EKOM is the resolution layer for product data.
A catalog is any structured set of commercial entities — products, brands, variants, listings: anything a buyer or an algorithm needs to evaluate. EKOM ingests, structures, enriches, and distributes catalogs at scale, so every downstream surface that depends on them can operate cleanly. It reconciles each product into one record, determines what is true against the evidence, and keeps it current everywhere it is read. The platform runs continuously and bi-directionally, against catalogs of millions of records, in production today.
The architecture treats foundation models as orchestrated components rather than as the product itself. EKOM's proprietary work sits in the layers around the model: the taxonomy that defines what each record means, the validation that ensures every output is correct, the brand-voice encoding that makes content sound right, and the channel-shape distribution that delivers data to each endpoint in the format it requires. The models contribute inference. EKOM contributes everything that makes the inference usable.
There are categories of work that look superficially like neighboring tools and turn out to be a different layer of the stack. The pattern is recognizable.
Stripe
~2010 · payments
Looked like another payment processor. Became the orchestration layer that absorbed fraud, disputes, multi-rail routing, and compliance, and made every consumer internet product capable of accepting money. Companies built on Stripe, not next to it.
Plaid
~2015 · fintech
Looked like a developer API for banking data. Became the connective graph for the entire consumer fintech category. Every neobank, robo-advisor, and challenger card runs on Plaid. The graph it built across institutions was the asset.
EKOM occupies that position for catalog data. The platform sits below the marketplaces, retail surfaces, brand systems, and discovery engines that operate on top of it. The structured graph it builds, refines, and distributes is what those surfaces actually read.
◉ What this means in practice
The product record is the atomic unit of a catalog graph. The same infrastructure that already connects brands, products, variants, audiences, and channels can be pointed at a new catalog on day one. Most companies get a working substrate immediately — the catalog is the starting point, not an engineering project.
What sits under each of the four layers.
Each layer is composed of engineering primitives built and refined against production traffic across multiple commerce verticals. The plain-language summary appears first. The technical detail follows.
Take messy data from many sources. Turn it into one clean, consistent record.
Source data arrives fragmented. Different schemas, different conventions, different completeness. The normalization layer ingests it, classifies it against EKOM's proprietary catalog taxonomy, and produces a unified record. The taxonomy itself has been built across global brand and retailer engagements. It encodes domain knowledge specific to how each vertical actually operates.
Multi-source ingestion. Feeds, APIs, files, unstructured docs.
Schema reconciliation. Proprietary taxonomy, vertical-aware.
Identity resolution. Entity matching across noisy inputs.
Quality profiling. Gap detection, completeness scoring.
Fill in what's missing. Get it right. Make it sound like the brand. Prove every claim.
Enrichment fills the gaps the normalization layer surfaces, to specification, in voice, with evidence. This is where foundation models do the most work, and where EKOM's orchestration is most proprietary. Multiple frontier-lab models are routed against the task at hand, each selected for what it does best, with EKOM's validation layer enforcing the schema, the voice, and the evidence requirements. Nothing is guessed.
Multi-model routing. Best-of-breed selection per task.
Evidence-chain reasoning. Every claim traceable to source.
Brand-voice encoding. Captured, not prompted.
Spec validation. Attribute-level conformance.
Deliver clean data to every endpoint, in the format each one needs, continuously.
Clean enriched records have to land in the shape each downstream endpoint requires. Marketplaces want their format. Retailers want theirs. AI assistants want a third. The distribution layer adapts one source of truth to every endpoint specification, bi-directionally and continuously, so the commercial operation stays in sync.
Channel-shape mapping. Per-endpoint specifications.
Bi-directional sync. Outbound and inbound.
Continuous refresh. Not scheduled batches.
Delivery telemetry. Acknowledgement and reconciliation.
Watch everything. Learn what works. Get sharper with every record.
The connecting layer reads across the other three. It surfaces what's drifting, what's converting, what's improving. This is where the system's learned judgment lives, and where every record processed makes the next record's enrichment sharper. The continuous-learning loop is the layer's core function.
Quality drift detection. Knows what wrong looks like.
Outcome linkage. Enrichment tied to downstream lift.
