Andy Marlow Profile picture
Jun 22 8 tweets 3 min read Read on X
This agency didn’t scale by adding headcount.

It scaled by adding Viktor.

In 30 days, they generated $133,752 in new ARR with the same team and same service line.

The difference was execution.🧵 Image
Meet Viktor.

An AI employee that lives inside Slack and Microsoft Teams, connects to 3,000+ tools, and helps teams execute work that normally never gets done.
Instead of switching between tools, the team can ask Viktor directly where work is already happening.

Reports, research, content planning, outreach, dashboards.

The requests happen in chat.

The outputs come back in chat.
The interesting part is what sits behind that chat window.

Viktor connects to 3,000+ tools and can work across your existing stack.

- Google Ads.
- Google Sheets.
- Notion.
- Google Drive.
- Pinterest.
- YouTube.

The work stays connected to the systems your team already uses. Image
Need specialized workflows?

Viktor comes with installable skills.

- Marketing analysis.
- Creative briefs.
- Campaign optimization.
- Content research.
- Ad operations.

Instead of starting from scratch, teams can deploy proven workflows in minutes.
The real unlock is consistency.

Most teams know what needs to happen every day, week, and month.

The problem is remembering to do it.

Viktor can schedule recurring work so reports, research, monitoring, and operational tasks keep running without becoming someone's manual responsibility.
That's how Authority Makers unlocked more execution capacity.

Not by hiring.
Not by adding another service.
By making sure important work actually got done.

The biggest bottleneck isn't strategy.

It's execution.

Try free at @viktor__com. $100 in credits, no card.
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More from @AndyMarlowg

Jun 19
Spent more time cleaning PDF output than building the actual AI workflow.

So I tested @nutrientdocs PDF-to-Markdown CLI.

Here's what happened ↓ Image
If you're building with RAG, AI agents, or knowledge bases, you've probably hit the same problem:

PDFs are full of messy layouts, broken tables, and formatting issues.

Before feeding documents into an LLM, you usually spend time cleaning everything manually.
I tried @nutrientdocs PDF-to-Markdown CLI to see how well it handles the conversion process.

The tool takes a PDF and converts it into structured Markdown that's much easier for:

• RAG pipelines
• LLM ingestion
• Documentation systems
• AI workflows

No complicated setup required.
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