Data architecture, productized
The home your data lives in.
AI performance is bounded by data architecture, not model capability. Every institution still designs that architecture by hand, by whoever is in the room, and then fixes the outputs when they go wrong. Sloie fixes the roots: how each fact is created, stored and controlled, for all of your data, so the operation it was made for runs faster and the decisions built on it can be trusted.
How a wrong decision gets made
Ask a simple question. Watch the answer get stitched together.
Nobody at the institution is careless. The number is wrong because the answer had to be assembled from five systems that never agreed on what a client, a merchant, a member or a part is. A model does the stitching in seconds and sounds certain. Pick a question.
The science
How much of your data should be structured? There is a right answer.
When a fact is structured and already aggregated, the answer is a lookup: cheap, exact, the same every time. When it is not, a model has to reassemble the number from pieces on every question, paying compute each time and getting it wrong in ways nobody can see. Structure everything and you pay to columnize material that never needed a column. The right amount is between, it is different for every kind of data, and finding it is the work our mathematicians do.
Roots, not outputs
Data comes from somewhere. Fix it there.
Every fact in your institution was created by an operation: an account opened, a trade booked, a claim filed, a contract signed. Most data programs fix the report at the end. Sloie fixes the root: how the fact is created, where it is stored, and what controls it passes on the way in. The cure is part of the process, not a cleanup after it.
Some data was never created by your operations. Countries, currencies, legal-entity types, medicine and disease names, building materials, zoning classes. These exist before your first transaction and they are the bedrock: fixed once, controlled forever, different per industry.
A fact is stored the way the process that made it needs it: an account balance as an event, a rate as a timestamped attribute, a legal name as an identity fact. That is what makes the operation fast.
Insight is not a second copy of the data and not a model's reconstruction of it. It is a lookup over the same home the operation writes to, so the board number and the operational number cannot disagree.
A country code not in the list, a rate in the wrong unit, a product without an owner: caught when written, at the source system, not discovered three months later in a reconciliation.
We do not clean the output and hand it back. We restructure the ground the company's systems are built on, so what they produce is right by construction.
What Sloie actually does
Reads your data. Builds the home. Keeps it standing.
Nothing moves and nothing new is installed in your stack. Sloie reads your systems where they are, then hands back the structure underneath them, in formats your systems already speak.
Read-only access to your sources: the CRM, the core system, the spreadsheets, the documents. Every field is catalogued: what it is, where it lives, how many other places hold the same fact.
A list of specific defects, with the rows behind each one: the same client under three spellings, a rate in two units, a product code the catalog does not know, a total nobody can reproduce. Not a score. A list you can act on.
Countries, currencies, identifiers, dates, units: corrected against the reference they belong to, logged against the source, and guarded by a control so the wrong form cannot come back.
What "parent" means. Which rating scale is authoritative. Whether the group limit covers subsidiaries. A short list of decisions only you can make, each with what it changes downstream. Sloie never guesses these.
Every fact gets exactly one place: on the bedrock of facts everyone agrees on, or in one of three layers above it. Your existing tables keep working as views over that home, so nothing can drift again.
The number you produce by hand in days comes out in seconds, with a trail to every source row. Then the house is re-checked as your data changes: new fields land already placed, controls stay current, and the number stays right next quarter.
The map of your data. The defect list. The fixes and their controls. Your decision list, answered. The home itself, with your tables as views over it. The proof number with its trail. And a standing check that it all still holds.
Everything. Your systems, your data, your vendors. The structure Sloie builds is yours in plain SQL, controls and documents. Sloie is what reads, builds and re-checks; the home is yours.
Why now
Models got better. Answers did not.
Restructuring the data underneath a model moves its accuracy more than upgrading the model does; the 2026 benchmarks say so plainly. Yet every semantic layer, knowledge graph and ontology on the market presupposes a substrate somebody else built. Nobody builds it. It is done bespoke, per institution, at cost, and it does not hold.
Sloie builds it, as a product, and keeps it standing.
of AI projects without AI-ready data will be abandoned through 2026. Gartner.
of enterprise GenAI pilots show no measurable return. MIT, 2025.
agent accuracy as the data layer goes from documents to deterministic structure, same model. arXiv, 2026.
home per datum. Our whole position, in a number.
First market: US financial institutions.
We are taking a small number of design partners for counterparty and product data. If you are the person who reconciles the number by hand, we would like to talk.
Become a design partner