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Every month someone asks me what AI tools I actually use. Not what I recommend in theory. What I personally rely on, day to day, to run the operation.
The honest answer is fewer than most people expect, and the ones I use are deeply integrated with each other rather than operating as a loosely connected collection of subscriptions. The mistake I see most often among entrepreneurs who are serious about AI adoption is that they accumulate tools the way some people accumulate gym memberships. Sign up during a moment of genuine motivation, use it consistently for about two weeks, then it quietly occupies a browser bookmark until the credit card declines at annual renewal.
That is not a technology problem. It is a systems design problem. Tools without architecture are just overhead with a better story.
This issue is a complete breakdown of my actual AI stack: what each tool does in the workflow, what it costs in time and money, and more importantly, how each piece connects to the others to produce outputs that no single tool could generate alone. Because a collection of tools is not a system. A system is what happens when those tools pass information to each other in predictable, structured ways and create compounding leverage over time.
The Design Principle: Everything Feeds Something
Before I get into specific tools, I want to explain the architecture principle I hold every piece of software to, because it is what separates a functional stack from a collection of subscriptions.
Every tool in my stack has one primary job and one or two defined handoff connections. The output of one process becomes the structured input for the next downstream process. Nothing in the stack is a dead end.
Here is a concrete example. When I record a client meeting using Fathom, the AI-generated transcript and action summary does not just sit in Fathom waiting for me to remember to look at it. Within minutes of the call ending, a Make.com scenario pulls the summary, appends the action items to the relevant client file in Notion, creates dated tasks from those action items, and generates a draft follow-up email staged in my outbox. One recorded conversation produces three coordinated, structured outputs with zero manual data entry from me.
That is the standard I hold every tool to. If it does not feed something downstream, it does not stay in the stack. Standalone tools that require me to remember to check them are exactly the tools that become expensive bookmarks.
Tool 1: Fathom (Meeting Intelligence)
Fathom joins every client call, every prospect discovery call, and every internal team meeting automatically. The AI transcription quality is excellent and the summaries are genuinely useful. But I want to be clear about where the real value lives, because most people who try Fathom focus on the summaries and miss the more important asset.
The real value of Fathom is the searchable institutional memory it creates. Every conversation I have had in the business for the past two years is archived, searchable, and timestamp-referenced. When a client references something they mentioned in a call last October, I can find the exact context in under a minute. When I want to review how I handle a particular objection across multiple discovery calls, I can pull the transcripts and look for patterns. When a misunderstanding arises about what was agreed in a previous engagement, the record exists and is unambiguous.
That institutional memory is worth more to the quality of client relationships than any individual summary feature. Most businesses run on a fragile foundation of memory and informal notes. Fathom turns conversations into a searchable, structured knowledge base that compounds in value over time.
Tool 2: Galaxy.ai (Multi-Model AI Hub)
I stopped paying for four separate AI subscriptions when I found Galaxy.ai. It provides access to multiple frontier models through a single interface for one consolidated monthly fee. When I need high-quality writing and reasoning, Claude handles it. When I need code generation or complex data manipulation, GPT-4 is my preference. When I want real-time information with search integration or a contrarian analytical perspective, Grok does the job.
The strategic value here is model diversity applied intelligently. Different large language models have genuinely different strengths, different failure modes, and different contextual blind spots. Using only one model is like insisting on using only one instrument in an orchestra. Galaxy.ai gives me the full range without requiring multiple billing relationships and multiple interfaces to manage.
Tool 3: Make.com (Automation Backbone)
Make.com is the connective tissue of the entire operation. It sits in the middle of the stack and handles every integration, every trigger, and every data movement between tools. If Fathom needs to push data to Notion, Make.com builds that bridge. If a new lead submits an application form, Make.com scores the lead, routes it to the appropriate pipeline stage, and fires the follow-up sequence.
I have approximately 30 active scenarios in my Make.com workspace. About eight run continuously in the background at any given moment. The initial time investment in building these automations was real: some scenarios took two or three hours to design and test properly. But the ongoing savings compound every week. My Make.com scenarios collectively save me between four and six hours per week of manual, repetitive work that would otherwise require my direct attention.
