People Data Labs alternative
The Best People Data Labs Alternative for AI Agents That Need Cited, Confidence-Scored B2B Data
A great rep once knew every account. Now your agents do.
But only if you give them data worth trusting.
What People Data Labs Actually Is
People Data Labs is a person and company data API. You send a query; you get back raw records. What you do with those records — the integration, the deduplication, the confidence logic, the routing — that's your problem. Developers build the plumbing themselves.
For teams with the engineering bandwidth, that flexibility is the point. For teams building AI-agent go-to-market, it's the bottleneck.
Why Teams Start Looking for Alternatives
Teams evaluating People Data Labs alternatives most often cite three things:
Integration effort. Raw records need transformation before an agent can act on them. That means custom pipelines, schema mapping, and ongoing maintenance every time an upstream field changes.
Monthly data refresh cycles. Stale data is a quiet tax. In a human workflow, a rep catches the bad phone number before the call. In an autonomous agent loop running at machine speed, bad data ships before anyone notices.
Per-record credit costs at scale. The math that works at five hundred records a month breaks at fifty thousand. Per-field or per-source billing compounds the problem when you're pulling from multiple enrichment providers — which most teams are.
Six enrichment tools. Six billing relationships. Six schemas to reconcile. No single source of truth on which field came from where, or how much to trust it.
That's the real pain. Not any one tool — the stitching.
What Agent-Ready Data Actually Requires
An AI agent is not a dashboard. It doesn't pause to sanity-check a job title or cross-reference a LinkedIn URL. It acts. Which means the data layer has to carry the confidence signal the agent would otherwise derive manually.
Three things make data agent-ready:
- Provenance — where did this field come from? LinkedIn? Hunter? Perplexity? A proprietary graph?
- Confidence — how much should the agent weight this field when making a decision?
- Reconciliation — when two sources disagree, which value wins, and can you see the conflict?
Without those three signals, agents act on assumptions dressed up as facts. At machine speed, that's expensive.
abm.dev: Account-Based Marketing for AI Agents
abm.dev is not another enrichment API you integrate yourself. It's the account-based marketing API built for autonomous agent loops — Search, Enrich, and Create, with a source and a confidence score on every field.
Eighty-Nine Canonical Fields. Every One Cited.
One enrichment call returns eighty-nine canonical fields: forty-three person fields, forty-six company fields. Every field carries three things: a citation, a source attribution, and a confidence score.
No fabricated facts. No silent fallbacks. No field that arrived without a paper trail.
When your agent reads a field value, it knows where that data came from and how much to trust it. That's the difference between an agent that personalizes and an agent that guesses.
Ten Providers. One Call. One Bill.
abm.dev aggregates ten data sources behind a single API call — deduped, reconciled, and returned as one clean record. No per-source charges. No per-field charges. You pay per enrichment.
One call, ten providers behind it. No per-source bills.
The providers doing the work include sources like LinkedIn, Hunter, and Perplexity — named, visible, and attributed. You're not buying a black box. You're buying a sourced record.
Pricing That Scales Like a Developer Tool
abm.dev bills per enrichment, not on a subscription:
- Entry: thirty credits from €2.89 — €0.29 per enrichment
- At volume: down to €0.06 per credit at two thousand credits
- Credits never expire
- The playground is free
No monthly minimums. No credits that vanish at the end of the billing cycle. No conversation with a sales rep to understand what you're paying for.
Start in the playground. Scale when the pipeline proves out.
The Claude Connector: MCP-Native from Day One
abm.dev ships a Claude Connector so an agent can call Search, Enrich, and Create directly over the Model Context Protocol (MCP). No middleware. No custom integration layer between your LLM and your enrichment stack.
Built for autonomous agent loops, not human dashboard-watching.
If you're building with Claude, abm.dev is callable as a native tool. The agent searches for accounts, sources contacts, enriches records, and creates outreach artifacts in a single reasoning loop. The data it acts on is cited. The confidence scores are in the payload. The agent knows what it knows.
People Data Labs vs. abm.dev: The Honest Comparison
| People Data Labs | abm.dev | |
|---|---|---|
| Data format | Raw records | Eighty-nine canonical fields, cited |
| Source visibility | Limited | Every field attributed |
| Confidence scores | No | Yes, per field |
| Billing model | Per record / subscription | Per enrichment, credits never expire |
| Agent integration | Build it yourself | MCP-native Claude Connector |
| Playground | Free | Free |
People Data Labs is a solid data infrastructure layer for teams with the engineering resources to build on top of it. abm.dev is the layer above — reconciled, scored, cited, and callable by an agent without a custom integration in between.
Different tools. Different jobs.
If your job is building AI-agent go-to-market, the integration overhead of raw records is a tax you pay before you've written a single prompt.
The Bigger Picture
Once upon a time, the best rep in the room knew every account. Not because they had more data — because they knew which data to trust, where it came from, and what it meant for the conversation they were about to have.
The data is rich enough now to do that again. At scale this time. But only if the data layer carries the signal the agent needs to reason well.
Cited fields. Confidence scores. Source attribution on every record. One call to ten providers. A billing model that doesn't punish you for growing.
That's what agent-ready enrichment looks like.
Try abm.dev
The playground is free. Eighty-nine cited fields on the first enrichment you run.
Launch credits available with the code LAUNCHCODES.
Personalization, at scale.
Stuart McLeod · Co-founder, abm.dev