For example, a salesperson might spend Monday checking company websites, finding the right contacts and copying notes into a CRM. A GTM engineer builds a repeatable way to do the suitable parts of that research, flag uncertain results and give the salesperson something useful to act on.
The important question is simple: does the system leave the team with less busywork and better information? A large list of names is not useful if someone has to repair most of it.
What does a GTM engineer actually do?
The work varies by company. It can involve researching potential customers, assigning incoming enquiries to a salesperson, keeping CRM records up to date or noticing when an existing customer needs attention.
A GTM engineer usually works through four practical questions:
- What should start the work: a form submission, a company change or a scheduled check?
- What information does the next person need?
- How will the system tell a confirmed fact from a missing or uncertain answer?
- What should happen next, including when something fails?
For example, an enquiry from a large company might need a different salesperson from a request by a solo consultant. The system can research the company and prepare that handoff. Someone still needs to decide the assignment rules and check whether the right people receive the enquiries.
Clay's GTM engineering guide explains this kind of work through data research, verification and routing. Here, we'll follow one smaller example so you can see the decisions behind it.
A worked example: agencies hiring account managers
Imagine you sell software that helps agencies share client conversations. You want to speak with agency founders who might be reviewing how their team handles those conversations.
A current account-manager vacancy could be a reason to research an agency. It does not prove the agency has a communication problem. That difference matters: useful research gives you a better question to ask, not permission to invent a pain point.
This is a fictional workflow, not a customer case study or a claim that Linkedify supplies every step.
Step 1: Decide which companies belong on the list
Choose a group your product can actually serve—for this example, UK recruitment agencies. Find company records using a source you are authorized to use, then check their websites.
The useful output is a company name, website and a brief reason it fits. If the business is a job board rather than a recruitment agency, set it aside. Otherwise, every later step spends time researching a company you never meant to contact.
Step 2: Check the hiring detail and keep the evidence
Look for a current account-manager vacancy on the company's careers page or another reliable source. Keep the page link and the date it was checked beside the finding.
A recruitment agency may advertise jobs on behalf of clients. “This agency posted the job” is not enough to conclude “this agency is hiring for itself.” Read the employer details before using the vacancy in a message.
If the page is unclear or no vacancy is found, record that result. Don't turn missing evidence into “not hiring,” and don't invent a role to keep the workflow moving.
Step 3: Match the contact to the company
Find someone whose current role fits the conversation, then confirm their employer. A name match alone is weak evidence: two people can share a name, and a profile may mention an old company.
If you find a likely founder but cannot confirm they still work there, hold the record for review. That is more useful than handing a salesperson a confident-looking wrong answer.
Step 4: Prepare a draft that stays within the facts
An AI assistant can summarize the vacancy and draft an opener. The review is where you catch unsupported leaps.
What the evidence says: the agency has an account-manager vacancy.
What a poor draft invents: the agency is overwhelmed by client replies.
A more careful message would be:
Hi Maya, I saw your agency is hiring an account manager. I work on software for sharing client conversations. When a new person joins, do they take over an inbox or join conversations the team already shares?
The example makes the sender's purpose clear and asks about the process. It leaves Maya room to say that their current setup works well. Use this wording only when the vacancy and your own description are true.
Step 5: Give the next action to a person
After review, the salesperson decides whether to contact Maya and through which suitable channel. If LinkedIn is used, its rules still apply; connecting a channel to a larger workflow does not change them. See LinkedIn's automated-activity guidance.
A reply should become a clear next action. “We already use a shared inbox” may mean no fit. “We're changing that now” may call for a follow-up question. “Ask me next month” needs a reminder, not the rest of the original sequence.
Record who is handling the conversation so two teammates don't answer it at once.
What one finished record should look like
Here is a fictional record after research. The labels are examples you can use in a spreadsheet or CRM; they are not Linkedify screen labels.
| Field | Example value |
|---|---|
| Company | Example Recruitment Agency |
| Website | Company's verified website |
| Fits our audience? | Yes — UK recruitment agency |
| Hiring finding | Account-manager vacancy for the agency's own team |
| Evidence | Link to the vacancy, with the date checked |
| Contact | Maya, current founder; employer confirmed |
| Uncertainty | We do not know how the team handles client conversations |
| Draft | Question about how a new account manager joins client conversations |
| Next action | Sam reviews the evidence and decides whether to send |
Notice the uncertainty has its own place. Hiding it makes the record look more complete while making the salesperson less informed.
