Turn a name and an email into a complete company record.
Automatically enrich B2B contacts in your CRM with industry, size, revenue, legal form and location. Through API and AI, right where your sales team already works. So segmentation and prioritisation stand on real data, not half-filled fields.
What company data enrichment actually means
Most CRMs are thin in one spot: a contact comes in through a form, leaves a name and an email, and that is it. For the person that is fine, for sales it is not. Who is supposed to prioritise when half the records do not even have the industry or the company size?
Enrichment closes exactly that gap. From what you already have, usually the company domain, the rest gets filled in: industry, headcount, revenue band, legal form, seat. It happens automatically, either the moment a new contact arrives or in batches across the base. What you end up with is a record you can filter and segment on, instead of a line that helps no one day to day.
This is one building block of AI data enrichment. This page is about the B2B part of it, the company data behind a contact.
Which fields can be enriched
Not every field is equally reliable. Registry data is solid, estimated revenue figures are ballpark. I tell you upfront which fields realistically fill in well for your target group and which stay patchy.
Industry and activity
Sector, a rough read of what the business does, often with a classification code. Lets you segment without anyone researching the industry by hand.
Company size
Headcount or a range. For sales this is usually the single most important number, because it drives who to approach and in what order.
Revenue and figures
Revenue band, sometimes growth. How reliable these are depends on the source, so I say upfront what is realistic and what is a rough estimate.
Legal form and registry data
GmbH, AG, sole trader, plus registry number and registered seat. This comes from official sources and is correspondingly solid.
Location
Address, country, region. Useful for territory splits in sales and for anything that legally hangs on the registered seat.
Web and contact channels
Company domain, main phone number, sometimes social profiles. Turns half a line into a record the team can actually work with.
Where the data comes from
There is no single source everything comes from. Registry data like legal form and seat comes from official directories. Company size and revenue come from specialised data providers via API. A rough industry read can in many cases be derived from the company website, and this is where a language model helps by classifying the page text, instead of someone clicking through every domain by hand.
The more important part is not the source anyway, it is the matching: how does an existing contact get mapped cleanly to the right company? Over the domain it usually works well. With freemail addresses or incomplete company names it gets fuzzier, and then it is more honest to leave a field empty than to fill it wrong. A wrong revenue figure in the CRM is worse than an empty field, because the team trusts it.
When enrichment is worth it
The value does not come from fuller fields as such, but from sales finally being able to filter by the right criteria. If you recognise yourself here, it is a good time.
- You have thousands of CRM contacts where barely anything beyond name and email is filled in.
- Sales cannot segment cleanly because industry and company size are missing on most records.
- Forms deliberately ask for little to keep bounce rates low, yet the leads still need to end up complete.
- You want to filter target accounts by fixed criteria, but the fields you would filter on are empty.
- Ahead of a bigger campaign or a CRM switch, the whole database should be filled in properly once.
How I set the enrichment up
No big tooling project. What you end up with is a workflow that runs quietly in the background and that you do not have to touch again after it is set up.
Clarify the base and the goal
We first look at what is already there and what is missing. Which fields do you actually need to segment and prioritise better? Enriching for the sake of it is pointless; it is about the few fields that make a difference day to day.
Pick sources and matching
I select suitable data sources and define how a record is matched, usually via the company domain or a combination of name and seat. Clean matching decides quality, not the number of sources.
Test run on a sample
Before the whole database is touched, a sample runs through. You see on real records what comes back, how high the hit rate is and where the limits are. Only once that fits do we go further.
Automate into the CRM
The enrichment lands as a workflow directly in HubSpot or your CRM, usually via n8n. New contacts are filled in automatically, the existing base in batches. Nothing runs past your system into a separate spreadsheet.
What it does in practice
A worked example, marked as such, not a measured number: say someone researches industry and company size for one record by hand and needs three minutes for it. Across 5,000 contacts that is around 250 hours of dull research. The same enrichment runs automated overnight, and the hours go where they count, into conversations with the contacts that are now cleanly prioritised.
What this looks like in practice is in the CRM data enrichment case study. If the company data should come from another system, say an ERP or a line-of-business app, that is more a case for CRM integration.
Common questions about company data enrichment
Tell me what is missing in your CRM.
Tell me briefly which CRM you use, how many contacts are in it and which fields you miss most. In the intro call I estimate hit rate, effort and cost before anything runs.
- Free intro call, about 15 minutes
- Sample test run before the whole base is touched
- An honest take on which fields are worth it and which are not