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Bring us your hardest data problem
OUTCOMES

Automate what a machine can understand. Escalate what needs judgment. Learn from every decision.

The objective isn't to eliminate people. It's to reserve people for decisions worthy of human intelligence.

Bring us your hardest data problem
BUSINESS OUTCOMES

What changes when intelligence compounds.

Reduce risk.

See risk earlier, understand the impact, and act before it becomes loss.

Improve experience.

Anticipate what customers, suppliers, and employees need before they ask.

Increase efficiency.

Automate decisions and processes, not just tasks.

Create new business models.

Turn intelligence into products, services, and operating models that were previously impractical.

Adapt to what’s next.

Continuously reshape enterprise intelligence as the business, market, and technology change.
THE VALUE FRAMEWORK

Where the value comes from.

Evaluate Enterprise Digital Brain across four categories of value. Every initiative should show up in at least one of them.

01

Less effort.

Fewer manual reviews, mappings, reconciliations, duplicate investigations, and repeated decisions.
02

More speed.

Faster data readiness, onboarding, remediation, migrations, implementations, and business initiatives.
03

More intelligence.

Richer context, relationships, knowledge, and reusable enterprise understanding around every product, party, and process.
04

More opportunity.

New recommendations, risk signals, next-best actions, relationships, and business opportunities that become visible as the Brain learns.
EXAMPLE RESULTS

What that value can look like.

A few examples of the results to aim for. Every enterprise will find more.

Far less
Human effort on data work

The brain profiles, maps, enriches, and validates continuously, so your teams stop doing the data work and start directing it.

A small fraction
Records touched by a human

Most records flow through on their own. The few that reach a person are genuine exceptions, escalated with the evidence to decide fast.

Far more
Products findable in-channel

Enriched, complete, connected attributes put far more of your catalog in front of customers, in search, in channel, and in AI answers.

Dramatically faster
Raw supplier file to channel-ready assortment

Supplier files go from raw to governed and channel-ready in a fraction of the manual cycle, so new assortment sells sooner.

Much faster
Implementation data work

Profiling, mapping, normalization, and validation run in a fraction of the time, so programs go live sooner and with cleaner data.

Put Enterprise Digital Brain to the test and measure your outcomes.

A challenge runs the brain on a sample of your data. A pilot then measures it against your current process, with the baseline agreed before work starts: human review reduction, first-pass acceptance, and time-to-ready, plus domain metrics such as intelligence coverage and enrichment velocity for product data, and identity resolution for party data.

  1. 01
    Challenge
  2. 02
    Scoped pilot
  3. 03
    Readout
Pick a challenge →

Questions about value and proof

We do not promise a generic percentage before a baseline. The pilot measures your current effort and outcome, runs the brain on the same scope, and quantifies the change in review effort, cycle time, completeness, or acceptance. Any ROI figure is modeled from those measured inputs, with the assumptions shown.

First from reduced manual stewardship, faster onboarding, and faster time-to-ready. Then from what the data makes possible: better findability, attach and cross-sell, fewer wrong-fit parts, and faster migrations. Each is measured on your data before it is claimed.

No. Efficiency is the first benefit. The larger value is what understanding makes possible: surfacing opportunities that were out of reach, anticipating what is likely to happen next, and solving known problems faster, each as a recommendation your people decide on.

By connecting product, party, and business context that no single system holds. The brain surfaces likely opportunities, such as whitespace in an account or the whole job behind a single SKU, each as a recommendation with its evidence; people decide.

Estimates vary, so start with your own baseline. Beyond the direct cost, poor data now slows AI: more than 80% of organizations surveyed delay, limit, or alter AI and generative AI initiatives at least occasionally because of data-related risks (Accenture, May 2026). A scoped challenge measures time per record, review effort, and cycle time on your data before anyone quotes a return.

Set a baseline before anything changes: completeness, time per record, human review rate, first-pass acceptance, and time-to-ready. Then measure the same metrics on the same scope afterward. Cogentric labels every number by how it was produced, and customer results are published only with permission and scope.

Measure it. Learning shows up as repeat-case automation, declining human touches, decision reuse, and lower correction recurrence on the same scope over time. These are tracked in a pilot against a baseline, and Cogentric publishes no learning figure until one is measured.

Not yet published. Cogentric is selecting a small number of design partners for launch, and customer results are published only with permission and scope. Until then, bring us your hardest data problem and we will measure the result on your data.

Bring us your hardest data problem.

Send a sample of your own data, as it sits today, and see what the layer produces on it.

Bring us your hardest data problem