How to Extract Company Information From a Website

To extract company information from a website reliably, define the fields you need, collect the relevant pages, capture each fact with its source, and verify time-sensitive details before using the result.

The goal is structured data—not a rewritten company profile. A good extraction workflow tells you what the site states, where it states it, when the information was published, and which fields remain unknown.

Define the output schema first

Without a schema, manual research and AI tools tend to collect whatever looks interesting. Decide what the destination needs before visiting the site.

A basic company dataset may include:

For sales research, you might add public initiatives, hiring signals, and recent announcements. For a directory, category, location, and contact fields may be enough. Avoid collecting personal or sensitive data that the use case does not require.

Map the pages that hold useful facts

The homepage rarely contains everything. Review the main navigation and collect pages with distinct roles:

• About: identity, history, mission, locations;
• Product or service pages: offers, features, and use cases;
• Solutions or industries: audience and market focus;
• Leadership: names and titles;
• Newsroom or press kit: dates, milestones, approved descriptions;
• Careers: team language and public hiring information;
• Contact and footer: address, legal name, and public channels;
• Terms or privacy pages: operating entity and jurisdiction.

If a page is behind a login, blocked, or removed, mark it unavailable. Do not infer its content from a search snippet.

Extract claims with provenance

For every material fact, store:

1. the field name;
2. the extracted value;
3. the exact source URL;
4. the page title or section;
5. the date shown on the page, if any;
6. the date you accessed it; and
7. a confidence or review status.

This turns a pile of copied text into auditable information. It also makes updates easier: when a leader changes or a product is renamed, you know which source to revisit.

Keep direct website statements separate from your classification. A site may describe itself as “an operations platform for mobile teams.” Mapping that phrase to a database category such as “field-service management software” is an interpretation and should be labeled as one.

Resolve conflicting information

Company websites often contain contradictions. A press release may use an old employee count, a regional contact page may show a local office as headquarters, or a leadership bio may remain after a role change.

Use these rules:

• prefer the most recent first-party source for changing facts;
• prefer legal or contact pages for the operating entity and address;
• keep dates attached to metrics;
• record both values when the conflict cannot be resolved;
• mark the field for human review instead of choosing the more convenient answer.

External authoritative sources can help confirm public filings or legal identity, but do not silently blend them into “website-extracted” data. Preserve the source type.

Use AI for extraction, not invention

AI is helpful when pages are long or the same fields must be collected across several URLs. Give the model a strict schema and require null values for missing information.

Try a prompt like:

Extract only information explicitly stated in the supplied webpages. Return a table with field, value, source URL, page date, and review note. Use “not found” when a field is missing. Do not infer headquarters, founding year, employee count, customers, revenue, or market position.

iWeaver’s AI Website Summarizer can help condense public webpages, while the Company Research Generator can organize several company sources into structured research. Always compare important fields with the original page.

Worked extraction example

Suppose a software company’s homepage says it helps “independent clinics coordinate patient communications.” Its product page names appointment reminders and message routing. The About page lists Boston as its home base, while a two-year-old announcement calls New York its headquarters.

The correct extraction is not to pick Boston without comment. Record Boston from the current About page, retain the older New York value as a conflict note, and flag headquarters for confirmation. The product list can be captured as stated. “Healthcare SaaS” may be a useful category, but it should be labeled as a researcher-assigned classification rather than a direct quote.

Clean and normalize the data

After extraction, standardize capitalization, country names, phone formats, and category labels without changing meaning. Deduplicate product names that appear in navigation and body copy. Preserve the raw value alongside the normalized value when the data will enter a CRM or database.

Run basic validation: required fields present, URLs valid, dates in one format, one value per field where expected, and no unsupported numbers. For a recurring project, compare the new extraction with the previous version so changes receive review.

Extraction is only the first stage

Structured website data can feed a directory record, account brief, fact sheet, or writing workflow. If the next task is to turn verified inputs into readable company content, see what a company information generator does.

Do not skip the boundary between these stages. Extraction asks, “What does the source say?” Content generation asks, “How should approved facts be presented for this audience?” Keeping them separate makes both outputs more reliable.

The best extraction is not the longest one. It is a traceable dataset with clear gaps, dated evidence, and enough structure for a person—or another system—to use without guessing where the facts came from.