Most AI fails inside organizations because it lacks context — it doesn't know your processes, your objects, your relationships. Shugyo builds that structured context first, then runs AI on top of it. Not another agent with plugins.
You've tried ChatGPT. You've run assistants, maybe an innovation pilot. They didn't stick — because AI without context is just a sophisticated autocomplete. It doesn't know:
You can't bolt intelligence onto chaos.
“I don't have access to your invoice data, but generally, delays can be caused by a number of factors…”
“23 invoices are stuck in verification past your 5-day SLA — concentrated with two reviewers. Worth $182K.”
The popular approach hands an agent a pile of plugins and lets it search your systems on every call. Fine for one-off lookups. It breaks the moment a question spans silos or depends on how your objects actually relate.
Ontology is a formal description of your company — what objects exist, what attributes they have, and how they relate to each other.
| Element | Description | Example |
|---|---|---|
| Objects | Things that exist | Invoice, Client, Employee, Project |
| Attributes | Properties of objects | Amount, status, due date, owner |
| Relationships | How objects connect | Invoice belongs to Client |
| Flows | How objects move | Draft → Verified → Sent → Paid |
Your data already lives in BigQuery — loaded via Airbyte, modelled with dbt. The ontology is the curated layer on top: each entity maps to a table, each relationship to a typed JOIN. The AI doesn't guess your schema. It queries an approved map of it.
A compact ontology snapshot is placed in the agent's system prompt, so it writes SQL grounded in your model. This is the difference between a generic assistant and one that understands your business.
A shared vocabulary between your data and AI. Ask about “delayed invoices” and the system knows exactly what that means.
Every question is answered against the same model. No more conflicting interpretations of the same number.
The ontology is a template. The digital twin is that template applied to real data in BigQuery — a living, curated model of your organization. Each entity is a lens onto a table; each relationship a JOIN the AI can traverse.
The twin contains facts: what happened, without interpretation.
Ask in plain language. Get answers grounded in your data.
No SQL. No dashboard hunting. Just questions and answers.
Know when something deviates from the norm — before it becomes a crisis.
Proactive alerts, not reactive firefighting.
See how your metrics move over time, so you can act before a drift becomes a crisis.
Decisions grounded in data, not gut feelings.
AI suggests improvements based on actual bottleneck data.
Evidence-based optimization, not consultant speculation.
The ontology turns your words into meaning. The digital twin turns meaning into a query over real data. The answer comes back grounded — and you can see exactly how it got there.
Every answer is traceable to source data.
Unlike black-box AI, Shugyo shows its work. For every answer, you can inspect:
Challenge with evidence. Refine with feedback.
Intelligence is the floor, not the feature. The point is judgment you can trust — and defend.
Every conclusion traces back to source data. No mysterious outputs.
AI proposes. Humans decide. Critical decisions always require human approval.
Ask “why” and get an answer. Understand the logic, not just the result.
Clear ownership of AI decisions. Measurable outcomes. Continuous monitoring.
The model is built on the data you already have — and it never leaves your control.
Your data is 100% yours. No exceptions.
We don't use your data to train models — ever.
Configure which users access which data. Role-based permissions.
Data encrypted at rest and in transit.
SOC 2 Type II compliant. AI Act ready.
Stop experimenting with AI tools that lack context. Start with a foundation that makes AI useful — grounded in how your organization actually works.