Skip to content
Bring us your hardest data problem
SOLUTIONS · PRODUCT DATA & COMMERCE

Sound familiar?“Our product data can’t keep pace with what the business needs.”

Go beyond the product record. Understand what the product can do.

Connect attributes, specifications, relationships, applications, compatibility, transactions, documents, supplier knowledge, human expertise, and business context. Enterprise Digital Brain builds a continuously evolving understanding of every product, then gives agents the intelligence to improve the data, solve complex product problems, anticipate what is needed next, and uncover opportunities the business could not previously see.

Bring us your hardest product-data problem See how product intelligence works
IS THIS YOU?

Knowing the SKU is only the beginning.

Pick a problem to read more.

01 · No system understands the product

“Our product data is everywhere, but no system truly understands the product.”

What it looks like

Product information is spread across ERP, PIM, supplier files, commerce, documents, engineering systems, and people. Every system holds part of the truth.

Why it keeps happening

Traditional platforms organize records. They do not automatically understand how specifications, applications, compatibility, relationships, documents, and business context fit together.

What the Brain does

Builds a living understanding of the product across systems, knowledge, relationships, and external sources, so agents can reason over what the product is, what it does, what works with it, and why it matters.

02 · Rebuilding the same data for every channel

“We keep harmonizing and enriching the same data for every system, supplier, and channel.”

What it looks like

Every supplier sends something different. Every system wants a different structure. Every marketplace and channel requires another version of the data.

Why it keeps happening

Mappings, classifications, normalization rules, and enrichment decisions are repeatedly rebuilt by teams or buried in one-off transformations.

What the Brain does

Agentifies harmonization and enrichment. Enterprise Digital Brain learns the mappings, standards, classifications, relationships, and decisions once, then continuously applies and improves them across sources and destinations.

Stop transforming the same product data over and over. Build intelligence that remembers how.

03 · The SKU, not the solution

“Our catalog knows the SKU. It doesn’t understand the solution.”

What it looks like

The system can return the product someone searched for, but it cannot understand the job, application, environment, constraints, or everything needed to solve the problem.

Why it keeps happening

Product records describe individual items. Business outcomes often depend on relationships between products, applications, compatibility, substitutions, accessories, and customer intent.

What the Brain does

Enterprise Digital Brain reasons across the whole product ecosystem to recommend complete solutions, compatible components, substitutes, accessories, and the next logical need.

From selling products to solving the whole job.

04 · AI can’t tell what to recommend

“Our AI has access to the catalog. It still doesn’t know what to recommend.”

What it looks like

Copilots, shopping agents, sales assistants, and procurement agents can retrieve product records but struggle with fit, suitability, compatibility, substitutions, or why one product is better for a specific situation.

Why it keeps happening

Access to product data is not product understanding. Agents need context, relationships, rules, evidence, and business meaning.

What the Brain does

Enterprise Digital Brain gives agents trusted product intelligence (attributes, compatibility, relationships, applications, provenance, business rules, and context) so they can reason and act with confidence.

Don’t just connect AI to your catalog. Teach it how your products work.

05 · Hidden portfolio opportunity

“We have more opportunity hidden in our product portfolio than we can see.”

What it looks like

The enterprise knows what it sells, but cannot easily see hidden assortment gaps, attach opportunities, substitutes, solution bundles, underserved use cases, or new channel opportunities.

Why it keeps happening

Traditional analytics answer questions someone already thought to ask. The relationships between products, demand, applications, channels, customers, and market context are too complex to inspect manually.

What the Brain does

Agents continuously reason across the product portfolio to discover opportunities nobody explicitly asked for: new bundles, missing products, cross-sell paths, assortment gaps, substitution strategies, and emerging demand.

The biggest product opportunity may be the one nobody knew to query.

06 · Starting over every initiative

“Every new initiative forces us to rebuild product understanding.”

What it looks like

A new PIM, ERP, marketplace, acquisition, AI initiative, geography, or commerce experience triggers months of profiling, mapping, classification, enrichment, and business-rule discovery.

