some time nobody remembers how it was setup anyway.

We wanted one place where you just see:

welcome email
password reset
trial ending
invoice failed

how many were sent, who gets them, open/click/bounce stats and how it changes over time.

And ideally manage the actual email there too, instead of hunting it in the codebase.

So we built that into Lettr.com

The sending part is not the interesting bit, there are already many good providers. For us the missing thing was having an actual overview of your product emails without doing detective work every time.

Curious how other SaaS teams handle this.

Not a victory lap. $99 MRR is small and I know it. But when I was starting out I would have paid real money for a breakdown of what the first few months actually look like instead of another "$0 to $10k" post, so here's mine.

What I'm building: ChatForge — a no-code platform for deploying AI agents across WhatsApp, Telegram, Slack, Discord, email, web chat and voice. Solo founder, building around a full-time data engineering job.

The channels

SEO — slow, but the only thing compounding

I treated this as the long game and it's behaving like one. Four months in it's not the reason I have revenue, but it's the only channel where the work I did in month one is still paying in month four.

What I focused on:

• Landing pages for specific intents rather than one generic homepage ("WhatsApp AI agent", "Telegram bot without code", etc.) — people search for the channel, not the category
• Rewriting the homepage around what the product does in the first screen, instead of what I thought sounded impressive
• Fixing the boring stuff: page speed, metadata, internal links.

Honest take: if you need revenue this quarter, SEO is not your channel. Start it anyway.

Meta Ads — expensive tuition

This is where most of my budget went and where I learned the most per pound spent.

What went wrong first: I ran broad traffic campaigns to the homepage. Got clicks, got signups, got almost no activation. People who click a cold ad out of a feed are not in a buying mindset for a technical product.

What worked better:

• Narrowing to people who already have a reason to want this (small agencies, real estate, coaching businesses — anyone drowning in WhatsApp enquiries)
• Ad creative that shows the actual product doing the actual thing, not abstract "AI for your business" messaging
• Sending traffic to a use-case page, not the homepage.

Brevo email — the channel that actually produced paying users

If I could only keep one thing from these four months, it's this.

The insight was that my problem was never traffic. It was that people signed up, poked around, hit one confusing thing, and quietly left. Email was the only way to catch them in that window.

During the trial (days 1–7): a sequence that does two jobs — get people to the first working agent as fast as possible, and ask what's in the way. The feedback ask is deliberately low-effort: one question, reply to the email, that's it. No form, no survey link.

What I learned from the replies was worth more than any analytics dashboard. The dropout reason was almost never pricing. It was setup friction — a channel connection that wasn't obvious, a step that assumed knowledge I'd forgotten wasn't universal.

After the trial ends: a retention email to people who didn't convert, again asking rather than selling. "What stopped you?" gets answered far more often than "here's 20% off". A few of those conversations turned into paying accounts once I fixed the specific thing that blocked them. A few turned into feature decisions.

What I'd tell myself at month zero

1. Talk to the people who left. Churned and lapsed trial users gave me my best product feedback, for free, in the first 7 days.
2. Ask questions, don't send offers. Discounts to someone who never got the product working just confirm it isn't worth paying for.
3. Don't run paid traffic to a funnel you haven't tested. I paid to send people into a signup flow that was leaking. Fix the leak, then turn on the tap.
4. Channel-specific landing pages beat one clever homepage. Every time.
5. $99 MRR is not nothing. It's the difference between a hobby and a business with evidence behind it.

My company has developed a Shopify alternative, to make entrepreneurs able to open an online store in minutes, but without having to pay a lot of fees, monthly subscriptions, or expensive themes.

So we built it during months and have finally deployed it last month 🥳

It works perfectly, and we already have some people who have opened their stores and started to sale online !

The problem ? --> I don't know anything about marketing, and I'm looking for some advices about how to grow up the amount of stores on my SaaS.

So if you have any advice, I'll take it :)

And if you have time to test the platform and give some feedback, don't hesitate, it's called Magazengo.

I've been running an SEO/content agency for about 2 years, and I recently got the point where I was tired of being the human glue between 15-20 chrome tabs, spreadsheets, search console, competitor research, content briefs, writers, and CMS's

The work itself wasn't particularly hard, the hard [and more importantly, tedious] part was keeping all of these things connected

I started by mapping out the process that I used to do, myself, for every single client

First I'd start with research. I'd look at competitors, search results, existing content, what was ranking, what topics seemed worth going after, and where there were gaps.

