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Andy Marlow

@AndyMarlowg

Jul 10 • 8 tweets • 3 min read • Read on X

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Growing an agency doesn't always require hiring more people.

One agency added $133,752 in new ARR in its first 30 days with Viktor.

Here's what changed. 🧵

I've been testing Viktor across real agency workflows to see where it actually saves time.

Instead of treating it like another chatbot, I gave it real operational work our team would normally do manually.

My first task was simple.

I asked Viktor to research prospects, organize the data, and prepare everything for outbound outreach.

Instead of giving suggestions, it delivered work my team could immediately use.

After that, Viktor kept the workflow moving.

It organized the research, updated the spreadsheet, prepared reports, and connected everything across our existing tools without forcing us to switch platforms.

That's the part I found most interesting.

Viktor works inside Slack or Microsoft Teams while connecting to 3,000+ tools behind the scenes.

Google Sheets.
Notion.
Google Drive.
Google Ads.
YouTube.

The work stays in one conversation while the execution happens across your stack.

The biggest shift isn't getting one task done faster.

It's giving your team the capacity to finish work that usually gets delayed because nobody has time to own it.

That's exactly why Authority Makers attributed $133,752 in new ARR during its first 30 days with Viktor.

Execution wins.

Viktor helps teams get more done without adding headcount.

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

The full link in my first reply ↓

Try Viktor for FREE here → ref.viktor.com/amarlow-x

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Andy Marlow

@AndyMarlowg

Jun 22

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.🧵

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.

Read 8 tweets

Andy Marlow

@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 ↓

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.