Why it keeps happening

The intelligence created during one transformation usually stays trapped in that project, platform, spreadsheet, or consulting team.

What the Brain does

Enterprise Digital Brain retains enterprise product understanding (relationships, mappings, rules, exceptions, taxonomy, provenance, and decisions) so the next initiative starts with what the enterprise already knows.

Platforms change. Product understanding shouldn’t start over.

07 · Built for today’s channels only

“We’re designing product data for today’s channels, not tomorrow’s.”

What it looks like

The schema works for current commerce, but AI agents, new marketplaces, regulations, regions, and business models keep introducing new information requirements.

What the Brain does

Enterprise Digital Brain identifies the data, relationships, attributes, and context the enterprise will need next, then helps evolve the product model continuously.

Don’t just maintain today’s product model. Continuously evolve it for what comes next.

From product records to product intelligence

Build understanding once. Put it to work everywhere.

  1. 01 · Understand

    Creates a living model of the product: attributes, specifications, documents, applications, relationships, suppliers, compatibility, rules, and expertise.

    Agents understand more than fields in a record.

  2. 02 · Improve

    Continuously harmonizes, classifies, normalizes, enriches, relates, validates, and governs product information.

    Product data continuously gets better instead of waiting for another cleanup project.

  3. 03 · Orchestrate

    Determines what data, structure, relationships, and representation each system, channel, workflow, or agent requires.

    One trusted product understanding can support many destinations.

  4. 04 · Solve & discover

    Reasons across the product ecosystem to recommend, substitute, anticipate, optimize, detect, and uncover opportunity.

    Product intelligence becomes business action.

Every classification, mapping, correction, relationship, exception, and decision becomes reusable product intelligence.

One product understanding. Multiple optimized views.

There may not be one perfect product record.

Different systems legitimately need different representations of the same product. Enterprise Digital Brain maintains one trusted product understanding, then continuously creates the right view for each system, channel, process, and agent.

Product intelligence
One trusted understanding
Shared identityShared provenanceShared confidence

The goal is not one product record everywhere. It is one trusted understanding, expressed correctly everywhere.

Product data + relationships + business context

Product data tells you what it is. Relationships tell you what it can do.

Product data
Cordless drill18V1/2-inch chuck2,000 RPMBrushless
Relationships & knowledge
Compatible batteriesConcrete applicationsRequired bitsAccessoriesSubstitutesSafety requirementsExpert knowledge
Business context
Already owns a battery ecosystemInstalling anchors in concreteLocal inventoryPreferred supplierJob deadline
Enterprise Digital Brain outputIllustrative

Recommend the right tool, compatible accessories, required consumables, and available alternatives based on the actual job and context, not just the catalog search term.

Right toolCompatible accessoriesRequired consumablesAlternatives

Search finds the product. Intelligence understands the job.

Agentify the product-data operating model

Stop maintaining product data by hand. Give the work to agents that understand it.

Enterprise Digital Brain agents can continuously:

  • Profile incoming product information
  • Discover attributes and structures
  • Infer taxonomy and classification
  • Map supplier terminology into enterprise standards
  • Normalize units and values
  • Identify missing information
  • Research approved sources for enrichment
  • Discover relationships and compatibility
  • Validate recommendations against evidence
  • Publish the correct representation to each destination
  • Route uncertainty to people
  • Learn from corrections and approvals

Automate what the Brain understands. Escalate what needs judgment. Learn from every decision.

Move beyond enrichment

Do not just complete the record. Discover what the business should know next.

Conventional enrichment asks
What fields are missing?
Enterprise Digital Brain also asks
What intelligence is missing?
Missing attributes

What specifications should exist but do not?

Missing relationships

Which accessories, substitutes, components, or products should be connected?

Missing applications

What jobs, environments, industries, or use cases is this product appropriate for?

Missing compatibility

What works with what, and what definitely does not?

Missing knowledge

What do experts know about this product that is not represented in the systems?

Missing future requirements

What will AI agents, new marketplaces, regulations, or channels require next?