What I want to emphasize is that the learning curve is genuinely accessible. I am not a developer. I have a technical background from my engineering days, but these scenarios are built through a visual interface that requires no coding. If you can think through a logical sequence of steps, you can build a Make.com scenario.
Tool 4: Notion (Operating System)
My entire business lives in Notion. Client files. Project tracking. Standard operating procedures. Financial dashboards. Content calendar. Meeting notes. Team documentation. It is the single source of truth for everything that happens in the organization.
The critical architectural point is that Notion is not where things are manually entered. It is where things land after other tools process and structure them. Client call summaries flow in from Fathom via Make.com. Content ideas flow in from my research tools. Financial data flows in from QuickBooks via a weekly sync. The humans on my team add context and judgment. The automations handle the data movement.
Tool 5: Beehiiv (Newsletter Platform)
This newsletter runs on Beehiiv, and after testing several competing platforms, it is the one I recommend without hesitation for anyone building a serious content operation. The deliverability has been consistently excellent. The analytics are deep enough to be genuinely actionable. The segmentation capabilities allow me to send meaningfully different content to different subscriber groups based on behavior and interests, without a complex tech stack on top of the platform.
The built-in growth tools deserve specific mention. The recommendation network has generated several hundred organic subscribers through cross-promotion with complementary newsletters. The referral program infrastructure is well-designed and does not require a separate tool to manage. For a publication focused on growth, having those tools native to the platform changes the economics significantly.
Tool 6: Littlebird.ai (Social Listening)
If you are producing content and not actively monitoring the conversations happening around your topic area in real time, you are developing content strategy in a vacuum. Littlebird.ai tracks keywords, topics, and industry conversations across platforms and surfaces the relevant signals in a clean dashboard. I use it every week to identify content angles I would not have arrived at through my own thinking, find the specific questions my audience is actively asking, and monitor what competing publications are covering.
It has replaced about two hours per week of manual research and made my content calendar significantly more responsive to what is actually happening in my audience's world rather than what I assume they want to read about.
Tool 7: Clay.earth (Relationship Intelligence)
Clay.earth is my CRM for real human relationships. Not for leads and deals, but for the actual network of people who matter to the business: current clients, past clients, collaborators, advisors, and referral partners.
Clay aggregates signals from LinkedIn, email history, calendar activity, and social platforms to build a living contact database that tells me who I have not connected with recently, what has changed in their professional situation, and what relevant context I should have before reaching out. It surfaces these signals proactively rather than requiring me to remember to check.
In a business that runs substantially on the quality of relationships, this is not a nice-to-have. It is how I maintain genuine relationship quality across a network that would otherwise be too large to manage attentively with limited time.
How They Work Together: A Real Workflow
Let me illustrate how these tools combine by walking through a complete cycle from new subscriber to new client.
A new subscriber signs up for The Wealth Grid through Beehiiv. Make.com Every month someone asks me what AI tools I actually use. Not what I recommend in theory. What I personally rely on, day to day, to run the operation.
The honest answer is fewer than most people expect, and the ones I use are deeply integrated with each other rather than operating as a loosely connected collection of subscriptions. The mistake I see most often among entrepreneurs who are serious about AI adoption is that they accumulate tools the way some people accumulate gym memberships. Sign up during a moment of genuine motivation, use it consistently for about two weeks, then it quietly occupies a browser bookmark until the credit card declines at annual renewal.
That is not a technology problem. It is a systems design problem. Tools without architecture are just overhead with a better story.
This issue is a complete breakdown of my actual AI stack: what each tool does in the workflow, what it costs in time and money, and more importantly, how each piece connects to the others to produce outputs that no single tool could generate alone. Because a collection of tools is not a system. A system is what happens when those tools pass information to each other in predictable, structured ways and create compounding leverage over time.
The Design Principle: Everything Feeds Something
Before I get into specific tools, I want to explain the architecture principle I hold every piece of software to, because it is what separates a functional stack from a collection of subscriptions.
Every tool in my stack has one primary job and one or two defined handoff connections. The output of one process becomes the structured input for the next downstream process. Nothing in the stack is a dead end.