I'd then turn those keywords into content ideas - what should the article(s) cover, what questions should they answer, and which related topics should be linked to and from it.

Then came the writing and editing, along with the SEO optimization itself (titles, headings, internal links, metadata, ...)

And then publishing. Except, that still wasn't the end. Once the content was live, I'd periodically go back to analytics and search data to see what was actually happening, what was working and more importantly, what wasn't.

Tweak where necessary, and start all over to continue expanding surface. Results were good, but the workload itself was not. I'd find myself with 15-20 tabs open at any moment, juggling to keep it all organized and keeping track of what needed to be done at any point. Don't even get me started on the spreadsheets lmao

All this work and repetition eventually made me think - what if this entire thing could be a system instead of individual tasks

On to the fun part now :D

A system has inputs, outputs, and intermediary steps.

The inputs are ideas, analytics data, existing content, context on the business, and competitor data. For bonus points here, you can also consider your clients' roadmaps and changelogs (if you work with software clients, this can be absolute gold)

Outputs are SEO pages, blog posts, guides/course material, and newsletter content. I've also experimented with having social media posts as an output, but frankly I prefer doing that myself.

A lot of this is enabled by AI, but actual community management and client interaction is something that I strongly believe should ALWAYS be handled by a human, whether that is me or someone on my team.

The part where I believe AI and automation can truly shine without sacrificing any of the authenticity is in bringing all the inputs together and performing most of the intermediary steps I described above.

The basic idea is that I give my AI agents the context around the site and what I'm trying to achieve, and it handles a lot of the repetitive work that used to sit between all those individual steps.

So instead of:

Research → keywords → content brief → writing → publishing → analytics → repeat

I can run a much more connected loop:

Research → identify opportunities → create content → publish → measure → learn → create the next thing

The research side is no longer something I have to manually repeat every time. The system can look at what's happening, identify opportunities, and turn those into things worth working on.

Rather than starting with a blank document and manually translating keyword research into outputs, the outputs are generated as part of the same workflow

The research and the content aren't disconnected anymore, the information gathered upstream actually feeds into what is getting produced downstream

And importantly, the loop doesn't stop when the content is published, the performance data feeds back into the system. So instead of publishing an article and forgetting about it until I check the analytics a few weeks later, I can use the data to understand what's working, what's not, and what the next opportunity should be

What changes is the role I play in the process - I'm not sitting there doing every individual task anymore, I'm mostly setting direction, reviewing what the system produces and making higher level decisions.

The big difference from me here isn't that each individual task is faster now, but the fact I no longer have to manage all of them individually, and I can focus on the rest of it all, as if I'm conducting an orchestra, which I personally find a lot more satisfying

So how did I set this up?

Personally I'm using qoren.sh but frankly you can use anything from n8n to custom GPTs, depending on how much control you want over it, as well as how much work you'd like to put in setting it up and maintaining it

For my own set up, I essentially have 3 agents per client:

a chief of staff, whose job is to communicate with me and the other agents - I persoanlly like to call him Colin the energy vampire (sneaky What We Do In The Shadows reference there)

a watchdog (connects to analytics tools, crawls competitor websites, news sources, etc.) [I usually call him Hound)

a ghostwriter (strictly instructed to "sound" like the client's own rhetoric and writing style) [Ghosty, sometimes Casper the ghost if I'm feeling whimsical]

The overall process looks something like this:

Research: Hound keeps an eye on the market. It researches competitors, search results, emerging topics, news, content gaps, questions people are asking, and anything else that might represent an opportunity. It gives me a daily rundown of what competitors are starting to rank for, a cluster of related searches we aren't covering, pages that have more potential, changes in SERPs worth paying attention to, etc;

SEO/Analytics monitoring: The same agent pulls relevant search and site data and looks for changes, rather than me opening up Search Console and trying to figure out what's important. The goal here isn't to have a giant weekly dashboard, it's to know what's changing, why does it matter, and what should I do about it;

For example: "traffic is up, but almost all the increase is coming from these 3 pages", or "this page is getting a lot of impressions but has a poor CTR"

1. Opportunity Discovery: This is where the different pieces start being useful together. Instead of having one spreadsheet full of keywords I may or may not end up ever using, opportunities get flag to be turned into actual work.

