Bud Data Foundry
AI that knows your whole organization — built on the systems you already run, with the permissions you already set. Documents, databases, APIs, live streams, logs and metrics across 200+ sources become one living model that people and AI agents can ask, analyze and act on. Everyone sees only what they are allowed to see; every answer shows where it came from; nothing about your existing systems has to change.
A living model of your organization — for people and for AI agents.
Data Foundry turns everything your organization knows — across every system you use — into one living model that people and agents can ask, analyze and act on. Every one of its five capabilities checks what a person or agent is allowed to see at every step, not just at the end. The data stays where it lives. The answer is sourced, or Data Foundry says it doesn't know.
Layer 06 — the ontology layer, the one role nothing else in the stack has.
Data Foundry grounds everything above it. Bud Agent and Bud Studio ask it; Bud MCP Foundry's tools act through it; Bud SENTRY governs it; Bud AI Foundry serves the models that read it, on fleets GPU Foundry and LayerZero run.
Feeds upward — agents in Bud Agent and Bud Studio reason over the knowledge graph and act through Bud MCP Foundry's tools, under Bud SENTRY's policies, seeing only what the person they work for sees.
Builds on Bud AI Foundry's serving plane for the models that read and answer, and on Bud GPU Foundry and LayerZero for the capacity they run on — any model, any silicon, inside your boundary.
Why most "AI over your company's knowledge" projects stall
Most organizations start the obvious way: connect a few systems, put a chat window on top, and see what happens. It's quick to stand up — and three things reliably sink it before it gets past the pilot. Data Foundry was built to avoid all three.
- Gathering everything first and thinking about access later.Copying documents into one place and working out who may see what afterwards — if at all. Once the original access rules are left behind they're hard to put back, and the filtering added later leaks through result counts, previews and suggestions. Point an AI assistant at it and the leak runs itself.
- Treating your knowledge as a heap of text.Chop it up, look for bits that sound similar, hope for the best. No sense of who works on what, which system holds which data, or how things relate — and nothing for an agent to reason over. It can't connect the dots, can't tell two people apart, and gives the CFO and the intern the same answer.
- Sending your most sensitive information somewhere you don't control.Many tools run only in their own cloud. For banks, governments, defense and anyone with rules about where data may live and who may hold it, that's not a trade-off — it's a deal-breaker before the conversation starts.
Five things that build on each other — and the oversight around them.
Every one of them checks what a person or agent is allowed to see at every step, not just at the end.
Permissions built in — not bolted on
Data Foundry starts from one simple idea: who is allowed to see what is part of your knowledge, and deserves the same care. Four things follow.
The guarantee in one line. The permission check happens before the search, for the specific person asking, against the live rules — and again on every answer. It isn't a setting anyone has to remember to turn on.
Data gravity — nothing to migrate, nothing to replace
Data has gravity. It piles up where it's created — in the databases, warehouses, streams and systems that run the business — and the bigger and more sensitive it gets, the harder and riskier it is to move. Data Foundry is built so you never have to.
| How a source is read | What it means | Typical sources |
|---|---|---|
| A working copy | Data Foundry reads from your system and keeps its own working copy — but your system stays in charge. That copy can be rebuilt from it at any time and thrown away without touching it. Nothing moves out, nothing gets replaced, and Data Foundry never becomes another place you have to protect. | SharePoint · Slack · email · documents · Excel · tickets · wikis · code |
| Ask the source live | Where copying doesn't make sense — a very large warehouse, a stream that changes every second, data too sensitive to duplicate — Data Foundry asks the source directly at the moment of the question. For Snowflake or Databricks it can work from their metadata and semantic views instead of copying the data into Bud. | Snowflake · Databricks · warehouses · streams · logs · metrics · time-series |
| You decide, source by source | The choice is yours per source, and both paths sit behind the same permission check and the same citation. Unstructured and structured sources are processed alongside each other, so one answer can cite a document and a table. | any mix |
This is why Data Foundry gets more useful with every source you connect, not more unwieldy. Your data stays where it is; what grows is the understanding of it — and people and agents alike come to it as the one place to ask. It's also why "delete this everywhere" actually means everywhere.