Enterprise Digital Brain does not just populate today’s schema. It helps determine what tomorrow’s product intelligence should contain.

What the Brain can uncover

The biggest product opportunity may be the one nobody knew to query.

Hidden assortment opportunity

What products are customers trying to solve for that the portfolio does not adequately serve?

Whole-solution opportunity

What products, accessories, services, and consumables naturally belong together?

Substitute opportunity

When something is unavailable or obsolete, what is the best alternative given the application, not just matching attributes?

Cross-sell / attach opportunity

Which products should logically be sold together but are not?

Channel opportunity

Where could products perform in a channel, marketplace, geography, or AI interface if the right intelligence existed?

Product lifecycle opportunity

Which products are heading toward service, replacement, obsolescence, or migration events?

Supplier optimization

Which product and supplier combinations create better availability, compatibility, risk, or economics?

Portfolio rationalization

Where are redundant SKUs, unnecessary variants, missing categories, and unexplained complexity?

Product + party intelligence

Know the product. Know the party. Understand the opportunity.

Distributor

Recommend the correct replacement component based on installed equipment, contract pricing, inventory, and job requirements.

Manufacturer

Predict service needs using product lifecycle, installed base, and customer history.

Retailer

Recommend based on suitability for this customer rather than “people also bought.”

Supplier

Identify product and customer combinations exposed to a component disruption.

Product tells you what something is. Party tells you who it matters to. Together, they tell you what to do next.

Built for AI and agentic commerce

Do not just expose your catalog to AI. Give AI product understanding.

Attributes
What is it?
Relationships
What works with it?
Applications
What is it for?
Compatibility
Will it actually work?
Substitutions
What else could solve the need?
Business context
What is appropriate here?
Evidence & provenance
Why should the agent trust the answer?

Enterprise Digital Brain makes product intelligence agent-ready, not merely API-accessible.

Adapt to what’s next

Your product model should not become obsolete every time the market changes.

Enterprise Digital Brain can identify and help create:

New attributes the business should capture
New relationships the model should represent
New taxonomy or ontology requirements
New channel requirements
Emerging customer terminology
New regulatory information
New AI-agent context
Changing compatibility or lifecycle relationships

Do not design product intelligence only for the channels you have today. Build a Brain that adapts to whatever comes next.

Traditional PIM vs product intelligence

PIM is necessary. It is not the whole answer.

Traditional PIMEnterprise Digital Brain product intelligence
Manages product recordsUnderstands products
Stores attributesDiscovers what intelligence should exist
Maintains taxonomyBuilds and evolves meaning and relationships
Publishes to channelsOrchestrates the right intelligence to each channel
Enrichment workflowsAgentic continuous improvement
Searchable product contentIntent-aware reasoning
Related productsCompatibility, solution, and relationship intelligence
Supports commerceSupports people, AI, and agents
Data completenessBusiness understanding
Starts again with the next projectIntelligence compounds

Enterprise Digital Brain does not make PIM obsolete. It makes the intelligence around the PIM dramatically more capable.

Trust

Every product decision should show why it deserves to be trusted.

Evidence

What supports the conclusion?

Provenance

Where did the information come from?

Confidence

How certain is the Brain?

Policy

What is the agent allowed to do?

Human review

When should someone decide?

Decision history

What happened before?

Enterprise Digital Brain does not just recommend the product. It shows why.

Core solution starting points

Start where the value is clearest.

01

Product data harmonization & enrichment

Continuously standardize, enrich, classify, and relate fragmented product data.

02

Product intelligence

Build context, applications, compatibility, substitutes, and relationships around products.

03

Whole-solution selling

Understand the customer’s job and recommend everything required to complete it.

04

AI & agent readiness

Give AI enough context and evidence to recommend or choose products reliably.

05

Supplier product onboarding

Agentify incoming supplier data from raw files through trusted product records.

06

Channel & marketplace readiness

Determine and create the product intelligence required for each destination.

07

Product portfolio discovery

Find assortment gaps, hidden relationships, redundancies, cross-sell, and product opportunity.