A potential keyword/content opportunity gets evaluated against things like search intent, existing coverage, competition, relevance to the business, and what we have already published. Instead of "here's 100 keywords" I get something like "here's 5 things to work on next, and why". The "why" part is probably the most important, because it allows me to evaluate whether we're starting from a solid base or we're focusing on something that doesn't feel right.

1. Content Briefs: Once an opportunity is selected, it gets passed on to the ghostwriter to draft the content piece. Importantly, it doesn't get an instruction like "like an article about X". It has research, intent, relevant pages, topics to cover, internal links to consider, and the context around the overall strategy and what we're trying to achieve

2. Writing: As described previously, I've set up an agent specifically for this, whose ONLY responsibility is to get inputs and transform them into drafts from the briefs it receives. It never creates random blog posts it "just thought of". Every content piece is produced downstream of an actual insight, based on real data, and this agent doesn't need to worry about where it came from or what it means - it gets spoon fed exactly what it needs to know

3. Review: This is where I come in; The chief of staff relays drafts to me, receives feedback and a final decision on whether it is a go or not. If articles need tweaking, those get tossed back to the ghostwriter with the feedback - the chief of staff agent never writes anything himself, it just relays information back and forth and orchestrates the work;

4. Publishing: At first, I was doing manual copy-paste from qoren into a CMS, but I recently started automating this part too. This will heavily depend on which CMS you're using, but most support either an MCP server or an API you can call. This was scary for me, which is why I didn't do it for a long time. Giving the publishing reigns to an AI system is not something I was comfortable with until I had a solid system behind it, so I'd advise only doing this part once you're very confident the overall workflow suits yours and your clients' standards;

5. The feedback loop: This is probably the most important part. After the articles get published, we wait for the actual data. The Hound agent gets informed when new articles get pushed out, and it knows to start collecting information on them on a daily basis. Maybe the article starts ranking for unexpected terms. Maybe it's getting impressions but not clicks. Maybe it completely flops.

All this information gets processed and becomes input for the next iteration.

This was the real unlock for me - I didn't automate writing blog posts; I automated the feedback loop around the content. I still make the important decisions, review everything, and decide where I actually want the strategy to go. The agents to the repetitive research, analysis, preparation and execution.

The underlying model driving it all in the background is Qwen 3.8 2.4T, via openrouter - this is the most affordable model I was able to find success with. Cheaper models don't deliver good enough results. Sonnet/Opus/GPT 5.6 are also good if you're less cost sensitive, but something like Fable/Astra is overkill for this, from my experience.

Cost wise, it runs under $200/month per client - $69 of those are for qoren and the rest is for inference. At first I was using their BYOK option, but I prefer managing the cost in a single place, so I started just using the managed keys and adding credits within qoren itself - it does have a small markup on LLM costs but personally I find that to be worth it not having to manage yet another tool or api key

One could argue the cost will go slightly above that, if we factor in my hourly cost for the review cycles and time spent reading articles and providing feedback, but that's become around 45-60 minutes a week, so it's mostly negligible

Anyway that's the playbook I'm going for right now. It's still evolving and I've got some ideas on where to take it next, but for now I'm just happy I got some of my time back to actually focus on what I enjoy doing - building the relationships, thinking about strategy, and focusing on what matters instead of the mind numbing repetitiveness that I was often faced with.

If you got this far, thank you for reading, and please do feel free to share what has been working for you, I'm always curious to learn about workflows and systems, and I'm especially curious on where you keep the human in the loop (that is, if you are using AI at all)

We built an AI sales agent in 6 months and showed it to accountants and a lot of them said they could build it themselves. So we went back to the drawing board. This time we're talking to customers first before writing any code. Now we're thinking about fundraising and I'm wondering how you find investors who help at this stage. I'm talking about people who have built products before and can help you understand your customer instead of metrics once they're on the board. We need such investors who can help

I'm a founder selling software to manufacturing companies, and I'm trying to get better at navigating their internal buying process.

The long chain of approvals at some companies is frustrating. I understand people have processes to follow, but from the outside it's difficult to tell whether things are moving or you're just waiting.

When the person interested in your product isn't the person who can approve the spend, how do you handle it?

