Your AI Stack

The Current Technology Dilemma

Think about the tools your business runs on today. There’s probably an email service like Microsoft Outlook or Gmail, a CRM such as Salesforce or HubSpot, a project management tool like Jira or Monday.com, a messaging platform like Slack, and a shared calendar. Most of those tools work well individually.

The problem is they do not talk to each other.

A task discussed in a meeting gets forgotten because no one updated the project board. A new lead sits in an intake form for two days because no one routed it. A follow-up email never gets sent because the calendar event did not trigger a reminder. 

The cost of all this is not only your software subscription monthly bill. It is also the implicit cost of your team spending time manually moving information from one system to another, all day, every day.

The issue is not the tools. It is the space between them. That is what a well-built AI stack addresses.

Building the Stack

Meeting Workflows: Claude + Monday.com

A call is recorded. A LLM (think Claude, GPT models from OpenAI, or Gemini) summarizes the transcript, pulls out action items and pushes them directly to Monday.com as assigned tasks. By the time the meeting ends, the to-do list is already built and sitting in your project board.

Project managers are not replaced. They are freed from the low-value step of manually transcribing what just happened. That time goes toward moving work forward instead of documenting it.

As a result, you’ll see fewer dropped balls and stronger follow-through, without adding headcount or changing how your team runs meetings.

Communication Workflows: Slack + ClickUp

A message posted in a Slack channel triggers a ticket in ClickUp automatically. No manual data entry. No copy and pasting from one tool to another. The request is captured, categorized, and assigned the moment it is sent.

Teams do not have to wonder whether something was logged. It was. The system is consistent in a way that people, however capable, simply are not. Every request follows the same path regardless of who sent it or when.

This kind of setup also creates auditability. When a client asks what happened to a request from three weeks ago, you have a clear record. That alone changes how confidently a team can operate.

Cross-Tool Automations

This is where a stack starts to feel like a system. A meeting added to your calendar automatically updates the associated CRM record. A lead submission triggers enrichment and creates an outreach task in your PM tool. A closed deal pushes a kickoff template to the relevant project board.

None of these are dramatic transformations on their own. But collectively, they eliminate the coordination layer that currently runs on people’s memories and good intentions. Small moments that compound daily.

You may be wondering: how do these programs talk to one another? It’s a mix of MCP and APIs. Let’s unpack that.

Model Context Protocol (MCP) is a technology developed by Anthropic that standardizes how AI models connect to external tools and data sources.

Now, what’s an API (application programming interface)? It’s essentially a translator between software programs. Think of it like this: an API is like the waiter at a restaurant. You (the diner) and the chef are two different software programs, and the waiter is the API. They take your order and pass it on to the chef.

Key Wins

Three things change when a business has a true AI stack in place.

Speed. Tasks move faster because no one is waiting for a human to notice something and route it. The handoffs are automatic.

Consistency. The same process runs the same way every time. It does not vary based on who is on shift, how full someone’s inbox is, or whether a reminder is missed.

Capacity. The same team can handle more output. Not because they are working longer hours, but because the coordination work that consumed their time has been automated. That reclaimed time goes toward higher-value work. Additionally, mental capacity is freed up from feeling bogged down by rudimentary tasks.

Next Steps

There is a bit of a catch, though: AI works best when you have a strong idea of what you want to build. Being able to write down (or draw a visualization) of the process you’d like to design works far better than asking AI to figure out what should improve in your business.

For ourselves, we rely on the phrase, “pay attention to what you pay attention to”, jotting down inefficient processes and bottlenecks we come across in our day-to-day lives. Then we build systems to solve these problems.

If you’re building your AI stack or simply need a thought partner throughout the process, we’d be happy to help. Brainstorm typically begins with an audit of your current tools, workflows, and gaps between them. From there, we design and build the connections that make your tech stack operate as a system rather than a mix of separate subscriptions.

If you’re wondering which integrations would create the most leverage for your team, let’s chat. Reply to this email or visit the Brainstorm website to get started.