People Data Labs Alternative: API-First Enrichment Compared

The best People Data Labs alternative depends on what you are doing with the data. PDL sells person and company data as infrastructure: APIs and licensed feeds that a team builds its own enrichment logic on. NeuralVerge returns a finished, resolved record for one email address or phone number, over REST or MCP, with matching already done. If you want to consume enrichment, not build it, that is the difference that matters.

The short version

• —Verdict: pick PDL when the data itself is your raw material, for example a product that licenses person and company records or runs its own identity resolution. Pick NeuralVerge when you want one call to return one finished record that an application or an agent can use immediately.
• —Who NeuralVerge fits: product and go-to-market teams without a data engineering group to spare, and agents that need to identify a person mid-task.
• —What it costs: Email enrichment is 10 points per match ($10 per 1,000) and Phone enrichment is 20 points ($20 per 1,000). Only matches are charged. The same price applies over MCP. See pricing for plans.
• —What to check on PDL's side: current terms, how matching and likelihood are handled in the enrichment response, and whether it offers an MCP server and what that returns. We do not assert any of that here.

The table below compares NeuralVerge with PDL and similar B2B data providers, dimension by dimension.

Where People Data Labs fits

PDL's own site describes person, company and job-posting data, APIs to enrich, search, autocomplete and clean datasets, and licensed custom data feeds delivered through cloud storage and data-sharing platforms. That mix is what makes it infrastructure: you can query it record by record, or take it in bulk and put it in your own warehouse.

Where it is the right call

• —Building your own enrichment product. If data resolution is the core of what you sell, you need records to build on, not a finished answer that has already made the decisions for you.
• —Custom matching requirements. When a workflow has unusual matching rules that a built-in matcher cannot express, working from the records gives you control.
• —Bulk and warehouse use. A team with data engineers that wants a licensed feed to join against its own tables is using the product the way it is positioned.

What it asks of you

Whatever you build on a dataset, the matching and confidence decisions become yours: which candidate is the right person, what to do with a partial match, how to treat an address with no clean match, how often to refresh. That is a deliberate trade, and for the right team it is a feature. For a team that only wanted a name and a job title behind an email address, it is a project you have to staff before the first useful result.

Pricing, refresh cadence and the exact shape of PDL's responses are things to read on its own site before you commit; they change, and we would rather not state them for the vendor.

How NeuralVerge works

Email enrichment takes one email address and returns the person behind it: the names they are known by, current company and position, location, other emails and phone numbers, and linked social profiles where they exist. Phone enrichment does the same from a phone number in international format. There is no dataset to query and no matching layer to write; the response is the result.

What comes back

This is the response shape for one address, taken from the source page (the person is a public figure in the page's own example):

{
  "session_id": "11111111-1111-1111-1111-111111111111",
  "kind": "email_enrichment",
  "email": "sam@openai.com",
  "machine": {
    "full_names": ["Sam Altman"],
    "emails": ["sam@openai.com"],
    "phones": ["+14155550123"],
    "locations": [{ "country": "US", "state": "California", "city": "San Francisco" }],
    "company": "OpenAI",
    "position": "CEO",
    "linkedin_profile": "https://www.linkedin.com/in/sam-altman/",
    "x_profile": "https://x.com/sama"
  },
  "total_points": 10
}

The full response also carries a readable human summary and fields such as gender, date_of_birth (often empty) and telegram_username. Every call returns a session_id you can trace later. Phone numbers come back for a subset of matches only.

The enrichment sources at a glance

From the source pages, at the time of writing:

Resolved at request time

Each request is resolved when you ask, rather than served from a static file. A job change or a company rename shows up the next time you ask, not the next time a snapshot is rebuilt. The trade-off is honest: a live resolution depends on what is available at that moment, and we do not publish a hit-rate figure, because it depends almost entirely on your list. Work addresses at companies with a public presence resolve well; personal addresses and role mailboxes such as info@ or support@ resolve far less often.

Beyond email and phone

The finished-record model extends further. US phone numbers can come back with line type, carrier, prepaid status and a litigation-risk flag through Phone enrichment US. Company records come from official national registers and the global LEI system through dedicated registry sources, and each registry result carries the URL it was read from. People and company search across professional profiles return structured matches.

Here is what that approach adds up to, advantage by advantage:

Freshness, and what a snapshot can hide

A licensed dataset is only as current as its last refresh, and the record gives the caller no signal that it may be out of date. That matters most for the records that change: a contact who moved roles recently, a company that just changed leadership. A live resolution has no snapshot between the request and the answer. The other side of that trade is that a live call sees only what is available at the moment you ask, while a dataset may hold an archive built up over time. Neither is better in the abstract; it depends on whether your question is "who is this today" or "what do we know about this person overall".

What you do not get

You do not get a raw dataset or control over matching rules. NeuralVerge has already decided how a request resolves to a record, which is the opposite trade from a dataset you build on. The records also contain personal data, so use them for legitimate business purposes under your own lawful basis and check the acceptable use policy.

Worked example and migration

Take an illustrative case: an inherited list of 5,000 email addresses that you want as names, companies and roles.

On a dataset

You query the data for each address, then apply matching and confidence logic that your team writes to decide which candidate records are trustworthy, and how to handle addresses with no clean match. The data is there; turning it into a usable list is engineering work layered on top.

On NeuralVerge

Each address goes through Email enrichment, and a resolved record comes back where a match exists. Addresses that resolve to nobody are not billed, so a low-quality list costs you time rather than points. The list is the direct output of the calls, with no matching step in between.

