Foundation models keep getting more capable every quarter, and more interchangeable. They are becoming infrastructure. The bottleneck is no longer the model. It is the records the model reads from, which live in many systems and rarely agree.
AGYL is the operating layer above that infrastructure. It reads across your systems, decides what is true about every record that matters, holds the evidence behind every value, and hands AI-ready records to whatever reads them next — a copilot, an agent, a workflow, a person. Your standards govern it. Your team approves the calls that carry risk. Everything built on top belongs to you.
Every enterprise software category has incumbents that were designed for the analytics era and are now retrofitting AI on top. AGYL was built for the AI era from the start. Different architecture, different operating model, different posture toward ownership. Three questions cover what matters before the rest of the page.
AGYL is software. You license it. Your team operates it. Onboarding is included and finite — a structured engagement that gets the system into production with your records and your standards encoded. After that, the software runs continuously in your tenant. There is no ongoing services billing. The IP is the platform, the source connectors, the vertical libraries built from prior implementations, and the methodology proven by EKOM in production.
You do, by architecture. Your records, standards, taxonomies, and approved decisions stay inside your tenant. They are never used to train models that serve another customer. What strengthens across the platform is general domain knowledge, the kind any vertical develops, never anything specific to your business. If you leave, your operating layer leaves with you.
Because every AI initiative inside your business is now bottlenecked at the same layer. Foundation models keep getting more capable and more interchangeable. The advantage is moving above them. Before any agent can act on your behalf, before any copilot can answer reliably, before any retrieval system can return the truth, something has to read across your systems, decide what is true, hold the evidence, and keep it current. AGYL is that layer. The companies that build it now will compound a decade of advantage on top of it.
When AGYL goes live in your tenant, three things sit inside it. The platform that does the work. The context graph that encodes how your business decides things. And the IP that gets it running — connectors, vertical libraries, the methodology proven in production. The longer it all runs, the sharper your AI gets.
The architecture that determines what is true, keeps the evidence, and holds it current across every record your business runs on.
Runs above your existing stack. Replaces nothing. The model layer underneath is a slot, not a dependency.
The structured map of how your business decides things. Your taxonomies, your authorities, your thresholds, your team's approved judgments. Built once, lives in your tenant.
The longer it runs, the more your operation runs itself on your terms. Every approval enters the graph and is reapplied to the next record like it.
Source connectors to the modern stack. Vertical libraries built from prior implementations. The configuration framework that turns your standards into governing logic. The deployment methodology proven by EKOM in production.
Onboarding is structured and finite. You are not buying a services engagement. You are buying software, packaged with the IP that gets it running on your records.
Foundation models keep getting more capable, and increasingly interchangeable. Whichever model leads this quarter, another catches up next. The companies winning with AI did not win by picking the right model. They built a layer above the model where their records became reliable enough for the model to act on. That is the operating layer. That is what AGYL is.
A model can write fluently with no idea whether the words it produces are correct. Before an agent acts on your behalf, before a copilot answers a question, before a retrieval system returns a result, something has to read across the systems that disagree, decide what is true, hold the evidence, and keep it current. The model layer is becoming infrastructure. The operating layer above it is where each business compounds its own records, standards, and judgments into something the model can actually act on.
Once the operating layer is running, the things your AI initiatives have been stalling on stop being problems. Agents act on the right record. Copilots answer with the right facts. Retrieval systems return the right document. The model finally has something reliable to read from.
An autonomous agent quoting a customer, dispatching a technician, or routing a contract for renewal is only as accurate as the record it reads. AGYL hands the agent a single resolved record with the evidence behind every value. The agent stops second-guessing what is true.
When a sales rep asks "what is our true ARR with this account," or a CFO asks "what is our exposure to this supplier," the copilot can only answer well if the records behind the answer are reliable. AGYL is the layer that makes that answer trustworthy enough to act on.
Whether the architecture is RAG, agentic retrieval, or MCP-style context, the answer depends on the records underneath. AGYL resolves them first, so retrieval and context-engineering systems read from one canonical version of each entity instead of three conflicting ones. Hallucinations from disagreeing source data stop happening.
The architecture EKOM proved on product data, abstracted to any record a business has to get right. AGYL runs above your existing stack, replaces nothing in it, and turns the records that come out the other side into something an AI agent or copilot can act on without hallucinating.
Reconcile the systems that disagree. Resolve to the value the evidence supports, with confidence scored on every call.
Every resolved value traces to where it came from. Below threshold, a person decides. That judgment enters the graph and is reapplied to the next record like it.
The resolved record returns to every system that reads it, in each endpoint's required shape. Endpoints change; AGYL repositions without rebuilding.
The context graph is not just a place where resolved records live. It is the structured memory of how your business decides things. Every approval your team enters, every standard you encode, every judgment about what good looks like — all of it accumulates into a single, governed asset that lives inside your tenant.
The first record is the most expensive. By the third, the graph already understands enough about your business that the second and third resolve faster, with less human review. By month 12, the graph holds enough of your operating logic that it understands the business better than any single person on your team. The asset compounds from there, year on year.
Six places where the operating layer makes new things possible, drawn from where customers have started. The list is not exhaustive. Most operating functions inside a serious business have records that span systems, that drift, and that AI agents and copilots will quietly stumble over until something resolves them. AGYL resolves each of these inside the same architecture, with the same evidence chain and the same governance. Once they're live, the records talk to each other and the AI built on top of them stops being unreliable.
In a $200M business, "Halsey, Inc." shows up as Halsey, Inc. in CRM, HALSEY, INC. in billing, and Halsey Industrial on a senior rep's private spreadsheet. Three reps quote three different prices on the same call week. Renewals miss because the contract end date lives in one system and the rep works from another.
