Saul Fleischman

Love the positioning around bridging technical and non-technical teams. The set-up copmplexity, not so much.

Quick question - how does n8n handle version control and collaboration when multiple engineers are building workflows? Do you have built-in Git integration or is that something on the roadmap? Seems critical for teams wanting to maintain workflows like code.

Love the focus on flexibility here - so many CRM tools feel rigid out of the box. Quick question: how does Attio handle the common pain point of data quality and deduplication when syncing email and calendar data across different team members? That's usually where homegrown CRMs fall apart.

This is ambitious in the best way - consolidating so many fragmented tools into one platform.

I'm curious about the data warehouse provisioning feature. How are you handling the complexity of integrating with different warehouse solutions while keeping the UX simple? And do you find teams are actually willing to consolidate their analytics stack, or is there still too much switching cost?

Great roundup of the scheduling heavyweights! That said, most of these tools nail the posting calendar but miss the listening side — they're built for execution, not insights. If you're serious about benchmarking, you really need to understand what competitors are actually saying across platforms, not just when they post. We built MentionFox to fill that gap with 52+ platform monitoring including emerging spaces like AI chatbots and niche communities.

What's your biggest pain point with these tools — is it the benchmarking accuracy, or more about connecting what you post to what people are actually talking about?

The "be known before you pitch" positioning is spot on — cold outreach from a stranger vs someone who's been visibly engaging in your content are completely different conversations. What's the average time from first Extrovert comment to actual deal conversation? Curious about the warming cycle length.

Really interesting approach to reputation — most tools focus on what you say, but miss where AI chatbots are sending people when they ask about your industry.

Imagine adding an AI Visibility tab: "ChatGPT recommends your competitor 3x more than you in [category]." We built an API for exactly this — foxapis.com/docs — /v1/geo/check returns a visibility score across 5 LLMs in under 2 seconds. Free to test, no signup.

The embeddable widget + docs training combo is solid—that's the winning formula for AI chatbots right now. One thing I'd watch: most Poe alternatives struggle with actually using your website content accurately (hallucinations are real). We built FoxChat solving exactly that—it crawls your actual pages and grounds answers in your real content, which pairs perfectly with a setup like this for support or lead gen. What's your experience been with accuracy when training on docs—do you see drift over time, or does it stay pretty consistent?

This is a smart angle—AI search visibility is the blind spot most teams have right now. The challenge I see is that visibility monitoring works best when you're also tracking mentions and context across where your audience actually hangs out (social, forums, AI chatbots, etc.), not just search. We built MentionFox to connect those dots—monitoring 52+ platforms including where AI models are being trained and recommended from. How are you handling the social + search visibility gap, or is that beyond scope for Gauge?

Brand monitoring tools like this are crucial, especially as search gets more fragmented across AI platforms. The geo angle is smart — most brands miss that their visibility varies wildly by region and AI model. We built MentionFox partly because we realized brand monitoring needs to span way beyond traditional search (52+ platforms including ChatGPT, Perplexity, Reddit, etc.).

How are you thinking about AI-generated search results — are you tracking brand mentions in ChatGPT/Claude responses, or focusing mainly on traditional SERP visibility?

It does nothing to change how LLMs answer questions about a company/product, and this is critical.

Monitoring AI visibility is becoming critical—most brands still don't realize how often ChatGPT and Perplexity shape customer perception before they even visit your site. RepuAI looks solid for that angle. We actually built MentionFox to monitor how you're mentioned across 52+ platforms including AI chatbots, since traditional social listening misses this entirely. Are you finding that AI search results are actually moving the needle for your customers' purchasing decisions, or is it still mostly a vanity metric for most brands?

Competitor analysis tools are tough because you're only as good as your data freshness and breadth — most tools miss emerging competitors until they're already gaining traction. If you're building this, I'd focus hard on real-time monitoring across where your users actually talk (social, forums, review sites). We built MentionFox partly because we kept seeing founders blindsided by competitors they found on Product Hunt or Twitter weeks after launch. What platforms are you planning to cover first, and how are you handling the API/scraping complexity?

Warm intros absolutely crush cold — the data is overwhelming. The trick is knowing enough about the person you're asking for an intro TO. We built MentionFox's people intelligence layer for this: profile anyone from public data across 30 dimensions, including how they communicate, what they care about, and their decision-making patterns. Makes the intro request much more specific. Are you seeing adoption more from sales teams or recruiting teams?

Content monitoring for AI search experiences is a smart angle — Google's AI Overviews and ChatGPT are basically becoming discovery channels now, so tracking brand mentions there matters way more than most realize. If you're monitoring search, you might also want to track who's recommending you across AI chatbots and platforms — we built MentionFox partly because brands were getting recommended by Claude but had zero visibility into it.

How are you handling the AI platform data collection — are you scraping, using APIs, or partnering directly with the platforms?

Competitor analysis tools are tricky because you're only as good as your data freshness and platform coverage — most tools miss mentions across Discord, Slack communities, and AI chatbots where competitors actually get recommended. We built MentionFox to monitor 52+ platforms including those blind spots, and it's become clear that traditional web scraping misses half the conversation. What platforms are you prioritizing first, and how are you planning to handle real-time updates without hitting rate limits?

Love the look and reach - but would also love to see use cases where this would not be very creepy.

will give it a spin, but prompt optimization, show your value prop over competitors what do you think?

Stacking Compyle up against two other no-code app builders I already use, I'm already leaning towards making Compyle the go-to, but to run out of credits on my first prompt is a fail. Love the Github auth connection, repo-creation, and of course, you're killer with improving the prompt! and editing from the preview.

When you check the prompt, suggest how to continue, give an option to add/modify rather than just continue to initial build. We think of things as your thing thinks, so let us add/modify.

Good for the previews and easy push to Github. First project and I'm out of credits after 5 prompts. Their affiliate "invite and earn" does not work. Tried with a friend, neither of us got credits. No live chat on the site to get help with this.