If you are moving from PDL

1. —Run a sample of 100 to 200 of your own addresses through both and compare how many come back with a usable name, company and title. This is a faster way to judge fit than reading a feature list.
2. —Map the fields you actually use. Check the source page for the exact field names rather than assuming they match PDL's.
3. —Move the lookups that were a matching project into single calls, and keep PDL for anything that genuinely needs bulk records.
4. —Keep the matching code until the sample results convince you that you do not need it.

Both approaches produce a resolved list. The difference is whether turning data into a usable result is a step you build or a step the call already did.

REST and MCP

The same lookup is reachable both ways. Over REST, one address per request:

curl -X POST https://api.neuralverge.ai/functions/v1/run-email-enrichment \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "email": "jane.doe@acme.com" }'

Over MCP, the hosted server at https://mcp.neuralverge.ai/mcp exposes it as a tool (tool names use underscores; REST paths keep hyphens):

{
  "tool": "run_email_enrichment",
  "arguments": { "email": "jane.doe@acme.com" }
}

Add the URL to an MCP client such as Claude, ChatGPT, Cursor or VS Code, sign in through the browser and click Allow. There is no server to build or host and no key to paste into a config file. The MCP server for Claude walks through a full connection, and the client setup guides cover each app. run_phone_enrichment works the same way with a phone number in international format.

The response shape and the price match the REST call; there is no separate MCP rate. The practical difference from a raw dataset is that the tool result is a finished answer an agent can act on, not a set of candidate rows it has to score first.

• —REST for a scheduled job, such as cleaning the 5,000-address list above. Your code loops, retries and stores the output.
• —MCP when an agent decides mid-task that it needs to identify someone, for example while qualifying an inbound lead or preparing for a call.

AI research runs over REST only; enrichment, extraction, Search and the data sources are what an agent can call as tools around it. Connected clients can be revoked under Settings, then API, in the dashboard; their labels start with "Connector:". If you are weighing the two for an agent workflow, check whether PDL offers an MCP server and what it returns.

Cost and limits

• —Email enrichment: 10 points per match, $10 per 1,000. Phone enrichment: 20 points, $20 per 1,000. Phone enrichment US: 100 points, $0.10 per number. Figures are from the source pages at the time of writing; points come from your monthly plan allowance.
• —Billing: the source pages state that enrichment is charged only on a match and that misses are not billed. If you use other sources in the same pipeline, check the billing note on each page, because not every call behaves the same way.
• —Coverage: global, with the highest hit rate on business email addresses. Personal addresses and role mailboxes resolve far less often, and phone numbers appear for only some matches.
• —Phone enrichment does not return carrier or line-type data; Phone enrichment US does, for US numbers.
• —Personal data: these sources return personal data, so you need your own lawful basis and a working opt-out process. That is a decision for your own counsel, and the terms and acceptable use policy set the limits on our side.
• —PDL's price: not stated here. Compare current terms on its site against your expected volume before you decide.

How to decide

Answer these in order:

• —Are you building an enrichment product, or consuming enrichment inside a workflow? Infrastructure suits the first; a finished call suits the second.
• —Do you have engineers to build and maintain matching logic? A dataset without that investment is not usable enrichment yet.
• —Do you need bulk records in your own warehouse? That is where a licensed feed earns its place.
• —Is the job a single identifier turned into a person? Enriching an inbound signup, completing a CRM record or cleaning an inherited list are all one call per row.
• —Will an agent make the lookup? Then check what each vendor exposes over MCP and what an agent gets back.
• —Does the pipeline also need research or extraction? Check whether that means a second and third vendor or one account that already covers it.
• —Does freshness matter? A recently changed role is where a stale snapshot can return a confident wrong answer; ask each vendor how current a record is.

Run a real batch of your own identifiers through each candidate and compare the hit rate and the setup time.

Frequently asked questions

Can I use NeuralVerge enrichment over MCP, or only through the API?

Yes. Email and phone enrichment are available as the run_email_enrichment and run_phone_enrichment MCP tools at https://mcp.neuralverge.ai/mcp. Add the URL to an MCP client such as Claude, ChatGPT or Cursor, sign in through the browser and click Allow. The response shape and price match the REST call. Check separately whether PDL offers an MCP server and what it returns.

Is NeuralVerge trying to replace PDL's dataset?

No. Licensable person and company data that other products build on is a genuine PDL strength, and a finished, per-match call does not replicate it. If your job is building your own matching and resolution logic, records are what that job needs.

Does NeuralVerge let me build custom matching logic on top of its data?

Not in the way a dataset does. NeuralVerge resolves a request to a finished record directly, with matching and partial-match handling already built in, which is the opposite trade from data you build that logic on top of yourself.

Which one is better for a team without dedicated data engineering resources?

A finished, per-match service like NeuralVerge's is generally the better fit, since it does not require building and maintaining matching logic. PDL is a better fit when a team has the resources to build that logic and wants control over exactly how matching works.

Can I use PDL and NeuralVerge together?

There is no technical conflict, though the overlap means most teams pick one as their primary enrichment layer rather than running both for the same lookups. A team using PDL as infrastructure for its own product could still use NeuralVerge for a need PDL alone does not cover, like research or extraction.

How long does it take to get a usable result from each?

With NeuralVerge, a single REST or MCP call returns a finished record. With a dataset, the data itself may be available quickly, but turning it into a usable enrichment result means building matching and confidence logic first. That is engineering time before the first usable output, not a difference in query speed.

Does NeuralVerge collect data in real time, or from a stored dataset?

In real time. Each request to Email enrichment or Phone enrichment is resolved at the moment you ask, rather than served from a static file, so a recent job change or company update shows up the next time you ask. Ask any dataset vendor how often its records are refreshed.