The digital twin — ontology, knowledge graph, and who is asking
Connecting sources is the easy part. What turns a pile of data into something an AI agent can reason over is structure and meaning. Data Foundry builds that in three layers, and together they form a living model of the organization.
| Layer | What it holds | What it makes possible |
|---|---|---|
| The ontology | A shared vocabulary of what your business is about — people, teams, customers, products, projects, systems, datasets — and a running catalog of everything you have, down to each table and field. People and agents can both enrich it. | Every search, answer and agent works from the same definitions. An agent asked about "churn" or "SLA breaches" knows what those mean here, and which data answers them. |
| The knowledge graph | How it all connects: who belongs to which team, which system holds which data, which customers and products a record or a metric concerns, how work and data flow between them. | Reasoning, not just retrieval. An agent can follow a thread across systems — from a customer to their tickets to the service metrics behind them — spot the pattern, and explain why. |
| Who is asking | A private understanding of every individual — and of every agent with its own identity: role, what they work on, what they know, what they've asked before. People can see theirs and correct it; you set each agent's. | Two people ask the same question and each gets the answer that fits them. An agent working for someone inherits the same understanding — and the same limits — so it is personal and safe from day one. |
Personal never means permissive. Knowing someone changes what Data Foundry shows them first — never what they're allowed to see.
Answers you could put in front of a regulator
An answer is only as good as the reading behind it. Read carelessly, tables become run-on text, spreadsheets lose their formulas, a metric loses its units, a log loses its timing, and the numbers a business runs on quietly go wrong.
Documents keep their tables, formulas and layout. Databases and streams keep their structure, types and timing. Anything that couldn't be read is flagged rather than glossed over, and Data Foundry can show the exact passage or record an answer came from.
Ask about figures — a total, a trend, a comparison — and Data Foundry works it out from the actual data, then shows which table, which rows, as of when. The same for an agent. Nobody needs technical skills or database access.
Every answer comes only from real material you're allowed to see, with sources you can click through to. When the answer isn't there — or isn't there for you — Data Foundry says so. "Not in your accessible knowledge" is a real answer. Making something up is not.
Built for AI agents — with oversight that can see oversharing
People are one kind of user. AI agents will be the main one — and Data Foundry treats them that way from the ground up. An agent connects through a standard interface and gets everything a person gets: search, sourced answers, questions of the data, the knowledge graph to reason over, and the ability to act. And it is a real user, not a borrowed login — whether it is working on someone's behalf or on its own.
| An agent that is… | sees | and |
|---|---|---|
| Working for a person | Only what that person sees. Every request is answered as the person the agent is working for. | Two people using the same agent get different answers. Handing work to an agent can only ever narrow what's visible, never widen it. |
| Working on its own | Exactly the sources, areas and actions you grant it — just as you would for a new member of staff — changeable at any time. | Within those limits it builds up its own knowledge and its own memory of what it has learned, kept separate from any person's. Nothing is inherited by default. |
| About to act | A preview of the action, checked against your rules. | Carried out, then confirmed. Anything that can't be undone needs a person's approval first. |
It can spot oversharing, not just avoid it. Because Data Foundry understands your access rules, it can see where something is open to more people than it should be — and tell you. Every access, every action, every permission change is kept as evidence you can export: one complete record of who saw what, when, and why.
Permission first. Then the search. Then the check again.
How a question is answered, how an agent acts, and the three tiers of handling you choose per area.
How a question is answered
How an agent acts
Different handling for different sensitivities
You choose per area. The tiers change how material is stored and checked — never who may see it.
Runs wherever your rules require.
On your own infrastructure, in a fully offline environment, or as a managed service — with your knowledge and your permissions never leaving your control. That is what a regulated organization can actually sign off.
Where it runs, what it reads, who asks
Where it runs
Inside your own boundary when that's where it needs to be. Runs on the Bud Novaria AI OS — any model, any silicon, on-premise, hybrid or air-gapped — so the models that read and answer never leave the boundary either.
Sources
200+ sources, including SharePoint, Slack, documents and Excel alongside Snowflake and Databricks through their metadata and semantic views. If one of yours isn't on the list, we add it.
Interfaces
People search and ask in plain English. Agents connect through a standard interface and get everything a person gets — search, sourced answers, questions of the data, the graph to reason over, and the ability to act — as real users with their own identity.