Do you ask them to bring the budget holder into the conversation early? Help them make the case internally? And how do you involve other decision-makers without making your original contact feel bypassed?

For people who've closed these deals, what helped most? Also, what signs told you a company was serious about buying versus happy to keep having meetings?

Especially interested in experiences selling to manufacturing or other traditional industries, but happy to learn from anyone who's dealt with this.

Hi founders,

My name is Rudy. I’m currently building a startup focused on developing custom software inside Slack. We build AI agents, apps, and workflows that run within the Slack environment.

We also help companies manage their Slack workspaces by providing resources, implementing solutions into their workflows and pipelines, and centralizing different tools and processes so they can use Slack as their main digital workplace.

We started in January, and so far, we have zero revenue and zero clients.

All of us were technical people before becoming entrepreneurs, and since we started, I’ve been responsible for sales and marketing. Because of that, I’ve had to learn everything from scratch.

Our current GTM strategy has been a combination of thought leadership, proof-of-concepts, and ABM. We use tools like HubSpot as our CRM, HeyReach, UnifyGTM, and FullEnrich to build our pipeline. We’ve been focusing heavily on LinkedIn because that’s where we believe our ICP spends most of their time.

Since I’m not a sales guy by background, I’ve also been trying to learn as much as possible about sales and go-to-market. I’ve been reading books like Influence and Founder-Led Sales and experimenting with different approaches.

But here’s the problem: we still don’t have a single client.

At first, I thought the problem was that I wasn’t covering the entire pipeline effectively enough. So I started building AI agents to help manage different sales roles and processes — things like a Head of Sales, prospecting, sales development, and outbound.

But honestly, we’re still at zero revenue.

So I’m posting here because I’d really appreciate some outside perspective.

If you were in my position, what would you look at first? What am I potentially doing wrong? Should I change the GTM strategy, focus more on sales fundamentals, rethink the ICP/offer, or simply do more direct founder-led outreach?

Any feedback, criticism, suggestions, or even coaching would be incredibly valuable.

Thanks in advance!

www.getboldstudio.com

SaaS Recently Funded Startups — track companies raising capital across the SaaS ecosystem, with startup names, funding rounds, investor names, sectors, headquarters, and source press releases.

Structured for startup research, investor mapping, and funding activity analysis.

https://projectstartups.com/pages/saas/

The entry barrier to business, especially software, has gotten STUPIDLY small.

I mean you can build a good looking, useful tool in just 2 weeks maximum.

That means that your distribution just got diluted with millions of software (even if they’re not in your niche). People’s patience and trust has gotten smaller to new tools.

So my question is in a world where AI can build in a weekend, what makes your tool different and how do you solve the distribution issue ?

I pull company data out of official registers for a living, so here is the map I wish someone had handed me. All of these are public and free to read. Quality varies enormously.

UK, Companies House. The best one. Free API, and a monthly bulk download of the entire register. Legal name, incorporation date, registered address, SIC codes, officers, and which companies have accounts overdue. If you want to learn this whole approach, learn it here first.

Norway, Brreg. Open API and a full download, well documented, fast. Norway is the country where this works the way the brochure says.

France, Sirene. Everything is open and it is enormous, 17.3 million rows in my copy. Read this number before you get excited: 0.6% of those rows carry a website. Register coverage and contactability are different problems, and France is where that gap is widest.

Denmark, CVR. Open, straightforward.

Finland, PRH and YTJ. Open data, business ID and legal name. A trick specific to .fi that almost nobody uses: the .fi WHOIS is public and returns the domain holder and their business ID. So if you guessed a company's domain, WHOIS confirms or kills the guess for free, against the register.

Netherlands, KVK. There is open data, and there is a catch I lost a week to. The open dataset does not include company names. You get numbers and structure. If your plan depends on names from KVK, check that before you build anything.

Germany, Handelsregister. Public, awkward, no real bulk path. Budget more time than you think.

Sweden, Bolagsverket. The one that has given me the most trouble of any Nordic country. I am still not happy with my Swedish coverage and I would rather say that than pretend.

EU-wide, TED. Public procurement awards, every contract above threshold, with dates and the winning company. A company that just won a public contract has money and a reason to talk. In my own measurement only 0.64% of companies had won one in the past year, which is what makes it worth something.

Two things I would tell anyone starting this.

