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Use cases

Solutions describes System9 by industry. This page describes it by workload, because the reason an organization arrives is usually a job to be done rather than a vertical.

Each one names the capabilities it lands on, so you can follow it straight into the product documentation.

1. Govern an estate you already own​

The job. Catalog, classify, lineage, and enforce policy across warehouses, object stores, file shares, SaaS content, and mailboxes, without a migration program first.

Where it usually stalls. Governance tools sit beside the data and can be bypassed by reaching the engine another way. Coverage stops at the structured half, so the file shares and the scanned documents stay dark.

How it works here. Foreign catalogs and databases register as federated sources, so a table stays where it lives and still gets catalogued, classified, and policy-enforced. Unstructured content runs through discover, parse, understand, govern and is registered as a catalog asset, which means a scanned PDF inherits the same classification ladder and lineage chain as a table. Row filters and column masks compile into the query plan of each engine rather than being applied by a service in front of it.

Start with the catalog, in-engine enforcement, and third-party interoperability.

2. Give agents real access, accountably​

The job. Let agents read production data, call tools, and take consequential action, with an answer to "who was that, what were they allowed to do, what did it cost, and what would have stopped them."

Where it usually stalls. The agent is an API key. Identity, budget, and audit are bolted on by the framework, and the guardrail is a prompt instruction or an output filter rather than an authorization decision.

How it works here. An agent is a directory entity with its own key material and a certificate carrying its ID and clearance, holding a bounded delegation of a person's authority that can only ever narrow as it is passed down. Spending is checked against agent, workflow, and organization budgets before execution. A decision gate scores the action's net effect and can refuse it at the boundary. The workflow journal is signed per step and externally replayable.

Start with the life of an agent, delegation and authority, and the decision gate.

3. Run where the network does not​

The job. Operate at a plant, a forward site, a vessel, or a secure facility where the link is intermittent, contested, or absent, without standing up a second platform with a second governance model.

Where it usually stalls. The platform treats a partition as an error. Policy needs a round trip, licensing needs a call home, and inference needs a vendor endpoint, so the disconnected site gets a different stack.

How it works here. Nothing phones home to start. Licensing verifies entirely offline against an embedded key. Policy holds last-known-good and keeps reconciling locally, and new policy arrives by signed offline bundle. Inference runs on a local model runtime on both the cell and the device. An air-gapped peer is modeled as an absent edge, not a failed peer to retry, and work done while dark accumulates in a hash-chained journal that flushes on reconnect.

Start with choose a deployment, core and edge, and operate a device air-gapped.

4. Collaborate with parties you do not trust​

The job. Two or more organizations need a joint result from data none of them will hand over.

Where it usually stalls. The clean room is a filter over a shared warehouse in a vendor's cloud, which is exactly the boundary a residency regime forbids.

How it works here. A clean room is a mutual-distrust overlay on a workspace, created deny-all by default. Collaborators see only per-room aliases, so the kernel never sees source table names. Code is reviewed and cryptographically signed by M-of-N reviewers before it can run, and an edit resets approval. Every output crosses an egress gate in order: k-anonymity, an output-row cap, a per-collaborator differential-privacy budget, a per-run epsilon ceiling, a destination allow or deny defaulting to deny, then content inspection. The first failure terminates the run.

Start with the two work boundaries and workspaces and clean rooms.

5. Turn observation into decisions​

The job. Take sensors, cameras, radios, documents, and feeds, and produce a decision that someone will act on and later have to justify.

Where it usually stalls. Ingestion is a pipeline, so provenance is asserted by a system of record rather than attached at capture, and the inference policy (what may be concluded from a sensor) does not exist as a control at all.

How it works here. Capture is signed on the device, at the moment of capture, into a tamper-evident hash chain with frame-level provenance markings. Sensor enrollment carries clock synchronization, without which multi-sensor fusion is guesswork, and a cognition policy governing what may be inferred from a given sensor. Observations are promoted to candidate and then canonical entities as a governed act with an evidence chain, not a join.

Start with Intelligence, sensor intake, and signing and provenance.

6. Prove what happened​

The job. Answer an auditor, a regulator, an incident review, or a court, about who did what, under whose authority, and whether the record is complete.

Where it usually stalls. There is one log pipeline doing the work of three different questions, and its absence of an entry proves nothing.

How it works here. Logging, audit, and lineage are separated on purpose, with different stores, guarantees, retention, and readers. Audit is many scoped chains rather than one, per workspace and per clean room. Reads are audited, not just writes. When a chain append fails the gap is written as a first-class event, so a verifier can tell a known append failure from a removed row. Lineage capture is deny-by-default: a mutation that cannot be captured can be rejected rather than silently proceeding.

Start with audit and tamper-evidence and the Activity Ledger.

7. Buy and sell data across an organizational boundary​

The job. List, discover, and consume data products across organizations that share no secret and may share no network.

Where it usually stalls. Cross-organization trust is verified through the vendor's control plane, so the transaction requires the vendor's cloud to be reachable and trusted by both parties.

How it works here. A listing is a signed attestation, not a database row: an offer identity, a content fingerprint, a reach, an attenuating scope cap, and a validity window, signed with a hybrid construction carrying both a classical and a post-quantum signature. A verifier that has pinned a publisher to hybrid refuses a listing that drops the post-quantum half. The data never crosses, only the signed listing does, and verification works offline against a clock the verifier supplies.

Start with the Data Exchange and verify a listing offline.

On compliance mappings

A compliance mapping here says how a capability supports an obligation, with the evidence the platform can produce for it. Accreditation is granted by your authority, and the platform's job is to make the evidence for it cheap to produce rather than to reconstruct.

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