Nothing moves out. The only thing that comes in is content together with the rules about who may see it. What never moves is your data: the working copy is rebuildable and disposable, the most sensitive sources are asked live, and "delete this everywhere" actually means everywhere.
Every headline number, with its basis.
The figures on this page are properties of the design, not benchmark results. Each is paired with where it comes from.
How these claims are framed. Source counts, check counts and layer counts are properties of the design. Deployment-specific numbers — answer latency on your hardware, coverage of your estate, the share of questions answered from the working copy versus the live source — are established in a proof-of-concept on your infrastructure, with your data and your permissions.
Against the usual approach.
Side by side with the way most organizations first try this — a chat window over their documents, built in-house or bought off the shelf.
| Chat over your documents | Bud Data Foundry | |
|---|---|---|
| Who sees what | Worked out afterwards, if at all; the assistant usually holds a master key | Rules captured with the content, checked before the search, re-checked on every answer |
| What it covers | Documents, maybe a database | 200+ sources — documents, databases, APIs, streams, logs, metrics |
| Understanding | Text fragments; nothing an agent can reason over | An ontology and knowledge graph — a digital twin of the organization |
| Your existing systems | Loaded into a new store that becomes one more copy to manage | Stay in charge; the working copy can be rebuilt or deleted at any time |
| Reading quality | Chopped into fragments; tables and numbers flattened | Faithful copy of every source; tables and streams stay data; originals one click away |
| Answers | Fluent, sometimes invented; sources best-effort | Sourced only, cited to the exact passage; says "I don't know" rather than guess |
| AI agents | Given free run of everything, or bolted on as an afterthought | Real users with their own identity and limits — or acting for a person and seeing only what that person sees |
| Knows who's asking | No, or only what's in the prompt | Yes — it gets to know each person, strictly inside their permissions |
| Oversight | Whatever was bolted on | Sensitivity tiers, oversharing detection, a full record of who saw what and why |
| Where it runs | Depends on every component | Your infrastructure, fully offline, or managed — your choice |
The best of them also connect broadly and respect permissions, and this brief doesn't pretend otherwise. Where they are already deployed and the question is document search for people, they do the job.
Data beyond documents; a knowledge graph agents can reason over; running inside your own boundary or fully offline; refusing to answer rather than guessing; seeing oversharing rather than just avoiding it; and agents that can analyze and act — under approval. We're glad to go through any of them side by side.
Comparison current as of Q4 2026 · the left column is the category approach, not a named vendor
Every organization being asked to put agents to work on what it knows.
Especially the ones with intricate access rules, answers that have to be trustworthy, and data that can't leave their control.
Answers a regulator can follow
Every answer cited to the passage, the table and the rows, as of when; permissions checked before the search; one exportable record of who saw what — inside the boundary.
Fully offline, with sensitivity tiers
Runs in a disconnected environment; the most protected material is never stored as text; agents have identities of their own with exactly the sources and actions they are granted.
The warehouse stays where it is
Snowflake or Databricks read through metadata and semantic views; SharePoint, Slack, documents and Excel processed alongside. Nothing migrated, nothing replaced.
The grounding layer for agents
A knowledge graph agents reason over, a standard interface they connect through, and an action loop — preview, check, act, confirm — that makes handing them work safe.
One place to ask
Contracts, tickets, drawings, field reports and the systems behind them, connected — so a person or an agent can follow a thread from a customer to their tickets to the metrics behind them.
Delivered as a managed service
Run Data Foundry for your customers on sovereign or dedicated infrastructure, with each customer's knowledge and permissions never leaving their control.
The platform around the model.
This brief is the reference for Bud Data Foundry. For the platform-level argument — why data, serving, agents and governance belong on one plane — read the whitepaper, or return to the product overview.
Platform White Paper
The Enterprise AI Management Platform
Where the ontology layer fits
The platform-level argument — why data, serving, agents and governance belong on one plane, and the economics that follow.
Read the white paperBack to overview
Bud Data Foundry
AI that knows your whole organization
Return to the product page — the film, the answer path, the digital twin, data gravity, and the comparison at a glance.
Back to the product pagePut your data on it.
The fastest way to see what a living model of your organization does for your people and your agents is a proof-of-concept on your infrastructure, with your data and your permissions.