Edge-case library. Every record teaches the system.
Continuous feedback loop. Back to L01 through L03 automatically.
◉ What this means in practice
These are not capabilities EKOM would build for a new customer. They are capabilities EKOM already operates daily, against catalogs of millions of records, for global brands and retailers. Pointing them at a new catalog is configuration, not construction.
What continuous production operation has built.
The architecture has remained stable. What has accumulated alongside it is encoded judgment from production traffic across multiple commerce verticals. Each item below is part of the platform's working operation today. Each one transfers directly to the work a brand or retailer's catalog demands.
A structured vocabulary built across consumer goods, hardgoods, fashion, electronics, beauty, food, and household categories. Every vertical has its own grammar for what attributes matter, how they relate, and what good data looks like. When the system meets a new catalog in one of those categories, it already understands the vocabulary.
Every messy record EKOM has processed has taught the system something specific. A way a SKU can break. A way a brand can talk. A way a category can contradict itself. Those lessons are encoded as rules, heuristics, and validation steps that run as part of every pass. Every catalog has edge cases. The system that handles them already exists.
Different brands speak differently, and brand voice is the easiest place for AI-generated content to fail noticeably. EKOM has built per-customer voice models that capture register, vocabulary, sentence rhythm, and prohibited language. The same encoding carries to any new catalog — the voice models already understand how brands talk about themselves, which is the language every product description and channel has to preserve.
The system has learned what good output looks like for a specific customer in a specific vertical, and detects when that quality drifts before the customer notices. This is a learned signal, not a rule. The same machinery flags when a record goes stale, when match confidence falls, when a feed starts to degrade.
Every record processed makes the next record sharper. The taxonomy becomes denser. The voice models become more refined. The edge-case library grows. The resolution layer's signal-to-noise improves. This is the dynamic that has run since EKOM went into production, applied to every catalog it touches from day one.
◉ What this means in practice
A new customer is not waiting for EKOM to learn the domain. The system arrives understanding how brands work, how categories are structured, how voice is preserved, and how quality is measured. Adapting to a specific catalog begins with that knowledge already in place.
Where the foundation model sits in the stack.
The architecture treats foundation models as interchangeable components within the four-layer system. When a better model is released, EKOM benefits from the improvement without rework. When prices drop, the cost structure improves. The proprietary work happens in the layers above and below the model, where the data, the validation, and the distribution live.
What the brand, retailer, or commercial system actually reads. Schema-conformant, voice-correct, evidence-traced, channel-shaped. The marketplace, the retail surface, the brand system, and the discovery and index layers the catalog feeds.
Cross-layer pattern detection, quality drift monitoring, outcome linkage. The learned judgment that decides what good looks like.
Channel-shape adaptation per endpoint. Bi-directional sync, continuous refresh, delivery telemetry. The plumbing that delivers data in the shape it has to be in.
Multi-model orchestration, evidence-chain reasoning, brand-voice encoding, spec validation. This is where foundation models do work, inside EKOM's orchestration.
Multiple frontier-lab models, routed per task and continuously evaluated. The interchangeable component. When a better model ships, EKOM gets sharper. When prices drop, the cost structure improves.
Proprietary taxonomy, identity resolution, quality profiling. The grammar of every catalog the platform touches, built once and reused everywhere.
As foundation models improve, the layers around the model do more work, not less. The architecture is designed to absorb model progress as a tailwind.
In summary
The model is a component. The infrastructure is the system.
EKOM has spent years building the orchestration, judgment, and distribution layers that turn frontier-lab models into a reliable commercial system. The result is infrastructure. Taxonomy, voice models, evidence chains, drift detection, channel-shape adaptation. It operates regardless of which underlying model runs beneath it.
In practice this matters three ways. The architecture is in production today. Adapting it to a new catalog is methodology transfer, not a new build. The learned judgment continues to compound. Every record enriched contributes to how the next one is processed. The foundation-model layer is upgradeable. As the underlying models improve, the operating substrate gets sharper without rework.
EKOM operates the system that makes AI usable for commerce — for every catalog a brand or retailer needs to keep true.
Jon Ricketts
Founder, CEO
Jonah Santo
Chief Commercial Officer