No AI agent, copilot, or company brain can reason on records that disagree with each other. AGYL is the layer that makes them agree. It reads across your systems, decides what is true about every record that matters, and hands clean, governed records to whatever reads them next.
Every enterprise software category has incumbents designed for the analytics era now retrofitting AI on top. AGYL was built for the AI era from the start.
AGYL is software you run, inside your tenant. Your team operates it. Onboarding is included and finite. After that, the platform runs continuously, on your terms. No ongoing services billing.
You do, by architecture. Your records, standards, and approved decisions stay inside your tenant and never train models that serve another customer. If you leave, the layer leaves with you.
Every AI initiative inside your business is now bottlenecked at the same layer. Foundation models keep getting more capable. The advantage is moving above them, to where your records, standards, and judgments live.
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. Runs above your 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 team's approved judgments. The longer it runs, the more your operation runs itself on your terms.
Source connectors, vertical libraries, the configuration framework, the deployment methodology proven in production. You are not buying a services engagement. You are buying software, packaged with the IP that gets it running.
Foundation models keep getting more capable and increasingly interchangeable. 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 AGYL.
The model layer is becoming infrastructure. The operating layer above it is where every serious business will turn its records into something the model can act on.
Once the operating layer is running, the things your AI has been stalling on stop being problems. The model finally has something reliable to read from.
An agent quoting a customer or dispatching a technician is only as accurate as the record it reads. AGYL hands it one resolved record, with the evidence behind every value.
When a CFO asks “what is our exposure to this supplier,” the answer is only as good as the records behind it. AGYL makes the answer trustworthy enough to act on.
Any retrieval system — RAG or otherwise — is only as good as the records it pulls from. AGYL resolves the records first, so retrieval reads one canonical version of each entity instead of three conflicting ones.
An architecture proven in production on product data, applied to every record a business needs to get right. Runs above your stack. Replaces nothing in it.
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 market is loud right now about company "brains." They sound impressive in a demo and stumble in production, because no brain can think clearly on records that disagree with each other. The companies actually getting AI to work figured this out the hard way: the brain has to read from somewhere, and that somewhere has to be reliable, or the brain is just confidently wrong.
AGYL is the layer underneath. The substrate that decides what is true about every customer, every supplier, every contract, every product. Once it's running, every department reads the same truth. The brain becomes possible because the records it reads from finally agree.
The asset compounds in two directions. Inside each record domain — a customer master, a supplier file, a product catalog — the graph gets denser as more records flow through it, and the system understands your business better every week. Across domains, the brains start to talk to each other. Your customer record references your contract record references your supplier record references your equipment record. Institutional knowledge stops walking out the door when people leave. It accumulates in the graph and stays.
You start with one record domain. Customer master, supplier file, product catalog — wherever the business is most exposed. Once it's running, you add a second. By the time the third is in production, the brains are talking to each other. What you've built is no longer a resolved record. It's the underlying language your business uses to decide things.
Six places where the operating layer makes new things possible, drawn from where customers have started. The list is not exhaustive. Most operating functions qualify — records that span systems, that drift, and that AI agents and copilots stumble over until something resolves them.
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 AGYL 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.
Each of these is one brain. The architecture connects them as more come online.
A standalone product, sold and run as its own thing. Built on AGYL’s operating layer.
EKOM is AGYL’s flagship product and the AI-ready data layer for commerce. 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 AGYL application 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.
Different buyers, the same architecture underneath. If your work depends on records being right, you are who we work with.
You are shipping agents, copilots, retrieval systems, vertical AI products. The demo works. Production exposes the data problem your team has been quietly papering over.
AGYL is the infrastructure underneath your product. We make sure the records your model reads are governed, current, and agree with each other. You stay focused on the brain. We make the brain possible.
You have an AI roadmap. You have records spread across systems that disagree with each other. You know the brain you want to build cannot read from messy data and still be right.
Start with one record domain. The one most exposed to your AI roadmap. Customer master, supplier file, product catalog, contract obligations. Add the next when the first is in production. The asset compounds inside your tenant.
Your engagements are increasingly defined by the data problem. The strategy decks land. The roadmap clears. Then implementation hits records that don't agree, and the program slows.
AGYL is the layer you recommend and stand up. Your team brings the engagement, the relationship, and the methodology. We bring the platform, the IP, and the production proof. Your client gets a foundation that survives past the engagement.
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.
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.
The brain you want to build cannot exist without this layer underneath. Pick the record domain most exposed to your AI roadmap and we will show you what is resolvable today, what your team is stalling on, and what the loop looks like once it is running. Documented against your own records, with the evidence behind every finding.