How this works in practice

FinCore tells us:

Our best client has between 100 and 500 employees, is growing and still runs a very manual finance operation.

From there, we build a list of companies with that profile. We can combine information such as:

• segment
• number of employees
• location
• estimated revenue
• technologies used
• team growth
• open roles
• funding received
• international presence
• public business information

Instead of starting with 50,000 random companies, we might get to, for example:

1,200 companies that really look like they could buy.

This is where data starts working.

One of those companies is AcmeTech. It has 220 employees, is hiring and has just opened an operation in another country. Everything suggests its finance operation is getting more complex.

Now we need to find out who should get our attention inside the company. We find:

• MSMariana SilvaCFO
• JPJoão PereiraController
• RCRafael CostaCEO

That doesn't mean we'll reach out to all three. The system works out that Mariana is probably the most relevant person. Now we have:

The right company + the right person + a likely right problem.

We know Mariana is AcmeTech's CFO. Now we enrich that contact with professional information that is available and appropriate for the operation. For example:

• LinkedIn
• work email
• company
• role
• career history
• company size
• company domain
• location
• possible mutual connections
• previous interactions with FinCore

We might find:

LinkedIn

available

Work email

available

Business phone

available

WhatsApp

not available

Mutual connection

none

That already changes the strategy. There's no reason to use WhatsApp. No referral is possible. But LinkedIn, email and phone are available.

AI shouldn't just write “Hi Mariana, how are you? Check out our solution.” It can analyze information about AcmeTech and help the team understand the context. For example:

• The company grew from 140 to 220 employees in 12 months.
• It opened finance roles.
• It announced international expansion.
• The CFO recently posted about financial close and operational efficiency.

AI can summarize all of this for the team:

AI summary

AcmeTech is growing fast, expanding internationally and growing its finance team. There are signs of rising operational complexity. The CFO recently showed interest in a more efficient financial close.

Now there's a sales hypothesis.

We don't know Mariana has a problem. But we have a plausible reason to believe a conversation is worth it.

At the same time, FinCore's founder publishes content on LinkedIn. Not generic content: content about the problems companies like AcmeTech face. For example:

Founder's post

Finance starts to break when the company grows faster than its processes.

Founder's post

3 signs your finance operation shouldn't depend on spreadsheets anymore.

Mariana may see one of these posts organically. If she engages, the system records it. Now our CRM knows:

Mariana is in the ICP

yes

Mariana saw or engaged with content

yes

That doesn't mean messaging her right away. It means we have more context.

A few days later, someone from AcmeTech visits FinCore's website. They visit:

1. Product
2. Client case study
3. Pricing

We may not know exactly who visited. But we know the company AcmeTech showed interest. Now the system brings together:

• company in the ideal profile
• CFO identified
• recent growth
• LinkedIn engagement
• website visit

Priority goes up. The company moves from “interesting company” to:

A company worth reaching out to now.

This is where data and automation start working together.

The CRM can automatically:

• update AcmeTech's priority
• record the new signal
• create a task
• notify the owner
• suggest the best channel
• generate a draft outreach

CRM alert

AcmeTech had website activity and is within the ideal profile. Mariana Silva is the main decision-maker identified. We suggest reaching out by LinkedIn and email.

The system does the operational work. A person decides whether the outreach makes sense.

AI uses the available data to create a first draft. For example:

LinkedIn draft

Mariana, I saw AcmeTech is growing a lot and expanding its operation. We've been working with finance teams that reached this stage and started struggling to keep manual processes going as the company grows. I thought it might make sense to talk.

BNW reviews it. The client approves it. Only then is the message sent.

AI sped up the work. It didn't make the sales decision on its own.

Mariana didn't reply. But we have her work email. The system waits a few days and starts another outreach. It's not a completely different message: it's the same sales thesis adapted to the channel.

Email

Mariana, we've been working with companies that grew fast and started facing more complexity in finance. I saw AcmeTech is in that expansion moment and thought it was worth getting in touch.

If she doesn't reply, we follow up. The CRM records everything. The team knows:

LinkedIn

sent

Email

sent

Follow-up

pending

Nobody has to remember this by hand.

Now imagine Mariana:

• opened two emails
• visited the website again
• clicked on a case study
• still hasn't replied

She has shown enough signals to justify a more direct approach. If we have an appropriate business phone, the team can call. The call isn't “Hi, I'm calling out of the blue to present our company.” It's:

Phone call

Mariana, I'm reaching out because we've seen that companies at AcmeTech's stage of growth tend to face a lot of complexity in finance. I wanted to understand if that's happening on your side too.

The channel changed. The context stayed.

Mariana takes the call. She says:

Mariana

Send me the material on WhatsApp.

Now there's context. The number is recorded in the CRM, and WhatsApp comes in as a continuation of the conversation. The system can automatically create the task “Send case study to Mariana on WhatsApp” or prepare the message:

WhatsApp

Mariana, this is Lucas from FinCore. As we agreed on the call, here's the case study I mentioned.

We don't use cold WhatsApp just because we found a number.

Two weeks later, FinCore sponsors an event for CFOs. The sponsor gets access to the attendee list according to the event's rules. BNW crosses that list with the CRM and finds:

Mariana Silva, AcmeTech

already being worked

The system doesn't create a new campaign as if we'd never spoken to her. It uses the history. A task might come up:

CRM task

Mariana will be at the CFO Summit. There's already been contact by LinkedIn, email and phone. Try to schedule an in-person conversation.

Message

Mariana, I saw you'll also be at the CFO Summit on Thursday. If it makes sense, we could grab a quick coffee there.

After the event, the CRM records the outcome.

Now imagine another company: BetaSoft. We want to talk to the CEO. The system identifies that a current FinCore client knows that CEO. Instead of cold outreach right away, we create a better option. The automation can flag:

CRM alert

Carlos, a FinCore client, knows André, BetaSoft's CEO. Do you want to ask for an introduction?

AI can suggest the text. But the introduction isn't sent automatically.

Relationships belong to people. Technology just spots the opportunity and reduces the work.

Then there's no referral. Simple. We go back to the other available channels. It could be:

1. Content
2. LinkedIn
3. Email

1. Email
2. Phone

1. Event
2. Email

1. Direct outbound

Each company can follow a different path. There's no mandatory sequence for everyone.

Most importantly, none of this can live in separate spreadsheets. When we open AcmeTech in the CRM, we want to see the whole story:

Now marketing, sales and management see the same story.

A lot can happen automatically behind the scenes. For example:

Automation doesn't replace strategy. It removes operational work.

AI can help to:

• research companies
• summarize information
• spot possible relevant signals
• classify companies
• prioritize contacts
• write first drafts
• personalize messages
• summarize conversations
• prepare salespeople for meetings
• update information in the CRM
• analyze why opportunities are moving forward or stalling

But there's one important rule:

AI speeds up the operation. Data gives context. Automation executes. People make decisions.

GTM Engineering is the infrastructure that makes all of this work as a system. It isn't just “using AI to write emails”. Or “automating LinkedIn”. Or “buying a list of leads”. It's connecting:

1. Data
2. People
3. Signals
4. Channels
5. CRM
6. Automation
7. AI
8. Sales process

So the company can keep answering:

• Who should we reach?
• Why now?
• Who's the right person?
• Which channel makes the most sense?
• What has already happened with this contact?
• What's the next action?
• What is actually producing sales and clients?