Product data you can stand behind.Every record, determined.
EKOM reconciles every product into one record, determines what is true against the evidence, and keeps it current everywhere it is read. Your team approves the calls that carry risk. EKOM does the rest.
Why now
The catalog is run by people. The systems reading it increasingly are not.
The teams who built these catalogs know their products better than any model will. What changed is the audience: procurement systems, retailer validators, and AI agents now read the catalog before a buyer does, and they decide whether your product gets listed, found, and bought.
We used to lose half the week figuring out which system was right before we could publish anything. My team got that time back, and now they spend it getting products live instead of chasing down data.
They clearly understood our taxonomy: the catalog, the attributes, the classification leaves. That was refreshing to see.
We onboarded a new marketplace in a week instead of a quarter. The records were already in the shape it wanted.
Evidence
Fragmented catalogs, resolved and built to each surface's standard.
Each of these teams ran product data across systems that disagreed. EKOM resolved every record, built it out to each destination's standard, and delivered it complete to every surface. What their teams did next was theirs to do.
Eight sources described the same products differently. EKOM resolved them into one record, built to every channel's spec.
With complete records finally reaching every portal, their team listed the full line on Grainger and Amazon Business in one quarter. The rollout had stalled for two years.
Three acquisitions left one catalog speaking in several voices. EKOM unified eleven systems and completed every record to the electrical trade's classification standard.
For the first time the catalog agreed with itself, so their sales team consolidated three price books into one and brought a national account live with no manual data pass.
At forty-five thousand products, machine readers reached only part of the catalog. EKOM resolved it and built the attributes marketplaces and search require.
On records they could finally trust, their merchandising team launched Walmart Marketplace and TikTok Shop, each fed in its own schema from one resolved record. They had held both back because the data was never ready.
Good products bounced at marketplace intake for missing attributes. EKOM built them to each marketplace's published spec, every value drawn from the context graph, not guessed.
Rejections at intake stopped, so their team opened two new marketplaces that quarter, each fed to its own spec from the same record.
How it operates
One loop, run continuously: reconcile, complete, distribute.
EKOM reads the catalog wherever it already lives, with no migration. Then it runs one continuous loop, and the team approves only the calls that need judgment.
Profiles every column, validates each record against its siblings, and recommends corrections inside your existing schema, with evidence and an audit trail.
| Field | Value | Signal |
|---|---|---|
| Amperage Rating | 5 A | conflict |
| Catalog Number | BUSSMANN_TCF15 | 15 A ✓ |
| ERP Description | 15A Circuit | 15 A ✓ |
| Product Title | 15A Breaker | 15 A ✓ |
Three fields agree on 15 A; one disagrees. EKOM resolves to the cited value. On an electrical part, a wrong rating is a safety issue, not just a data one.
Applies your taxonomy, units, and channel rules to every record. Packed values become structured attributes; missing fields are inferred from related products.
net_volume: 500 ml
pack: Case of 12
process: Cold-pressed
certification: Organic
One packed field becomes five structured ones, validated against the category standard. The catalog is now filterable by volume, pack, and certification.
Delivers each record in the shape every endpoint requires, then watches those endpoints. When a marketplace changes its rules, EKOM repositions the data under your governance.
LegMaterialEKOM caught the new required attribute, found the values already in the records, and mapped them. The catalog kept publishing, with no rebuild.
Normalize → Enrich → Distribute · Continuously · Under your governance
Across every sector
Wherever product data is detailed and the channels reading it keep changing, EKOM is built for that catalog.
EKOM resolves every field on the record, from the ones a buyer reads to the ones a regulator or a marketplace checks. The same resolution runs from a circuit breaker to a lipstick.
What EKOM is
Your systems store the catalog. EKOM resolves it.
Every product is one entity, described inconsistently across every system that touches it. EKOM reconciles those sources into a single accurate record and keeps it current as the catalog and its channels change.
A storage system keeps what it is given.
The resolution layer decides what is true, then generates from it.
Every value EKOM commits is evidenced. The foundation model is one interchangeable part of that work, never the source of truth.
What the engine produces
One product, resolved into a single record.
Many sources reconcile into one record at the core, cited where authority exists and inferred where it does not. The same resolved record feeds every Normalize, Enrich, and Distribute pass.
Tap or hover any node to see the source behind it
Under the hood
The rails a resolved record runs on.
Three mechanisms carry the work: a graph that reads each product in context, an evidence chain that ties every value to a source, and a reference library that encodes how a vertical behaves. Accuracy is what they produce. It is not a setting that gets switched on.
A record is evaluated against its category, sibling SKUs, vendor patterns, and the channel rules that govern its destinations, not as an isolated row. The unit of analysis is the catalog, not the cell.