Here is a concrete example. When I record a client meeting using Fathom, the AI-generated transcript and action summary does not just sit in Fathom waiting for me to remember to look at it. Within minutes of the call ending, a Make.com scenario pulls the summary, appends the action items to the relevant client file in Notion, creates dated tasks from those action items, and generates a draft follow-up email staged in my outbox. One recorded conversation produces three coordinated, structured outputs with zero manual data entry from me.
That is the standard I hold every tool to. If it does not feed something downstream, it does not stay in the stack. Standalone tools that require me to remember to check them are exactly the tools that become expensive bookmarks.
Tool 1: Fathom (Meeting Intelligence)
Fathom joins every client call, every prospect discovery call, and every internal team meeting automatically. The AI transcription quality is excellent and the summaries are genuinely useful. But I want to be clear about where the real value lives, because most people who try Fathom focus on the summaries and miss the more important asset.
The real value of Fathom is the searchable institutional memory it creates. Every conversation I have had in the business for the past two years is archived, searchable, and timestamp-referenced. When a client references something they mentioned in a call last October, I can find the exact context in under a minute. When I want to review how I handle a particular objection across multiple discovery calls, I can pull the transcripts and look for patterns. When a misunderstanding arises about what was agreed in a previous engagement, the record exists and is unambiguous.
That institutional memory is worth more to the quality of client relationships than any individual summary feature. Most businesses run on a fragile foundation of memory and informal notes. Fathom turns conversations into a searchable, structured knowledge base that compounds in value over time.
Tool 2: Galaxy.ai (Multi-Model AI Hub)
I stopped paying for four separate AI subscriptions when I found Galaxy.ai. It provides access to multiple frontier models through a single interface for one consolidated monthly fee. When I need high-quality writing and reasoning, Claude handles it. When I need code generation or complex data manipulation, GPT-4 is my preference. When I want real-time information with search integration or a contrarian analytical perspective, Grok does the job.
The strategic value here is model diversity applied intelligently. Different large language models have genuinely different strengths, different failure modes, and different contextual blind spots. Using only one model is like insisting on using only one instrument in an orchestra. Galaxy.ai gives me the full range without requiring multiple billing relationships and multiple interfaces to manage.
Tool 3: Make.com (Automation Backbone)
Make.com is the connective tissue of the entire operation. It sits in the middle of the stack and handles every integration, every trigger, and every data movement between tools. If Fathom needs to push data to Notion, Make.com builds that bridge. If a new lead submits an application form, Make.com scores the lead, routes it to the appropriate pipeline stage, and fires the follow-up sequence.
I have approximately 30 active scenarios in my Make.com workspace. About eight run continuously in the background at any given moment. The initial time investment in building these automations was real: some scenarios took two or three hours to design and test properly. But the ongoing savings compound every week. My Make.com scenarios collectively save me between four and six hours per week of manual, repetitive work that would otherwise require my direct attention.
What I want to emphasize is that the learning curve is genuinely accessible. I am not a developer. I have a technical background from my engineering days, but these scenarios are built through a visual interface that requires no coding. If you can think through a logical sequence of steps, you can build a Make.com scenario.
Tool 4: Notion (Operating System)
My entire business lives in Notion. Client files. Project tracking. Standard operating procedures. Financial dashboards. Content calendar. Meeting notes. Team documentation. It is the single source of truth for everything that happens in the organization.
The critical architectural point is that Notion is not where things are manually entered. It is where things land after other tools process and structure them. Client call summaries flow in from Fathom via Make.com. Content ideas flow in from my research tools. Financial data flows in from QuickBooks via a weekly sync. The humans on my team add context and judgment. The automations handle the data movement.
Tool 5: Beehiiv (Newsletter Platform)
This newsletter runs on Beehiiv, and after testing several competing platforms, it is the one I recommend without hesitation for anyone building a serious content operation. The deliverability has been consistently excellent. The analytics are deep enough to be genuinely actionable. The segmentation capabilities allow me to send meaningfully different content to different subscriber groups based on behavior and interests, without a complex tech stack on top of the platform.
