The work you cannot send to a third-party AI service is the work Hive was built for.
Privilege, material non-public information, and personal health information do not survive a third-party endpoint. Here is what each looks like when the AI runs inside your own walls instead.
Material non-public information cannot sit on someone else’s server.
- The pain
- Internal AI agents in finance touch the most sensitive data a firm holds: deal information, unpublished results, and client positions. Routing that through a third-party API creates an external record subject to discovery and recordkeeping scrutiny, and it sits awkwardly against third-party and outsourcing-risk expectations.
- What Hive does
- Hive runs internal agents and analysis on open-weight models on the institution’s own hardware, with a full local audit trail of every request.
- Exposure removed
- Material non-public information never leaves the institution’s boundary, the cross-border transfer step is removed, and there is no external endpoint holding a discoverable copy of the prompt.
A Canadian asset manager wants an internal research agent that reads draft deal memos and unpublished earnings models. OSFI Guideline B-10 sets expectations for third-party and outsourcing risk, B-13 for technology and cyber risk, and Quebec Law 25 would treat sending that data outside Quebec as a transfer requiring assessment. Running the agent on a third-party API would place material non-public information into an external provider’s systems. With Hive on-prem, the agent runs on the firm’s own box, the data stays inside the institution’s boundary, the audit trail stays local and inspectable, and there is no cross-border transfer to assess. The firm validates the fit with its own auditor.
Outcome Internal agents on your most sensitive data, with the data never leaving your control.
Personal health information stays with the custodian, full stop.
- The pain
- Clinical and administrative AI workflows are valuable, but personal health information sent to an external AI endpoint puts the custodian’s accountability at risk and can constitute a breach without an appropriate agreement and controls in place.
- What Hive does
- Hive runs clinic and hospital workflows on open-weight models on hardware the custodian controls, so personal health information stays in the custodian’s own systems.
- Exposure removed
- No personal health information enters an external service provider’s data path. The custodian keeps direct control, which is the posture the accountability obligation expects.
A US regional health system wants to draft visit summaries and triage intake notes with AI across several clinics. Under HIPAA, the system stays responsible for how any service provider handles protected health information, and a third-party AI endpoint would place patient records outside its direct control without a business associate agreement and matching controls. With Hive on-prem, the model runs on hardware inside the health system, the summaries are drafted locally, and the patient data never leaves the custodian’s own systems. The health system validates HIPAA fit with its own counsel.
Outcome The clinical productivity, without ever handing patient data to a third party.
Privilege does not survive a third-party processor.
- The pain
- Document review and drafting are exactly where AI helps most, and exactly where the data is most protected. Sending privileged material or client confidences to a third-party AI service puts another party in the chain of custody, which can risk waiver and breach the duty of confidentiality.
- What Hive does
- Hive runs an open-weight model on a box inside the firm. Lawyers can summarise, review, and search across matter files, contracts, and discovery sets with the model running locally.
- Exposure removed
- No client confidence reaches an external processor. There is no third-party record of the prompt to be subpoenaed and no out-of-jurisdiction provider to be compelled.
An Ontario litigation boutique needs to review forty thousand documents in a production. Sending them to a third-party AI tool would route privileged material through an external provider, with the duty of confidentiality under the Law Society of Ontario rules in play and a real risk of privilege waiver. With Hive on-prem, the review model runs on a box in the firm’s own server room. The documents never leave the building, the work product stays privileged, and the partner can attest exactly where every byte was processed. The firm confirms the approach with its own counsel.
Outcome Faster review, privilege intact, nothing to disclose to anyone.
Some environments allow no outbound path at all.
- The pain
- Public-sector and defence workloads often carry classification, residency, and accreditation requirements that no external AI endpoint can satisfy. For the most sensitive of these, any outbound network connection is itself prohibited, which rules out every hosted AI service by definition.
- What Hive does
- Hive runs on hardware the organisation controls, and supports air-gapped and offline deployment where no outbound network path is permitted, so inference runs with no external connectivity at all.
- Exposure removed
- With no outbound path, there is no external endpoint, no third-party record, and no cross-jurisdiction reach. The data stays inside the accredited boundary because the architecture provides no way out.
A UK public-sector body needs AI assistance on sensitive case files held under strict residency and access controls, in an environment where outbound connectivity is not permitted. A hosted AI service is impossible by rule. With Hive deployed air-gapped on hardware inside the accredited boundary, analysts get a capable assistant with no external connection, the case files never leave the environment, and the deployment can be inspected end to end. The body validates accreditation fit with its own authority.
Outcome Modern AI inside the boundary, with no path out by design.
When “do not send our data out” is a hard rule, on-prem is the posture that keeps it.
- The pain
- Many organisations are not in a named regulated vertical but still treat their data as something they will not put on someone else’s server: trade secrets, source code, R&D, customer records, and competitive plans. For them, every third-party AI tool is a policy violation waiting to happen.
- What Hive does
- Hive gives them the same modern AI capability their competitors get from public tools, running entirely on their own hardware so it never touches an outside provider.
- Exposure removed
- There is no external endpoint, no third-party record, and no out-of-jurisdiction data path. The hard rule stays intact because the data never leaves.
A manufacturer with valuable process IP wants its engineers to use AI on internal design documents and supplier contracts, but company policy forbids sending any internal data to a third party. Public AI tools are off the table by rule. With Hive on-prem, the engineers get a fully capable assistant running on a box in the building, the IP never leaves the network, and the policy is honoured by architecture rather than by trust.
Outcome Modern AI for your team, with your no-third-party rule kept by design.
You do not need to be an enterprise to own your AI.
The entry box is workstation-class and priced in the low thousands, which puts owned AI within reach of a small firm, not only a large institution. A boutique law practice, a small clinic, or a lean finance shop can run the same on-prem posture as a much larger organisation, on a single box that pays for itself in about seven months on sustained daily work. As the team grows, the same box extends with added compute and nodes rather than being replaced.
Glean, Writer, Cohere North, and Copilot route every query through their cloud. Hive runs in yours.
Find your use case running inside your own network.
Book a demo and we will show Hive running locally on the kind of work your team does every day.
Regulatory points on this page are general information, not legal or compliance advice. Validate fit for your obligations with your own counsel.