# Best data enrichment tools: a buyer's checklist for 2026

A checklist for choosing data enrichment tools: coverage, price per row, freshness, verification and output shape, and where an agent with a catalog fits.

## Summary

Every list of the best data enrichment tools ranks vendors. That helps less than you would hope, because the right tool depends on what you enrich and how often. A better method is to test each tool against the same five questions.

This is that checklist. We wrote it for someone choosing how to build with agents, so each point says how to check it on a small batch before you commit.

## 1. Coverage

Coverage is how many of your rows come back with the field you asked for. It depends on your list. A tool strong on US software companies can be weak on local shops in Germany.

Test it with a sample from your real list, not the vendor's demo list. Take 100 rows, run them, and count the rows that return a usable value. Do this per provider. One provider's misses are often another provider's hits.

## 2. Price per row

Plans hide the real price. A plan with a pool of credits has an effective price per row that depends on how many credits you use and whether a miss costs a credit. Ask both questions.

Divide the cost of the run by the rows that returned a value, not by the rows you sent. That is your cost per useful row.

## 3. Freshness

People change jobs. Companies change domains. A database that was right a year ago is wrong for some share of your list today. Look for a last verified date on the record, and ask how often the provider refreshes it.

If a provider cannot tell you when a record was last checked, treat every record as unverified.

## 4. Verification

An email that exists in a database may still bounce. Verification checks the address before you send. For work emails, find and verify are two separate calls, and some providers do only one of them.

Check whether the tool tells you the result of verification as a field you can filter on. A single yes or no hides the difference between a valid address and a catch-all domain that accepts anything.

## 5. Output shape

Output shape is how the data comes back. Does it return named fields or one blob of text? Are the field names stable? Can your agent or script read it without cleaning?

For agents this matters more than for people. A person can read a messy cell. A script cannot. Run one call and look at the raw response before you build on it.

## The checklist on one page

| Question | How to test it | Red flag |
|---|---|---|
| Coverage | Run 100 rows from your own list | Vendor only shows its own demo list |
| Price per row | Cost divided by rows that returned a value | Misses are billed and not reported |
| Freshness | Look for a last verified date | No date on the record |
| Verification | Check for a separate verify call and a status field | One yes or no with no detail |
| Output shape | Read one raw response | Field names change between calls |

## Where an agent with a catalog fits

A catalog is a way to run the checklist several times at once. looot holds many providers behind one key. Your agent can send the same 100 rows to more than one provider, compare coverage, and keep the cheaper one per job.

It also changes the price question. You pay per call to the provider you picked for that call. There is no plan to outgrow. You can read each endpoint in the [public catalog](https://looot.ai/public-catalog) before you spend anything.

A catalog has limits. It returns data to your agent. It does not give you a shared table, and it does not sync to a CRM. Our [Clay alternatives post](https://looot.ai/blog/clay-alternatives-for-ai-agents) goes through that gap.

## Work emails right now

Work email lookup is the most common enrichment job. This block shows the cheapest priced endpoint in the catalog today.

Cheapest live price for work emails: live prices per lookup are read from the catalog when the page loads, see https://looot.ai/blog/best-data-enrichment-tools-checklist.

- Find a work email

## Company data right now

Company enrichment takes a domain and returns facts. Providers differ in how many fields they return, so a lower price may mean less data.

Cheapest live price for company data: live prices per lookup are read from the catalog when the page loads, see https://looot.ai/blog/best-data-enrichment-tools-checklist.

- Enrich a company

Tip: Pick on cost per useful row, not on list price. A cheap provider with low coverage can cost more per result than a dearer one that finds the field.

## Run the test yourself

Read the numbers it gives you, then pick. The same method works for [what 1,000 lookups cost](https://looot.ai/blog/what-1000-lookups-cost) once you scale up.

[Browse the catalog](https://looot.ai/public-catalog)

## Questions

### What are the best data enrichment tools?

It depends on your list. Test each tool on 100 rows of your own data and compare coverage, cost per useful row, freshness, verification and output shape.

### How do I compare data enrichment tools on price?

Divide the total cost by the rows that returned a usable value. Ask whether misses are billed. List price per row alone hides both.

### What should I check first when choosing data enrichment tools?

Coverage on your own list. A tool that performs well on its demo list can return little for your market.

### Do data enrichment tools verify email addresses?

Some do, some only find. Treat find and verify as two jobs and check that verification returns a status you can filter on.

### Can an agent replace data enrichment tools?

An agent can call several providers through a catalog and compare them. It returns data to you and does not provide a shared table or a native CRM sync.

### How fresh is the data from data enrichment tools?

Ask each provider for a last verified date on the record. If there is none, treat the data as unverified.

## For agents

### Prompt to test providers on your list

```text
Take the first 100 rows of my list. For work emails, send the same rows to each email provider you have access to. Report, per provider, how many rows returned an address, the price per call, and the cost per row that returned an address. Do not use more than these 100 rows.
```