Authoritative sources first, grounded inference for the rest. Each call carries a confidence score, proceeds under governance above your threshold, and reaches a person below it. Values trace to evidence, never to training data.
A continuously expanding map of taxonomies, endpoint behaviors, and normalization conventions, kept per vertical and separate from any customer's data. A new catalog arrives at a platform that already knows its domain.
Products rank where the buyer is looking, on your site, Google, and marketplace search.
Agents read structured data first. EKOM makes you visible to how buyers buy now.
Feeds pass validation on the first try. Listings get approved, not silently demoted.
Retailer and distributor onboarding clears without escalation. A new channel stops being a project.
The same SKU reads the same way on every surface. Consistency holds at scale.
Every correction carries provenance and every decision has a named approver.
Vertical fluency
Your data stays yours. EKOM already understands how your vertical behaves.
EKOM runs on each catalog independently. Your records, your schema, and your standards remain yours alone. What strengthens across the platform is the vertical knowledge: taxonomies, endpoint behaviors, and normalization patterns, never your data.
Per-account isolation. Your catalog is only ever yours.
Encryption in transit and at rest, role-based access, and a full audit trail on every action. Your data never touches another customer's environment, by architecture rather than policy.
From the first pass
Years of accumulated issues surface on the first pass.
The first pass surfaces problems that built up over years. After that, your standards are enforced on intake, so quality holds instead of drifting back between projects.
Cardinality, blank rate, sibling validation, vertical-typical mapping. Years of accumulated patterns surface at once, and EKOM does the reading, not the team.
Drift gets caught on intake, before it propagates. The team reviews exceptions instead of every record.
Routine work runs automatically inside your standards. Only the judgment calls, such as classification and manufacturer-spec conflicts, reach a person.
A marketplace adds a required field, a retailer tightens validation, EKOM catches it and repositions the data. A new channel goes live in a week, not a quarter. No services ticket. No rebuild.
Findings documented
against the source record.
Every finding is documented against the actual record, before you share a single field of your own. Four industries and four catalogs, with the same structural problems underneath.
What EKOM is built on
Principles, not promises.
The questions teams raise before they start, answered as the principles the system runs on.
EKOM determines what is true before it generates anything.
Generation alone has a structural flaw: a model can produce fluent, confident content with no way to know whether the underlying facts are correct. When the source data is wrong, it does not catch the error, it describes the error more convincingly and publishes it everywhere. EKOM reconciles the conflicting values first, determines which is correct, and records the evidence. Generation follows, because by then there is something true to generate from.
On how EKOM differs from generative AI
EKOM sits above your stack, it does not replace it.
EKOM reads the data in the systems you already run and writes the resolved record back to them and to the channels that consume it. There is no migration and no re-platforming. Your PIM, ERP, and feeds stay in place; EKOM reconciles what they hold.
On replacing existing systems
Your data is never used to train models that serve other customers.
EKOM operates on each customer's catalog independently. Your records, schema, and standards are yours alone. What improves across the platform is general vertical knowledge such as taxonomies and normalization patterns, not your data. Encrypted in transit and at rest, role-based access, full audit trail on every action.
On data handling and isolation
Your team stays in control of every decision that carries risk.
You define the confidence thresholds. EKOM applies corrections automatically when they clear those thresholds and routes lower-confidence or higher-risk decisions to a designated reviewer. Every change is recorded with its evidence, so each decision is traceable and reversible.
On automation and governance
The model is one interchangeable component, you are not tied to it.
The foundation model supplies inference. The identity resolution, evidence chain, taxonomy, and distribution that produce an accurate record are EKOM's own work and do not depend on any specific model. It can be replaced as better ones emerge, without changing how the system behaves.
On which AI model is used
The first pass is useful immediately, and quality holds after it.
The first pass identifies existing errors and inconsistencies, documented against your own records. Your standards are then enforced at intake, so quality is maintained rather than re-eroding between projects. Teams have brought a new channel live in a week rather than a quarter once records met that channel's requirements.
On time to results
The same resolution works across every vertical, B2B and consumer.
The process applies whether the critical attribute is an electrical rating or an ingredient list, across industrial distribution, electrical supply, beauty, grocery, footwear, and home. The only requirement is that your products are read by marketplaces, search, agents, or trade partners.
On industry and product-type fit
Have a question specific to your catalog? Request a scoped analysis and we will answer it against your own data.
Put your team back on the work only they can do.
Share a representative slice. You get back a clear picture of where your product data carries error today, what it is costing across your channels, and how much of it EKOM resolves on the first pass. Documented against your own records, with the evidence behind every finding.
The analysis comes first. No commitment, and no services engagement required.