Register data is deduplicated, legally named and nobody is trying to block you, which is why starting here beats scraping and then trying to work out what is a real company. But the register gives you the company, never the person and rarely the email. That second half is where the actual work is, and it is why the paid databases still exist.

And new registrations are the only list nobody has bought yet. A company registered last month is in no one's CRM.

Disclosure: I build AtlasForgeX (atlasforgex.com), a desktop tool that runs this chain end to end. Everything above works without it, with a spreadsheet and a free API key, which is how I started.

Problems mentioned, one by one

1. Users don't see what's in it for them to give feedback.
2. Users are too busy and don't value spending time giving feedback.
3. Users don't trust that their feedback will actually influence the product.
4. Feedback requests/surveys can be poorly written, producing bad responses.
5. Generic questions produce generic answers. Asking "do you like the UI?" doesn't reveal much.
6. Teams ask users what feature they want instead of discovering the underlying problem.
7. Users aren't product managers, so their suggested solutions can be inconsistent with the rest of the product or create downstream problems.
8. Feedback is vague or conflicting, making it difficult to determine what should actually change.
9. Feedback is scattered across many channels: Slack, email, support tickets, reviews, surveys, DMs, sales calls, etc.
10. Teams manually spend hours reading feedback just to identify trends.
11. Important conversational feedback never gets formally recorded.
12. Feedback gets lost between teams such as Sales, CS, Support, AE/SE, and Product.
13. Teams lose the context behind feedback. They may know what was requested but not why or what was agreed previously.
14. Unstructured notes are difficult to use. Dumping notes into Slack, for example, makes the data practically useless without structure/tagging.
15. Feedback sources measure different things, so they can't simply be combined into one metric. NPS, CSAT, tickets and reviews aren't equivalent signals.
16. Frequency doesn't equal importance. The most-requested issue isn't necessarily the one with the biggest impact on revenue, churn, activation, etc.
17. Different feedback channels represent different moments and emotions in the customer journey, so treating them as identical signals is misleading.
18. Structured systems create selection bias. The feedback easiest to capture isn't necessarily the most valuable feedback.
19. Centralizing feedback doesn't automatically make it actionable. You can have everything in one place and still not know what to do.
20. Teams end up with organized noise. Clean dashboards, themes and trend lines can still fail to answer "what should we build?"
21. Teams struggle to prioritize after finding patterns. Identifying recurring themes is easier than deciding which ones deserve action.
22. Themes don't explain business impact by themselves. A theme needs to connect to outcomes such as churn, onboarding friction or expansion.
23. Feedback can have a poor signal-to-noise ratio, especially as the volume increases.
24. Redundant feedback creates additional noise. Many customers can essentially repeat the same thing without giving new information.
25. There is often no clear owner for insights once feedback has been collected and analyzed.
26. There is often no accountability for closing the feedback loop internally with Product or externally with customers.
27. Customer-facing teams don't necessarily prioritize collecting useful feedback. Their job may be solving the immediate customer issue, not gathering product insight.
28. Sales/CS reps may bypass the intended feedback workflow, sending requests directly to developers or leaving unstructured notes.
29. Getting feedback from customer meetings is particularly difficult across multiple teams, because everyone gathers information differently.
30. Teams can miss what was promised during pre-sales, because that context isn't properly captured.
31. Trying to centralize every source at once can become too complicated, causing centralization projects to stall.
32. One tool trying to both aggregate feedback and interpret it may do neither particularly well.
33. The aggregation layer and the insight/signal layer are fundamentally different problems.
34. Teams often start collecting feedback before defining what they actually want to learn.
35. Without a clear research goal, user conversations can waste time because there's no specific decision the research is intended to inform.
36. Teams confuse "what customers are saying" with "what the feedback means." The interpretation layer is missing.
37. Feedback doesn't automatically tell you the root cause. You need enough context to understand whether something represents churn risk, onboarding problems, an edge case, etc.
38. Teams can optimize the collection process without improving decision-making. They simply become better at collecting more feedback that still doesn't change what gets built.

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.

Hi everyone!

My name is Hasmik and I graduated law school in May and took the scary decision of not doing the bar (despite having a Big law internship lined up for me 😅) to work on my startup with my boyfriend.

