Using GetViktor, Patricia.app, and AskRocco: Slack AI Agents for Founders

A member of our mastermind asked me a direct question that I think a lot of founders are quietly wondering about: after months of using an AI agent called GetViktor, what actually brings the most value to my business? What do I use every single day, and what's still missing? It's a fair question, because there's a real gap between founders who install an AI tool, poke around for a week, and never get real business value out of it, and founders who rebuild a chunk of their workflow around one. I've landed pretty firmly in the second group, and another member building a competing product called Patricia.app added his own perspective on the same call. Between the two tools, plus a third one a fellow member just launched called AskRocco, there's a useful picture of what these Slack-based AI agents are actually good for right now.

How this actually works day to day

We run GetViktor through a shared Slack channel with everyone at our company, seven people total, so anyone can post a request and anyone can see how everyone else is using it. That visibility turns out to matter more than I expected going in. When someone on the sales team asks it to draft a LinkedIn post in my tone based on a recent mastermind summary, and it comes back polished in about a minute, everyone else in the channel sees that happen and picks up new ideas for their own use of the tool. We've connected it to 19 different integrations so far, including our CRM, our calendar, our email platform, our video conferencing tool, our ad platforms, and our meeting transcription tool, and we spend around $400 a month on it. A fellow member who's building Patricia.app as a next-generation alternative gave me a demo of his product recently, and it's got a cleaner interface with nearly as many integrations already, likely rolling out more broadly within a month or two.

One thing worth naming honestly: reliability has improved a lot. Hallucination is down roughly 95 percent compared to a year ago in my experience. The real risk now isn't the tool making things up, it's the tool citing stale data confidently. I once had it write a report on AI company valuations that quoted a figure eighteen months out of date. The fix is simple but non-negotiable: after it drafts anything with numbers in it, ask it to go back and re-verify every claim and data point before you publish. That one extra step catches almost everything.

• Shared visibility. Everyone at the company posts and reads requests in one Slack channel, so the whole team picks up new use cases from watching each other.
• 19 connected tools. CRM, calendar, email platform, video conferencing, ad platforms, and meeting transcription are all wired in, at a cost of around $400 a month.
• Fewer hallucinations, more stale data. Made-up facts are down roughly 95 percent from a year ago; the real risk now is confidently citing outdated numbers, which a re-verification pass catches.

Content creation, where the payoff is the most obvious

This is where I've gotten the clearest return. My workflow is simple: I have it draft an outline, I approve or adjust the outline, I have it propose ideas for any charts or data visuals, I approve those, and then I have it write the full piece. From there I review and give feedback. What used to take me two to three weeks of writing time now takes about an hour of my attention to produce a well-researched, 25-page report as a finished PDF.

The part that genuinely surprised me is when it started being proactive without being asked. A few months in, it noticed that a report we'd published the previous year had been our best-performing piece of SEO content, and on its own initiative it drafted an updated version for the current year and had it ready for me the next morning. I'd been planning to pay a graduate researcher several thousand dollars to produce something similar. What came back was roughly 90 percent as good, done in a fraction of the time.

Where the other use cases show up

Content is the most visible use case, but it's far from the only one. Across our team and the other founders using similar tools, the same pattern keeps repeating: connect the agent to the systems you already run your business through, and it starts doing real work in each of them.

• Dashboard building and financial analysis. It can query connected data sources like payment processors and community platforms directly, building reports and dashboards without anyone touching a spreadsheet.
• Bookkeeping. It can categorize transactions and handle a meaningful share of routine accounting work, freeing up hours that used to go to manual entry.
• Meeting prep. One founder described it messaging him each morning with his day's meetings, relevant research, and context pulled from prior conversations, then joining the calls itself to take notes.
• Meeting notes. A built-in note-taker joins calls, produces a rundown afterward, and posts it to the team's shared channel so everyone can discuss what happened without having to attend.
• Ad management. A newer tool built by a fellow mastermind member, AskRocco, focuses specifically on Meta ad creative and campaign optimization, with Google Ads support coming soon.

Email writing sits in this same category, even though it doesn't get talked about as its own feature. Because the agent is already connected to the tools we use to send outbound campaigns and our warm newsletter, it can draft sequences and individual messages using the same research and tone it applies to blog content, then hand them off for a quick human review before anything goes out. It's less a standalone use case and more a natural extension of the content workflow, applied to a different format and a different audience.

Software development, where it's changing the product loop entirely

The founder building Patricia.app described the deepest workflow change of anyone on the call. When a bug gets logged, whether internally by the team or directly by a customer using the product, the agent creates the tracking ticket, assigns it, and triages it automatically, all inside Slack. In a meaningful share of cases, it goes further and actually fixes the bug in code, with a human still giving final approval before anything ships to production. That combination, customers can log bugs directly and get a fix shipped back within 24 hours, has become a real guarantee his company can make, because the agent removes almost all the manual triage and handoff time that used to sit between a bug report and a fix.

There's a broader shift buried in that example worth naming. The traditional idea of a fixed product roadmap, planned quarters in advance, starts to look outdated when an agent can pick up a request, prioritize it, and act on it in near real time. That doesn't mean roadmaps disappear, but it does mean the gap between someone asking for something and getting it is shrinking fast, and founders who build that responsiveness into their process now will have a real edge over the ones still running quarterly planning cycles.

The part that's easy to miss: this isn't just for the technical people

One point that came up late in the discussion is worth sitting with. Most of us running these tools are already comfortable with technical workflows, connecting integrations, writing prompts, working directly in coding tools. But a support team member based outside the core team, someone who isn't going to open a coding environment or configure an API connection, still benefits enormously from an agent that lives inside Slack, where she already works every day. That's arguably the more important use case long term. The founders and technical leads were always going to find ways to use AI effectively. The bigger opportunity is giving that same advantage to the rest of the team, in the tools they already use, without requiring them to become technical to benefit.

There's also a quieter benefit that compounds over time: teams that log their AI agent's conversations with customers, whether through a support chatbot or an internal tool, can mine those transcripts a few times a week to spot confusion, missing data, or recurring bugs. One founder described fixing four separate issues in a single morning just from reviewing what his AI support tool had struggled to answer that week. The agent becomes not just a productivity tool but a source of product feedback that used to require manually combing through support tickets.

What I'd tell a founder just getting started

If you're weighing whether to bring one of these agents into your business, the honest advice from our group is to start with whichever workflow already has the most friction. For us that was long-form content, since writing well-researched reports used to eat weeks of my time. For the founder building Patricia.app, it was the bug-to-fix pipeline, since bugs were a constant drag on his engineering team's attention. For the founder behind AskRocco, it was ad creative and campaign management specifically. None of these founders tried to deploy an agent across every function of the business on day one. They picked the workflow that hurt the most, proved it out there, and expanded from a position of already having something working. That's the pattern worth copying, far more than any specific tool choice.