Finding prospect companies in industrial zones: an AI-assisted scanning agent

Prospect company scanning is the preliminary research process in which a sales team systematically finds the potential customers in a target region and sector, scores them and turns them into a call list. For B2B sales teams focused on industry, this is one of the most laborious jobs before the first call.

In this article we explain how the AI-assisted scanning agent in YazSis CRM AI finds prospect companies from an industrial zone, what the agent collects and what it does not, and how the team makes the final decision.

The limit of finding prospects by hand

• Organized industrial zone sites, chamber directories, maps and web searches are browsed separately, and the results are carried into a spreadsheet by hand.
• The same company appears in different sources with different spellings, and duplicate records appear.
• There is no ranking in the list to show which company to call first.
• By the time the list is drawn up it may be out of date, and it is not clear who has taken on which company.

Five steps from scan task to call list

1. Open a scan task: you choose the province, district, industrial zone and sector. The source can be an OSB (organized industrial zone) website, a chamber directory, a web search, the official maps API, or a mix of these.
2. The agent scans and scores: the AI agent collects the company name, switchboard, general email, website and address. Findings land in the panel in score order.
3. The team approves: staff approve or reject each finding. An approved finding opens a new company card or fills only the empty fields of an existing card.
4. Build the call list: the manager filters by province, district, industrial zone, sector and score, builds a call list and assigns it to a salesperson.
5. Record the call: the salesperson calls the next company and records the outcome (reached, not reached, positive, negative or call again), the note and the time of the next call. A positive conversation becomes an opportunity.

The choice of source determines the quality of the task. Industrial zone sites and chamber directories are often tidy in terms of company name and address, while a web search and the official maps API can surface companies that are not in directories. When unsure, you can start with a mixed source and compare which source brings more findings using the source and finding counts on the scan tasks screen. The manager sees findings awaiting approval on the dashboard; findings of a similar nature can be approved in bulk and turned directly into a call list.

A fictional example: on Monday morning a sales manager opens a scan task for an industrial zone in a particular district and for the metalworking sector. The task enters the queue and the agent scans the source. The manager sees the findings awaiting approval on the dashboard; they review and approve the high-scoring ones and reject the unsuitable ones. They build a call list for the approved companies and assign it to a salesperson. The team starts calling with a list ordered by score instead of a hand-compiled spreadsheet, and who has which company is on record.

What the agent does and does not do

When people hear “AI agent”, they often picture an uncontrolled data collector. Here, in contrast, there is a helper whose limits are drawn from the start:

• It does not collect personal data. It writes only corporate contact information: company name, switchboard, general email, website and address.
• It does not scan every site. Sites that prohibit scraping and sources that require a login are not scanned; robots.txt rules are followed.
• It does not add companies on its own. No company card is opened until a finding is approved. Findings that are not approved are deleted at the end of the retention period.
• It does not overwrite existing records. If an approved finding updates an existing card, it fills only the empty fields.

The AI prepares the list; your team decides which company enters the CRM.

After approval: company card, ownership and map

An approved company becomes a company card where contacts, conversations, opportunities, quotes and email history are gathered in one place. A prospect company can be claimed by a salesperson, left in a pool or assigned in bulk, so it is clear who has the company. Information about a company also builds up in the team’s shared record, not in a salesperson’s notebook.

For regional planning there is a map of Turkey showing company density by province and a dot map on each province’s page. With the industrial zone layer and the type, owner, source, sector and tag filters, a region’s prospects, customers and former customers are seen together, and the field sales plan is made accordingly.

Questions before opening a scan task

• Which province, district and industrial zone is your priority? Starting with a small region first makes the results easier to evaluate.
• In which sectors is your customer profile clear? Scoring and filtering become meaningful with this information.
• Who will approve the findings? The approval authority should be tied to a role from the start.
• After the findings are approved, who will build the call list and who will make the calls?

YazSis CRM AI is used today in YazSis’s own support and sales center. We define the scope of a setup adapted to your organization together after a needs analysis. You can look at the scanning agent and the other screens on the YazSis CRM AI page.