abm.dev vs RB2B

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

The short version

RB2B is a website-visitor de-anonymization tool. It identifies anonymous visitors to your site at the person level — largely US traffic — and pushes them to Slack and your CRM, with a free tier plus paid plans. It’s aimed at growth and sales teams who want to know who showed up. That’s its job, and for teams chasing inbound intent it’s a real one.

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

Different jobs. RB2B answers “who landed on my site?” abm.dev answers “tell me, with citations, about the person or company I name.” One starts from traffic you didn’t choose. One starts from a target you did.

What matters when an agent is the consumer

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

Live data, quality over quantity

abm.dev resolves each enrichment live, at the moment you call it — running web research through Perplexity and Tavily, verifying email through Hunter, and reading LinkedIn, Companies House, and others on the spot. The answer reflects the world when you ask, not a record assembled at some earlier time. You ask about a named target; we go and research it now.

And it favors quality over quantity. We return fewer values, but every one carries its source, a confidence score from zero to one, and the reason it was chosen. A value comes back only if it can be cited. No padding a record to look complete. You get less, and you can trust all of it.

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, and why it was chosen — its selection_reason. An agent can branch on that — trust the high-confidence email, re-research the title it doesn’t rate, ignore the source it doesn’t trust. 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 signal you push to Slack tells a person “this name showed up.” 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.

Ten sources, one call

abm.dev enriches from LinkedIn and ten-plus providers behind a single call — aggregated, deduped, reconciled into eighty-nine canonical fields 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: tell it the ICP or persona you’re hunting and it scores and structures the result for that goal, rather than returning a generic blob you then have to interpret.

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 — and it’s reachable over a plain REST API. There’s also a hosted MCP server at https://mcp.abm.dev/mcp exposing 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 visitor-identification tool is reached primarily through its Slack and CRM destinations — excellent if your team wants names dropped where they already work, 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 sources are included in the price. That fits spiky, automated workloads — an agent that enriches a thousand accounts this week and none the next. We describe RB2B only in broadly-known terms — a free tier plus paid plans — so confirm its current features and pricing on RB2B’s own site.

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. See the pricing page (/#pricing) for the latest rate.

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 on RB2B’s own site.

When each one fits

Reach for RB2B ifyour question is “who landed on my site?” If you run inbound and want anonymous visitors named at the person level and dropped into Slack or your CRM for a fast follow-up, that’s the job it’s built for — and a different one from ours.

Reach for abm.dev ifyour “user” is an agent and your question is “tell me about the person or company I name.” If you’re building GTM automations that need data which can defend itself — every field sourced, scored, and explained, resolved live at call time — discoverable and callable without glue, and priced per call rather than per seat. Built for autonomous agent loops, not human dashboard-watching.

The two aren’t really rivals. RB2B finds the names that came to you; abm.dev researches the targets you choose to pursue. Plenty of teams will want both. What abm.dev was built for is the second half — the deep, cited research an agent can act on.

Looking for a RB2B alternative?

People comparing RB2B alternatives are often mixing two jobs: RB2B tells you who visited your site; abm.dev tells you everything defensible about the accounts and people you’re pursuing. If your gap is outbound-grade data rather than visitor identification, this is the right layer — and the two compose nicely.