Exze reads your schema and your data, discovers how your business really works, and turns it into a validated domain model. Your AI agents work through that model, with fewer hallucinations and answers you can trace back to the source.
Exze turns a database into an executable business model, governed by rules and accessible to both traditional applications and AI agents.
Available today: the model and AI questions, at demo.exze.com. Coming next: actions, APIs, screens and the MCP server.
A semantic model of the business, organized with Domain-Driven Design (DDD).
AI proposes; your business expert validates.
It fails because nobody taught it how your business works. Every company that connects an AI agent to its own systems runs into the same four walls.
Columns like cst_cd and acct_7120 mean something only to the person who built them. The agent guesses.
Orders in the ERP, price lists in spreadsheets, terms in PDFs. The links between them live in people's heads.
Nothing tells the agent which paths through the data are valid, or what each code means.
Duplicates, orphans, stale copies and impossible values flow straight into the agent's answers.
A real run on a demo distributor database built the way real ERPs end up: 347 tables, 2.9 million rows, no declared foreign keys, and backups, period snapshots and Excel imports mixed in. Nothing below is drawn by hand.
Explore every step yourself: profile, discover, model, validate and ask questions in plain language.
Open the live demo →Every definition, relationship and rule Exze proposes comes with evidence from your actual values. The first thing you get is a health diagnosis of your data, in week one.
ord_hdr.ord_st holds only A, C and X. Proposed meaning: Active, Cancelled, Void.
100% of 1.2M rows · 3 distinct values
cst_mst.cst_cd links to crm_acct.customer_id, with no foreign key declared.
99.2% of codes match · 312 orphans
Order total = sum of its line amounts.
Holds in 98.7% · 15,412 orders break it
The same customer appears under up to three codes, matched by TaxId.
2,104 duplicated customers
ord_hdr_bkp_2019 is an old copy of the orders table.
Last write 2019 · excluded from the model
cst_mst.tax_id and email hold personal data.
Hidden from agents by default
Exze models your business the way domain-driven design does: aggregates with their entities, value objects and invariants, connected only by navigations that make business sense. Measurement does the groundwork, AI drafts the model, and a person signs it off.
Read-only access. Exze measures every table and column: what it really contains, not what its name says. Empty tables, copies, period snapshots and Excel imports are set aside.
Relationships nobody declared, found by comparing actual values. Business rules measured with their exceptions. Quality issues ranked by severity.
AI receives the measured evidence, never full rows of data, and proposes aggregates, invariants, valid and forbidden navigations, a glossary and questions for the expert. Every piece is checked against the database.
A business expert approves, corrects or rejects each piece, sees where it comes from in the source tables, and adds the rules only people know. Exze measures them on the data before accepting them.
Agents answer in plain language through the validated model. Every query declares its route and is checked before it runs; shortcuts that would give false figures are rejected with the reason.
Your company is not one relational database. Orders live in the ERP, price lists in spreadsheets, contract terms in PDFs, carrier rates behind an API. Exze brings each source into the same domain model, so an aggregate can draw on a table and a document at once.
In the live demo you can ask every question twice: once to an AI that reads the database directly, and once through Exze. Here is one of them.
How many active customers do we have?
For this business, an active customer is one with an active order in the last 90 days. That definition lives in people's heads, not in the tables.
It counts customers with st = 'A'. The status field looks right, but the business doesn't use it to mean active.
It applies the rule the expert added and Exze measured on the data: an order in status A in the last 90 days. Cancelled and voided orders don't count.
In the demo the expert can switch that rule off and on, publish, and watch the answer change. demo.exze.com
Enterprise ontology platforms proved that a model of the business is what makes AI useful. They are built for the largest companies in the world. Exze brings the same idea to mid-size companies.
| Enterprise ontology platforms | Exze | |
|---|---|---|
| Built for | Global corporations and governments | Mid-size companies |
| Time to a working model | Months, with on-site engineers | Days, drafted by AI and validated by an expert |
| Your data | Moved into their platform | Stays in your systems |
| Rules and definitions | Written by hand | Measured on your data, with evidence |
| Data quality | A separate project | The first deliverable |
| Language | English first | Spanish and English |
"Modeling how a business really works has been my whole career. Exze turns that work into a product."
Jorge, Founder & CEO. Systems engineer. 13+ years at Office Depot, a Fortune 500 retailer, in relational and multidimensional data modeling that drove profitability solutions such as Cost-to-Serve, among others. A career in the analysis, development and implementation of integrated enterprise systems across many technologies and programming languages.
What your data really says, and what is broken.
One business area, validated with your team.
Plain-language questions, traceable answers.
Alerts on changes, then the next business area.
We are selecting a small group of pilot companies. In four weeks you get a diagnosis of your data, a validated model of one business area, and an AI agent that works on top of it.
jvillasmil@exze.com