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B2B data infrastructure

The data layer your go-to-market stack is missing, not duplicating.

Verified company and contact data, continuously re-checked and delivered to your own storage. Feeds, clean-and-enrich APIs, and search — all sitting on one dataset, so records resolve instead of contradicting each other.

Talk to usSee the data layer
  • Flat-rate on core data
  • Per-outcome enrichment
  • Provenance on every field

Illustrative record

every field carries its own source

Company
Northwind SystemsPrimary registry
Employee band
201–5002 sources agree
Role
VP Revenue OperationsRe-verified 3 days ago
Work email
Verified · role-matchedAcceptance checked

Source, timestamp, and confidence on every field. A wrong value is a question you can answer.

Three layers, one dataset

Everything sits on the same records, so nothing contradicts anything else

Take all three or start with one. Identifiers resolve across every layer, so a company found in search is the same company you enrich and the same one in your feed.

Data feeds
Continuous company and contact data

Continuously verified company and contact data, delivered to your storage. Flat-rate and unmetered, so the same volume costs the same as a tenth of it.

  • Records re-verified on a rolling basis rather than sold once and left to decay
  • Delivered to customer storage, so it joins the systems you already run
  • Unmetered core data — no per-record billing as volume grows
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Clean & enrich
Repair what you already have

Fix the data you already have before buying more. Canonical matching against golden records, stale-record repair, and email verification — charged per outcome, not per query.

  • Canonical ID matching resolves your records against a single identity graph
  • Stale job titles and company details corrected rather than left to rot
  • Per-outcome pricing: pay for records fixed, not for searches that found nothing
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Discover & search
Find accounts by describing them

Find companies and people by describing what you need, in plain language, instead of assembling boolean queries and guessing at category codes.

  • Natural-language search over companies and the people at them
  • Semantic retrieval that understands intent, not just keywords
  • Result sets you can feed straight into an outbound program or a product
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How a record is made

Where a field came from, and when it was last checked

  1. 01

    Collected from primary sources

    Signals are taken from the places change is recorded first, rather than repurchased from upstream wholesalers.

  2. 02

    Verified across independent sources

    Each field is confirmed against more than one signal, so a single bad source does not become a record you send to.

  3. 03

    Conflicts resolved and the resolution kept

    Where sources disagree, the winner is decided programmatically and the decision is stored alongside the field.

  4. 04

    Delivered with provenance attached

    Every field carries its source, its timestamp, and a confidence score, so any value can be traced or challenged.

Why it holds up

Four reasons this behaves differently in production

Each of these is a property of the system, not a claim about it. Any one of them can be checked on your own records before you commit to anything wider.

One platform, not stitched vendors

Most stacks glue a feed provider to a verification service to a search tool, and pay an integration tax in both money and engineering time. Here the same dataset sits under all three, so records resolve across layers instead of contradicting each other.

Provenance on every field

Source, timestamp, and a confidence score travel with each piece of data. Any field can be traced back to where it came from and when it was last checked — so a bad record is a question you can answer, not a mystery.

Current by default

Re-verification runs on a rolling basis. Job changes, invalid addresses, and company updates are corrected as the cycle reaches them, and records that are still accurate get a fresh timestamp rather than being resold as new.

Primary sources, not resellers

Data is collected from primary signals and verified across independent sources, rather than repurchased from the same upstream wholesalers. Conflicts are resolved programmatically and the resolution is recorded.

Who it is for

Built for the teams who have to answer for the data

The same foundation serves three very different budgets. What changes is which layer you start with.

GTM products

Embed verified company and contact data, enrichment, and verification without building and operating the pipeline yourself. Ship sooner and keep the margin.

Discuss this

GTM teams

Keep the CRM honest. Stale records corrected, email addresses checked for validity before the record is used, job changes noticed while they still matter.

Discuss this

Demand-gen agencies

Run high-volume programs on data that is current on day one, without per-record costs that punish the volume your clients are paying for.

Discuss this

Before you ask

The questions that come up on the first call

Answered here so the conversation can start at the part that matters — your data, not our pitch.

Most teams buy a feed, bolt on a verification service, and add a search tool, then maintain the seams between all three. Morphotech Data runs one dataset underneath all three, so a record that appears in the feed resolves to the same identity in enrichment and in search. You are buying the resolution, not just the rows.

Bring a slice of your own data and we will show you where it breaks

Start with the datasets you already run on. We will resolve them against the same identities our feeds use and show you what changes before anything wider is committed.

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