08

Lifecycle intelligence

Anticipate service, replenishment, substitution, obsolescence, and migration needs.

09

PIM modernization

Make the existing PIM dramatically smarter without forcing replacement.

10

Product data transformation

Carry accumulated product understanding through migrations, acquisitions, and replatforming.

Challenge

Bring us the product-data problem your systems cannot solve.

Give Enterprise Digital Brain a sample of your product records, supplier files, specifications, documents, or catalog. We will show what agents can understand, harmonize, enrich, connect, reason over, improve, and uncover, with the evidence behind every conclusion.

Possible entry offers
Data Potential ChallengeSupplier File ChallengeAI-Ready Product ChallengeFindability ChallengeProduct Intelligence Audit
Bring us your hardest product-data problemSee all challenges

Questions about product data and commerce

Product intelligence is what a business knows about a product beyond its record: what it is for, what it fits, what it works with, what can replace it, and which documents explain it. Search, commerce platforms, and AI agents increasingly need that context to answer a buyer. Enterprise Digital Brain builds it from your product data, supplier files, and documents, with the evidence behind each value.

Usually because the product data does not describe what the customer is trying to do. Search depends on attributes, relationships, applications, and synonyms; when those are missing, the right product exists but cannot be found. 56% of benchmarked ecommerce sites fail to adequately support users' search needs; 44% have issues with compatibility searches (Baymard Institute, Apr 2026).

It is a data problem before it is a search problem. Keyword, attribute, and intent searches each depend on richer product data: an attribute search fails when the attribute is missing, and an intent search fails when the record does not say what job the product does. 34% → 96% of products findable in-channel in one distributor category (Example workflow · Illustrative of mechanism, not a customer result).

Model the product once with business meaning, then check what each channel needs against it continuously. 54% of shoppers abandoned a purchase because product content was inconsistent across channels (consumer research, Jan 2025). Enterprise Digital Brain is designed to determine what each channel requires and flag the gaps before publishing.

Whole-job selling understands the customer's project and recommends what the job needs, not only the SKU they searched for: a customer buying one fitting can be shown the tools, parts, and accessories the job requires. It is one example use under Product data and commerce.

Start by measuring what the new channel needs that your data does not yet carry. New-channel readiness, an example solution, determines the additional product intelligence a channel requires and closes the gaps with evidence. The Commerce data readiness challenge runs it on one of your categories.

Missing or inconsistent content sends buyers elsewhere. 83% of online shoppers are likely to abandon a site or product page that does not give them the information they want (consumer survey, Aug 2026). Complete, consistent product data across channels is the first thing to measure.

Substitution intelligence, an example solution, recommends an alternative when a product is unavailable, based on specifications, compatibility, and application rather than category alone, with the evidence shown. It is offered as a challenge on your own catalog.

Listings that do not match the product are one reason buyers give. 54% of shoppers abandoned a purchase because product content was inconsistent across channels; 71% returned an item that did not match the online listing (consumer research, Jan 2025). Accurate, complete product content is one lever to measure; it is not the only cause of returns.

Treat each supplier file as a reasoning problem, not a template to enforce. Enterprise Digital Brain is designed to read the file as it arrives, work out what the columns mean, map and normalize values to your taxonomy, fill gaps from approved sources, and route uncertain cases to review. The Supplier file challenge measures time-to-ready on one of your real files.

Potentially. Instead of requiring every supplier to match a perfect template, the brain is designed to infer structure and fill gaps from approved sources, with evidence and confidence on each value. How far that goes depends on your rules and the risk of each attribute.

Enrichment adds the attributes, relationships, and context a product record is missing so it can be found, compared, and sold. In Enterprise Digital Brain, every enriched value carries its source and confidence, and inferred values are flagged for review under your policy.

Cognitive data operationsWe don’t know if our data is ready for what comes next. Heal and amplify PIM and MDMMake the platforms you already own smarter.

Bring us your hardest data problem.

Bring one category that should sell better than it does. Send it as it sits today, and we will show you what the brain does with it.

Bring us your hardest data problem