abm.dev vs Seamless.AI

A great rep once knew every account. Now your agents do.

The short version

Seamless.AI is a real-time B2B contact search engine — a web app and database aimed at sales teams building prospect lists. You search, you filter, you build a list, you export it. It’s generally sold on a subscription or per-seat basis. The center of gravity is the dashboard and the list, and for reps who live in that workflow that’s a real strength.

abm.dev is account-based marketing enrichment built for AI agents. The core is an enrichment API: hand it a person or company, get back verified contact data plus synthesized account research — built to be called inside your own agents and pipelines, not browsed in a UI.

Same neighborhood. Different reader. One was built for a rep building a list in an app. One was built for an autonomous agent loop.

What matters when an agent is the consumer

A human can eyeball a record and sense whether it’s right. An agent can’t. It needs the data to carry its own evidence. So the questions that matter shift.

Per-field citations, confidence, and selection reasons

abm.dev returns research with provenance attached at the field level: each value carries where it came from, a confidence score from zero to one, and why it was chosen (its selection_reason). An agent can branch on that — trust the high-confidence email, set aside the title it doesn’t rate, prefer the source it does. The judgment moves into the loop, where the agent can act on it, instead of staying in a human’s head.

This is the heart of the difference. A list-building product is designed to show a row to a person. An agent-first product is designed to defenda value to a program — to say not just “this is the title” but “this is the title, from this source, this confident, and chosen for this reason.” No fabricated facts. No silent fallbacks. A value is cited or it is not returned.

Live data, quality over quantity

The usual pitch in this category is the size of a maintained database — hundreds of millions of records on file. abm.dev’s pitch is the opposite. Every value carries its source, a confidence score from zero to one, and a selection_reason, and a value is returned only if it can be cited. No padding a record to look complete. You get fewer values, and you can trust all of them. Where coverage-first vendors optimise for the size of a maintained database, abm.dev optimises for cited, scored values you can act on.

And abm.dev resolves each enrichment live, at call time, across its sources — live web research through Perplexity and Tavily, email verification through Hunter, plus LinkedIn, Companies House, and others. The answer reflects the world at the moment you ask, not a snapshot from whenever a record was last crawled. Resolution happens when you call. The work is done against the live web, for the entity in front of you, then and there.

Ten sources, one call

abm.dev resolves data from ten sources — LinkedIn, Companies House, Perplexity, Tavily, Hunter, and others — behind a single call, reconciled into eighty-nine canonical fields (forty-three person, forty-six company) plus forty signals. One request, one normalized shape. No per-source bills, no stitching three vendors together by hand, no reconciling conflicting records yourself.

It’s also goal-aware: pass a goal_override — your ICP or persona — and it shapes and scores the result for that goal, rather than returning a generic record you then have to interpret. Emails carry an email_verification_level and confidence of their own.

Agent-native access, by design

abm.dev publishes the surfaces agents use to discover and call tools on their own — /llms.txt, /agent-tools.json, and /openapi.json at the root — all reachable over plain REST. There’s also an MCP server at https://mcp.abm.dev/mcp exposing tools like enrich_entity and get_enrichment_status. A Claude, OpenAI, LangChain, CrewAI, Cursor, Claude Code, or Windsurf agent can find the tools and call them with little glue. The integration target is the agent, not the human operator.

A search-engine-and-list product is reached primarily through its web app — excellent if your team lives in that app, less so if your “user” is a headless pipeline running at three in the morning.

Pricing shaped for programmatic use

abm.dev is per-enrichment: you pay for the calls you make, with no subscription or seat to carry, and credits never expire. All ten sources are included — no per-source bills, no per-field charges. It starts from about twenty-nine cents per enrichment, with packs at thirty for €2.89, one hundred for €9.29, five hundred for €36.99, and two thousand for €119.99. That fits spiky, automated workloads — an agent that enriches a thousand accounts this week and none the next. Contact-database products in this category have generally been sold on a subscription or per-seat basis; for specifics, see Seamless.AI’s own docs, as we don’t quote competitor pricing here.

The playground is free, and abm.dev is in open beta with around twenty dollars in free credits for every new account (code LAUNCHCODES) — enough to enrich a real list and judge it yourself.

Side by side

Where a cell says “not asserted here,” that’s deliberate — those are claims we won’t invent about another product. Confirm them against Seamless.AI’s own documentation.

When each one fits

Reach for Seamless.AI ifyour team is reps building prospect lists by hand — searching, filtering, and exporting from a web app where they already work. If the workflow is a person at a dashboard assembling a list to work, that’s its home turf.

Reach for abm.dev ifyour “user” is an agent. If you’re building GTM automations that need data which can defend itself — every field sourced, scored, and explained — discoverable and callable without glue, and priced per call rather than per seat. Built for autonomous agent loops, not human list-building.

Most ABM doesn’t fail on strategy. It fails on data and tooling — enrichment that’s scattered across vendors and built for dashboards instead of the agents and pipelines teams actually run now. That’s the gap abm.dev was built for.

Looking for a Seamless.AI alternative?

Most people searching for a Seamless.AI alternative cite the same thing: volume is easy, verifiable accuracy is hard. abm.dev’s answer is structural — every field arrives with its sources and a confidence score, so you (or your agent) can judge each value instead of trusting a black box.