AI that knows your whole organization.
Built on the systems you already run, with the permissions you already set. Bud Data Foundry turns documents, databases, APIs, live streams, logs and metrics — across every system you use — into 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.
Enterprise data becomes context.
Contracts, tickets, drawings, warehouses, logs — structured and unstructured alike — connected and grounded so that what a model says is anchored in the business rather than the open internet. One end-to-end data system: connectors, comprehension, an ontology and knowledge graph, search and answers, and data agents at scale.
Runs on your own infrastructure, in a fully offline environment, or as a managed service — with your knowledge and your permissions never leaving your control.
A living model of the organization — for people and for agents.
Every organization is being asked to put AI agents to work on everything it knows — and every one hits the same wall: data scattered across hundreds of systems, access rules that are intricate, answers that have to be trustworthy, and data that can't leave its control. Solve one of these and you get a pilot. Data Foundry solves them together — a living model of the whole organization, built where the data already lives, that every person and every agent can ask, analyze and act on, seeing only what they should.
The check happens before the search. And again on every answer.
Data Foundry starts from one idea: who is allowed to see what is part of your knowledge, and deserves the same care. It doesn't gather widely and then hide things — it only looks at what you're allowed to see in the first place, so nothing can leak through a count, a preview or a suggestion.
Which enterprise customers are at risk of churn this quarter, and why?
- CRM · accounts and renewalsin
- Service metrics · time-seriesin
- Support ticketsin
- Board pack · Q3 forecastout
- HR · people recordsout
Seven accounts carry a renewal risk above the threshold. Four show the same pattern: sev-1 tickets up in the last 60 days while p95 latency breached the SLA twice.
Not in your accessible knowledge — a real answer. Making something up is not.Access rules come in with the content
When Data Foundry connects to a system, it brings the documents and the rules about who can see them together, from day one. It also recognizes that "j.smith" in one system and "Jane Smith" in another are the same person — and when it isn't sure, it asks rather than guesses.
Losing access comes first
An out-of-date document is a small problem. An out-of-date permission is a breach. So when someone's access is removed, that change jumps the queue.
An agent can only ever narrow, never widen
Working for a person, an agent sees only what that person sees. Two people using the same agent get different answers. Working on its own, an agent has an identity of its own and exactly the sources, areas and actions you grant it — nothing inherited by default.
A knowledge graph agents reason over — not a heap of text to search.
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.
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. An agent asked about "churn" or "SLA breaches" knows what those mean here.
Who belongs to which team, which system holds which data, which customers and products a record or a metric concerns. Reasoning, not just retrieval: an agent follows a thread across systems and explains why.
A private understanding of every individual — and of every agent with its own identity: role, what they work on, what they know. Two people ask the same question and each gets the answer that fits them. Personal never means permissive.
People and agents both enrich it
The ontology is a living vocabulary. Every search, answer and agent works from the same definitions, and both people and agents can add to them.
Numbers that stay numbers
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.
An agent inherits the same limits
An agent working for someone inherits the same understanding — and the same boundaries — so it is personal and safe from day one.
Nothing to migrate. Nothing to replace.
Data 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.
The only thing that comes in: content together with the rules about who may see it, kept current in the background.
What never moves: your data. Data Foundry never becomes another place you have to protect — and "delete this everywhere" actually means everywhere.
Your systems stay in charge
Data Foundry reads from your systems and keeps its own working copy — but that copy can be rebuilt from them at any time and thrown away without touching them. Nothing moves out, nothing gets replaced.
More useful with every source, not more unwieldy
People and agents come to Data Foundry as the one place to ask, while the data keeps living where it was created. That is why connecting the fiftieth source makes the model sharper rather than heavier.
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.
Five things that build on each other.
Every one of them checks what a person or agent is allowed to see at every step — not just at the end. The sixth is what makes the whole thing safe to hand to agents.
Connect
The tools people use every day — shared drives, wikis, help desks, CRM, chat, email, code — and the data underneath them: databases, warehouses, APIs, streams, logs, metrics and time-series. If one of yours isn't on the list, we add it.
Comprehend
Every kind of data read the way a specialist would. Documents keep their layout and tables. Databases and spreadsheets stay structured and queryable. Streams, logs and metrics keep their timing and shape. A number stays a number you can trust.
Organize
A knowledge graph of your organization — people, teams, customers, products, systems, data, and how they all relate — on a shared vocabulary of what your business is actually about. Something an agent can reason over, not just search.
Find & answer
For people: search that finds the exact phrase and the right meaning, and plain-English questions with sourced answers. For agents: the same through a standard interface, plus direct questions of the data itself — totals, trends, comparisons, anomalies — with every number traced back.
Act & learn
Agents can analyze, decide and act — under control. Every action is previewed, checked against your rules, carried out, then confirmed; anything that can't be undone needs a person to approve it. What happens feeds back into the model, so it gets sharper the more it is used.
Oversight that can see oversharing
Ordinary material. Sensitive material, re-checked on every single result. And the most protected material, which Data Foundry never stores the text of at all. Because it understands your access rules, it can see where something is open to more people than it should be — and tell you.
What's inside the layer.
One end-to-end data system — from the connector that reads a source to the record that shows who saw what — instead of a retrieval pipeline assembled from parts.
Source connectors
200+ sources, each bringing its content and its access rules together, kept current in the background. Missing one? We add it.
Preprocessing & indexing
A faithful copy of every source: documents keep tables, formulas and layout; databases and streams keep structure, types and timing; scans recovered as well as the original allows.
Ontology & knowledge graph
A shared vocabulary and a running catalog down to each table and field, with the graph of how everything connects — enriched by people and agents alike.
Search & answer
Exact-phrase and meaning-based search, plain-English questions with sourced answers, and questions of the data itself — for people, and for agents through a standard interface.
Act & learn
Agents analyze, decide and act at scale — each action previewed, checked against your rules, carried out and confirmed; irreversible actions wait for a person.
Sensitivity tiers & audit
Three tiers of handling you choose per area, oversharing detection, and one complete, exportable record of every access, every action and every permission change.
Against the usual approach — a chat window over your documents.
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: gathering everything first and thinking about access later; treating your knowledge as a heap of text; and sending your most sensitive information somewhere you don't control.
| 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 |
A fair word about established enterprise-search products. The best of them also connect broadly and respect permissions, and this page doesn't pretend otherwise. Where the conversation gets specific is on the points above: 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 · Talk to a solutions architect
The full story, in depth.
The five capabilities in full, the four permission rules, the three layers of the digital twin, how a question is answered and how an agent acts, where it runs — and every headline number with its basis.
Product Brief
Bud Data Foundry Product Brief
The deep-dive product reference
Connect, comprehend, organize, find and answer, act and learn — with the permission model, the digital twin, sensitivity tiers, deployment options and the comparison against the usual approach.
Read the product briefPlatform White Paper
The Enterprise AI Management Platform
Where Data Foundry fits in the platform
The platform-level argument — why data, serving, agents and governance belong on one plane, and the economics that follow.
Read the white paperPut 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.