AGYL resolves the customer master once. Every account becomes one record with the active contract, true ARR, open opportunities, support history, and renewal date attached. Deployed back into the CRM the reps already use.
Duplicate vendor records inflate the master file. The same supplier shows up under three legal entities, two divisions, and one misspelling. Spend is fragmented, leverage is invisible, and certifications quietly expire because nobody owns the consolidated view.
AGYL resolves the supplier master against authoritative sources. Legal entity registries, tax IDs, certification bodies, your own approval history. Every duplicate collapses. Every certification carries its expiration. True spend by parent entity surfaces ahead of every negotiation.
Contracts live in PDFs, in counsel's drawers, in a DMS, in the original counterparty's outbox. The obligations they create, MFN clauses, renewal windows, indemnity caps, audit rights, are invisible until they bite. CFOs find out about a $400K auto-renewal the day it processes.
AGYL resolves the contract layer. Every active agreement becomes a record with its key obligations extracted, cited to the exact clause and page, scored on confidence, and surfaced on a timeline. Legal validates the high-stakes calls. Finance sees the calendar of obligations for the first time.
It happens with any physical asset a business depends on: facility equipment, fleet vehicles, manufacturing lines, IT hardware, medical devices. The maintenance system has one model number, the warranty portal has another, the original install record has a third. The wrong part gets ordered. The next visit is two weeks out. Warranties go unclaimed because the paperwork doesn't match.
AGYL resolves the asset registry. Every asset becomes one record with its model, serial, warranty terms, service history, OEM bulletins, and the right parts cross-referenced. Field teams see the truth before dispatch. Procurement reads the same asset as Operations. Finance doesn't lose claims that should have been filed.
A new project lands and needs staffing. The HR system has job titles. The certifications database has credentials, some expired. Project records show what people shipped, in detail. Code commits and tickets show what they actually built. Peer attestation in Slack shows who other engineers ask when something's hard. None of these systems agree on what your people are capable of, so staffing decisions get made on the wrong signal — title, tenure, or availability — instead of demonstrated expertise.
AGYL resolves the capability record. Every person becomes a structured account of demonstrated skill, drawn from authoritative sources, validated by their team and themselves, and kept current. The right person on the right project gets named in seconds. When someone leaves, what they knew stays in the system instead of walking out with them.
A circuit breaker is rated 5A in one field and 15A in three others. On an electrical part, the wrong rating isn't a typo; it's a safety issue. An AI agent generating an answer about that product, a copilot recommending it to a buyer, or a marketplace feed publishing it to retail will all act on whichever record wins the race. Across 500,000 products and 4,000 attributes each, no team reads every record by hand. This is where AI in commerce hits its limit, and EKOM is the operating layer that pushes that limit back.
EKOM is AGYL's flagship product, the first application of the operating layer to be productized as a standalone. Commerce-team demand pulled it out as its own brand and its own go-to-market. It runs in production today across the catalog, and it is the layer the AI built on top of that catalog reads from.
The six examples above are where customers have actually started. They're not the limit of what the operating layer can do. Most operating functions inside a serious business have records that span systems, that drift, and that AI investments will quietly stumble over until something resolves them.
If you have a record that matters, lives in many places, and isn't reliable enough to act on, AGYL applies. The architecture is the same. The evidence chain is the same. The team that helps you stand it up is the same. What changes is the shape of the record and the tools the data lives in.
The first application of the operating layer to be productized as a standalone, driven by demand from commerce teams. EKOM is what the operating layer looks like when it ships as its own thing.
A standalone product, sold and run as its own thing. Built on AGYL's operating layer.
EKOM is AGYL's flagship product and the operating layer for AI-ready product data. It runs in production today across the catalog — wherever product data lives in many systems, drifts, and matters enough to get right. Commerce teams use it to reconcile every product into one record, determine what is true against the evidence, and hand AI-ready records to every system that reads them, including the agents and copilots being built on top.
EKOM is the first application of AGYL's operating layer to be productized as its own standalone, driven by demand from commerce teams. The architecture EKOM proved in commerce is the same architecture every other record on this page now runs on.
AGYL sits above your stack and writes resolved records back. No migration. No re-platform.
Foundation models supply inference. They are interchangeable. The architecture above them, and the intelligence inside it, is yours.
Single-tenant. Encrypted. Never used to train models that serve other customers. If you leave, your operating layer leaves with you.
Your team sets the thresholds and approves the calls that carry risk. Every decision is recorded, traceable, reversible.
AGYL was started by operators, not data scientists. The team behind it has spent years inside the systems we now build the operating layer for — running catalogs, building data infrastructure, sitting through the meetings where everyone argues about which spreadsheet to believe.
That history shows up in the work. We don't ship abstract platforms. We sit alongside your team through the first records, encode the standards your people have spent careers building, and make sure what we build is something they can run when we step back.
EKOM, our flagship product, is the proof. It runs in production today for catalog teams who would not have time for software that needed babysitting. The same discipline is applied to every record AGYL touches.
Foundation models are becoming infrastructure. Whichever model leads this quarter, another catches up next. What stays in your business is what you build above the model — your records, your standards, the decisions your team has made about what good looks like.
In early 2026, one of the world's largest application software vendors acquired the leading master-data-management platform for the explicit purpose of making AI-ready data available across its installed base. Around the same time, a Fortune 100 hospitality leader publicly committed over a billion dollars to build the same kind of layer in-house, naming it for what it does for AI rather than what it does for data management. The most sophisticated buyers are building this layer either way. The question is whether they build it themselves, buy it bundled inside someone else's stack, or work with a company whose only job is to get this layer right.
That is what AGYL is for.
Share a representative slice. We will show you what is resolvable today, what your AI is currently stalling on, and what the loop looks like once the operating layer is running. Documented against your own records, with the evidence behind every finding.