The built-in growth tools deserve specific mention. The recommendation network has generated several hundred organic subscribers through cross-promotion with complementary newsletters. The referral program infrastructure is well-designed and does not require a separate tool to manage. For a publication focused on growth, having those tools native to the platform changes the economics significantly.
Tool 6: Littlebird.ai (Social Listening)
If you are producing content and not actively monitoring the conversations happening around your topic area in real time, you are developing content strategy in a vacuum. Littlebird.ai tracks keywords, topics, and industry conversations across platforms and surfaces the relevant signals in a clean dashboard. I use it every week to identify content angles I would not have arrived at through my own thinking, find the specific questions my audience is actively asking, and monitor what competing publications are covering.
It has replaced about two hours per week of manual research and made my content calendar significantly more responsive to what is actually happening in my audience's world rather than what I assume they want to read about.
Tool 7: Clay.earth (Relationship Intelligence)
Clay.earth is my CRM for real human relationships. Not for leads and deals, but for the actual network of people who matter to the business: current clients, past clients, collaborators, advisors, and referral partners.
Clay aggregates signals from LinkedIn, email history, calendar activity, and social platforms to build a living contact database that tells me who I have not connected with recently, what has changed in their professional situation, and what relevant context I should have before reaching out. It surfaces these signals proactively rather than requiring me to remember to check.
In a business that runs substantially on the quality of relationships, this is not a nice-to-have. It is how I maintain genuine relationship quality across a network that would otherwise be too large to manage attentively with limited time.
How They Work Together: A Real Workflow
Let me illustrate how these tools combine by walking through a complete cycle from new subscriber to new client.
A new subscriber signs up for The Wealth Grid through Beehiiv. Make.com detects the subscription event immediately and checks the source tag to determine how they found the publication. If they came from a specific lead magnet, they receive a tailored welcome sequence. Their contact information is simultaneously pushed to Clay.earth so they enter my relationship graph from day one.
When that subscriber eventually fills out a strategy call application, Fathom automatically joins the discovery call. Within minutes of the call ending, the action items are extracted by Make.com and pushed to the client's Notion file, tasks are created with due dates, and a draft follow-up email is queued for my review. The entire post-call workflow requires about three minutes of my attention rather than the thirty it would take if I were doing it manually.
When I sit down each week to plan content, I review Littlebird.ai for trending conversations in my space. I bring the most compelling angles into Galaxy.ai and develop outlines across multiple models to stress-test the thinking. The final content gets written, formatted, and scheduled in Beehiiv with the appropriate segmentation applied. Make.com logs the published content to my Notion content calendar automatically.
The right AI stack does not make you dependent on technology. It frees your judgment for the decisions that actually matter.
What to Build First
If you are building this from scratch or significantly rebuilding, resist the impulse to implement everything at once. Start with three foundational tools: Fathom for meeting intelligence, Make.com for automation, and Notion as your primary operating system. Get those three integrated and functioning before you add anything else.
Add tools only when you hit a specific, identified friction point in your workflow. Not because the marketing looked compelling. Because you know precisely what problem it solves, where it sits in your architecture, and how it will connect to what you already have.
Every tool in my stack earns its place monthly. The ones that do not earn it get cut without sentiment. That standard is what keeps the stack lean enough to actually use and powerful enough to actually matter.
If you want my full stack overview with integration maps showing exactly how each tool connects, reply to this email with the word STACK and I will send the complete breakdown.
Wealth is a system, not a guess.
Alex Rivera
Wealth Architect at The Wealth Grid
Tools referenced in this issue:
Fathom.video (AI meeting intelligence): https://fathom.video/invite/c-kq_A
Galaxy.ai (multi-model AI hub): https://galaxy.ai/?ref=danr2
Make.com (automation backbone): https://www.make.com/en/register?pc=dkcapital
Littlebird.ai (social listening): https://littlebird.ai/download?utm_medium=referral&source=invite_link&referralcode=ZGFuQHBpbm5hY2xlbWFzdGVycy5jb20=
Clay.earth (relationship intelligence): https://clay.earth/?via=dan-kaufman
Beehiiv (newsletter platform): https://www.beehiiv.com?via=Dan-Kaufman