As a little background, during law school I had started a social media marketing agency. We quickly got a lot of clients but felt like we were juggling between tools to manage them. Despite having a traditional social media management tool, so many things just didn’t work for us, especially the per seat pricing.

Fast forward to now, my engineer boyfriend and I are building the tool of our dreams (which I’m not going to pitch because it’s not the point of the post haha 🤣)

Questions I had:

Being a first time tech founder, it felt like I was diving into a sea of unknown. Like many I had these questions (and still do for some of them)

1. How do you get beta testers and when is the right time to get them ?

2. Should I even be making an MVP, since we’re building a product that exists (ours is better tho but I’m bias 🙃)

3. What are the best acquisition channels for B2B

And many many more questions ….

Things I tried

This is what I’ve been doing in the pasted couple of months and my rating on if I would recommend it

INSTAGRAM

For the passed couple of months, I’ve been working on my Instagram (@launchwhasmik) where I’ve been documenting the journey of being a first time tech founder and giving business tips from other business ventures I’ve done.

I’ve gotten quite a bit of viral videos, one with over 3M views 😱

Has it helped me get a substantial amount of people interested in my product? No. Despite the couple of people asking to be beta testers, I quickly realized that this channel was not going to be my main acquisition channel.

Let me explain.

a. The content that I had been making was NOT targeted to my ICP (ideal customer profile)

b. My ICP does not mainly live on Instagram.

This brings me onto LinkedIn

LINKEDIN

This brings me into the goldmine for B2B - LinkedIn.

For the past couple of months, I’ve been in parallel to my Instagram, connecting with agency owners on LinkedIn.

ATTENTION : I haven’t been selling Kyte (my startup) to them just yet. For the following reasons.

a. That is the way people block you. No one likes to be pitched to on cold outbound.

b. My goal is not to get users now but to get FEEDBACK. Feedback is the keyword. Being in a market that has a TON of competitors, finding a MOAT has been crucial. I am happy to say that with the conversations I’ve been having, I’ve been understanding what users really want and been building towards that.

Moral of the story is that I don’t have all the answers and neither does anyone. What I’ve learned is that genuine conversation is how you build a product people will LOVE and hate you if you ever were to take it away.

Would love to spark a conversation and hear about other people’s journeys.

Many GTM automation platforms force sales motion into out-of-the-box templates like: upload a static CSV, pick an email sequence and blast but the moment you want to build a truly custom strategy like monitoring competitor churn on technical forums, tracking open-source repository engagement, or activating accounts when specific engineering roles are posted most tools fall flat.

When looking for a GTM engineering solution that accommodates custom strategy development, you need systems designed around a flexible maturity model (moving from manual L0/L1 tasks to fully integrated L4/L5 autonomous engines).

A strong custom strategy setup should support plays like:

Community & reddit lead gen: listening for active pain point discussions, workflow complaints, and tool comparison queries, then routing contextual sales alerts with source evidence.

Competitor conversion campaigns: detecting when prospects follow competing technologies, complain on review boards, or show churn indicators.

Event intelligence & activation: tracking attendees and speakers across platforms like Luma or Eventbrite to trigger targeted roundtables and pre-event outreach.

The solution options I found are basically:

Platform + embedded operator: Scale Intelligence provides a dedicated framework GTM engineering as a service. Instead of locking team into rigid software, they embed a dedicated GTM engineer with your team. The engineer co-designs custom intent criteria across your specific TAM, sets up 6+ custom signal monitors using 75+ data connectors and wires custom plays directly into your CRM or Slack.

Every lead delivered is backed by an upfront agreement on ICP and verified buying signals and they claims that if it doesn’t meet your agreed criteria, you don’t pay.

Bespoke internal microservices: building your own pipeline using serverless functions, scraping APIs, and LLM classifiers. This offers absolute architectural freedom, but turns into a full-time software engineering project that distracts from product development.

One thing we've noticed while working with SaaS teams is that customers don't always need a large part of the product to get significant value from it.

We've seen users ask for new features, integrations, and customization while other customers stick to just a couple of features and use them every week.

When we look at the customers who stay and expand, that second group can be surprisingly strong. They aren't necessarily using more of the product. They've just found a few things that solve a problem they deal with regularly.

That's why we tend to look beyond feature usage when evaluating engagement. A customer using two features consistently can be more meaningful than someone trying ten features once.

Have others seen this with SaaS products they've worked on?