You do not need dozens of fields. Keep the ones that help someone confirm the facts or take the next step. If a field never changes a decision, ask whether it needs to be collected.
Which tools do you need?
Start with the job each tool must do. Your existing software may already cover several jobs.
| Job | A simple starting point | What to verify before connecting more software |
|---|---|---|
| Store companies, evidence and progress | A spreadsheet or your current CRM | Can the team find the latest record and avoid duplicates? |
| Research companies and contacts | Company websites and an appropriate data source | Does the source provide the specific fact you need? |
| Draft or classify information | An AI tool with human review | Does the answer match the source, including uncertainty? |
| Move information between tools | An existing integration or a small script | Which record will be updated, and what happens if the update fails? |
| Manage outreach and replies | The team's current channel tools | Can someone see who needs an answer and who is answering? |
A new subscription should solve a visible gap. Buying a research tool before you know what you need to confirm makes it easy to collect impressive-looking data that nobody uses.
Test the awkward cases before repeating the workflow
First follow one normal company from research to the final record. Then deliberately try cases that could produce a mistake:
- A company with no useful hiring page.
- A job posted by an agency for its client.
- A contact who has changed employers.
- The same company appearing twice.
- A page that fails to load.
Check that each produces an honest result and a sensible next step. A failed page load should say the page could not be checked. It should not become “no vacancy.” A duplicate company should not automatically create a second outreach task.
Then review a small batch before expanding. Look at the sources and finished records, not just whether every step has a green tick. A process can run without errors and still produce the wrong answer.
Check whether it saves enough work
Count the review and repair time as well as the time saved. Otherwise, automation can appear useful while quietly moving the work to someone else.
Here is an illustrative calculation, not a product result:
- Researching 50 companies manually takes 6 minutes each: 300 minutes.
- Reviewing the same 50 companies after automated research takes 2 minutes each: 100 minutes.
- Fixing failed records takes another 40 minutes.
- Net time saved per batch is 300 − 100 − 40 = 160 minutes.
If setup took 8 hours, or 480 minutes, it would take roughly three similar batches to recover that time: 480 ÷ 160 = 3. That assumes quality stays comparable. Ongoing maintenance, software charges and data fees still need to be included in the decision.
Also ask whether the resulting records lead to useful conversations. Faster research is helpful; faster research about the wrong companies is not.
How is GTM engineering different from RevOps or sales?
The responsibilities overlap, and companies use the titles differently. A practical way to divide the work is:
| Role | Example responsibility in this workflow |
|---|---|
| Sales | Decide whether the agency is worth contacting and handle the conversation. |
| Revenue operations, often called RevOps | Agree how leads are recorded, assigned and measured across the team. |
| GTM engineering | Build and maintain the research and handoff steps so those decisions can be carried out reliably. |
One person may do all three in a small business. The title matters less than making sure somebody owns the work after it is built.
Where Linkedify fits
Linkedify focuses on LinkedIn outreach campaigns, account management and replies. Those can be parts of a broader GTM process. Company research, qualification and CRM records may need other tools or manual work.
If your research already works but conversations are getting missed, that is a concrete reason to look at Linkedify. Ask to see how your team would find a reply, handle it and track the next step. Check any proposed connection to another tool separately rather than assuming it is included.
Common questions
Do I need to code to do GTM engineering?
Not always. Existing integrations and workflow tools can handle some processes. Code helps when you need checks or connections those tools cannot provide. Understanding the sales task, data quality and failure cases matters either way.
Is GTM engineering only for outbound sales?
No. Similar work can help with incoming enquiries, CRM updates and existing-customer follow-up. Outbound research is just the example used here.
When should a small business start?
When a useful process repeats often enough that building and maintaining it could save meaningful work. If you are still learning who needs your product, direct conversations may be the better next step.
What skills should a GTM engineer have?
They need to understand the customer and sales process, work with data, connect tools, check AI output and explain failures clearly. They should be able to show how a system helps the team, not just how many steps it contains.
What should we measure first?
Check whether the records are accurate and useful. Then compare time spent, total cost and business outcomes with the old process. Keep the workload and observation period comparable so the numbers mean something.