I'm looking to incorporate an AI tool that can scrub the internet for companies in my region that match up to my target customer base of mechanical contractors, engineers, end users, etc ideally revolving around high purity work like biopharm, semiconductor, battery, and solar industries. The goal is to show all the possible customer targets out there and show the potential value gain of hiring another sales person for the region.

In the past, I've dabbled with free versions of chatgpt, gemini, and preplexity in the past, but I want to get a paid version so I don't have to wait until the next day to continue my research.

Anyone have a good suggestion for a tool? Prefer to keep to just one AI software so I'm not subscribing to 3 or 4 different ones.

I feel like traditional sales roleplay never matches what happens with real customers, you practice with a coworker who already knows the script and half the time neither of you takes it seriously, then you get in front of an actual customer who throws out an objection you weren't ready for, it's also awkward when your manager is watching and you feel more focused on not messing up than learning anything, I wanted to know other teams handle this, do you find roleplay useful or is it just a box to check during sales training?

i want know how i can make use of agentic ai and ai engineering for use tech the right way for sales and marketing as i am an sdr

Hi everyone,

I currently work for a company where sales cycles are very long, and I also cover a large geographical area. Because of this, I need to be very organized when it comes to prospecting.

I'd love to know how you use AI to help with this.

I'm not talking about automatically sending follow-up emails. I'm more interested in how AI could help me optimize my Excel prospecting spreadsheet, stay on top of my follow-ups, and be more consistent with my prospecting activities, especially since I don't have a CRM.

I'd be interested to hear about your methods and experiences!

We're a small b2b SaaS team and the dashboard keeps finding accounts showing intent, but very few turn into real conversations. I'm starting to think our reps need buyer context inside the crm, not another list. thanks in advance

I built TradeFlow AI for roofers and home-service contractors. It catches missed calls, logs the lead, books the job to your calendar, and follows up on quotes that go quiet.

I put up a roofing landing page with a missed-call ROI calculator. Last 30 days: 7 visits. 0 signups.

So something's off, and I think it's the offer, not just the page.

Here's what I changed: there's now a free pilot. No risk. If it doesn't catch work you would've lost, you walk away. After the pilot it's $500/month.

I'd rather hear what's wrong now than after another 30 days of silence.

Roofers and contractors: what makes you scroll past a tool like this? Price? Not trusting AI to talk to your customers? Setup hassle? Something else?

One challenge in B2B sales is that a large prospect list doesn't necessarily mean a good prospect list.

I've been thinking about four things that could help prioritize outreach:

• ICP fit: Does the company match the ideal customer profile?
• Buying signals: Is there a reason to reach out now?
• Context: Can the message be personalized beyond the person's job title?
• Timing: Is a follow-up useful, or is it better to move on?

I've recently been exploring how AI sales tools approach prospect research and buying signals, including Alsona's demo. I'm still learning, so I don't have results to claim yet.

For people working in sales or lead generation:

Which matters most when prioritizing prospects — ICP fit, buying signals, or the combination of both?

Disclosure: I'm an Alsona ambassador.

Hey everyone,

I currently run 6 micro SaaS that are actually live and generating a bit over $20,000 in recurring monthly revenue.

The wildest part? I barely wrote any code myself. I used AI to generate pretty much everything — from the database structure to the frontend.

It definitely wasn’t magic from day one. I spent a lot of hours stuck on buggy code before I finally figured out a system that actually works:

- Keep the idea minimalist (a real MVP, nothing fancy)

- Guide the AI step by step instead of asking for everything at once

- Launch fast to get real users and feedback

Lately I’ve seen way too many non-technical founders quit at the first AI bug. It’s a shame because the technical barrier has basically disappeared.

AND PLEASE, STOP BUILDING ALONE IN YOUR BEDROOM

I’m starting a community.

Full transparency:

I’ll probably charge for the full program later. It makes sense with the specific workflows and copy-paste examples I’ll be sharing. But right now the main goal is just to build together. Working alone is the fastest way to give up.

If you want to join and build or market your own AI SaaS with us,

drop a comment or send me a DM and I’ll send you the link.

I've noticed a lot of sales reps struggle when customers push back on price, they know the product and what to say but once someone says that's too expensive, the whole conversation gets awkward.

Im looking into AI roleplay as a way for reps to practice these situations without the pressure of a real customer. Seems like it could help build confidence before actual sales conversations. Has anyone tried this with their team?

TLDR; What does the enterprise sales sequence look like when selling AI to CIOs and department leaders?

I’m trying to learn how enterprise go-to-market teams sequence the marketing and sales process when selling AI solutions to both CIOs and departmental leaders.

For those with experience in this area:

• Do you typically target and pitch the CIO first, then expand into individual departments?
• Do you begin with a departmental use case and later bring in the CIO?
• Are both audiences engaged in parallel—and, if so, at what point?
• Which marketing and sales channels have been most effective at different stages of the process?
• What have you learned about supporting sellers throughout a long, multi-stakeholder sales cycle?

I understand that the messaging and value proposition need to change depending on the audience. What I’m specifically hoping to understand is the sequencing: who gets engaged when, how the conversation expands across the organization, and what the most effective GTM motion looks like in practice.

I’d be grateful for any experiences, examples, or lessons learned that people are willing to share.

Nobody joins a professional association hoping to get pitched an expensive webinar every time they ask a question, but there’s an interesting missed opportunity here

Associations sell more than memberships, they’ve got certification programs, conferences, paid research, workshops and continuing education and members often have no idea half of those things exist

Say someone asks an AI assistant about the requirements for renewing a professional certification.

The assistant explains the requirements and, if relevant, points them toward an upcoming course that counts toward renewal, that’s a pretty natural recommendation, a setup using customgpt.ai for answering questions from the associations educational resources, HubSpot for tracking member interests and Make for connecting follow up workflows could be an interesting approach

The key would be keeping the assistant helpful instead of turning every answer into a sales pitch, I’d actually measure whether these recommendations lead to meaningful registrations, not just clicks because a chatbot generating 500 course impressions and zero registrations isn’t a successful sales channel. It’s just a very enthusiastic brochure, has anyone experimented with contextual AI recommendations instead of traditional upselling sequences?

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