A trade secret is only a secret while it stays inside your walls.
Soverance installs a private AI server inside your company. Your team gets the full power of AI on your strategy, your code, and your numbers. Nothing they type, and no document they upload, ever leaves the building.
The problem, in three facts
Nearly half of employees admit it.
In a major industry privacy survey, 48 percent of employees admitted entering non-public company information into public AI tools.
It took Samsung about twenty days.
Weeks after allowing ChatGPT, Samsung saw three leaks, including proprietary source code and a confidential meeting transcript. Nobody was malicious. The company banned generative AI outright.
Deleting does not mean deleted.
In 2025, a federal court ordered a major AI provider to preserve every consumer conversation, including chats users believed they had deleted.
Every prompt is a copy of your company's thinking, held by someone else.
Your team probably already uses AI, whether you approved it or not. Under deadline pressure, people paste pricing models, source code, customer lists, negotiation positions, and product roadmaps into whatever chatbot is on their phone. Every one of those prompts becomes a record held by a third party.
That record sits under the provider's retention policies, not yours. It can be preserved under legal holds you are never told about, reviewed by the provider's staff, exposed in the provider's breaches, and, on free and consumer tiers, used by default to train the model.
A written policy banning public AI does not stop this. It just pushes it out of sight. The only thing that stops shadow AI is an approved tool that is genuinely better than the shortcut.
Four things you hand over with every paste.
The legal standing of your trade secrets
Trade secret protection rests on the reasonable measures you take to keep the secret. Routinely sending it to a consumer tool that retains it, and whose staff can review it, is not a fact you want to explain to a court, an acquirer, or an investor later.
Your edge, donated to a shared tool
On free and consumer tiers, what your team types can become training data for the same model your competitors use. Your best work, sharpening the tool that levels you with everyone else, for free. Amazon warned its employees about exactly this after finding chatbot answers that resembled internal Amazon data.
A shared third party inside your strategy
The company behind a public AI tool counts millions of customers, very possibly including your competitors and your counterparties. Even if the provider behaves perfectly, you have introduced into your most sensitive work a third party that courts can compel and you cannot audit. Local, that third party does not exist.
Their breach becomes your breach
A public AI provider concentrates sensitive data from millions of users, which makes it a permanent target. Providers have exposed plaintext chat histories through misconfigured databases, leaked other users' data through bugs, and lost customer data through their own analytics vendors. When your documents are in that pool, their incident is your incident, and you find out when they choose to tell you.
The provider's own CEO says it plainly.
"If you go talk to ChatGPT about your most sensitive stuff and then there's a lawsuit, we could be required to produce that."
Where your company's thinking lives, tool by tool
| Dimension | Free AI tools | Enterprise cloud AI | Soverance local AI |
|---|---|---|---|
| Your strategy and code leave the building | Yes | Yes | Never |
| Used to train the model | Yes, by default | No, by contract | Impossible by design |
| Who can read what your team types | The provider, per its own terms | Limited, for abuse review | No one outside your company |
| A record a third party holds | Yes | Yes, retained for provider monitoring | None exists |
| Deletion | Delayed, with legal carve-outs | Governed by provider policy | A file operation your IT controls |
| The model changes under your workflow | Whenever they decide | Whenever they decide | Only when you choose |
| Cost model | Free, because you are the product | Per seat, per token, forever | One-time, unlimited use |
One machine. In your office. Under your control.
Soverance builds a dedicated AI workstation, sized to your company, and installs it on your premises. Your team opens a chat interface in their browser, on your local network, and works the way they already work with AI: summarize this contract, compare these proposals, draft this spec, pull the numbers that matter out of this spreadsheet.
The difference is architectural, not contractual:
No third party.
There is no provider between your team and the model. No vendor logs, no outside retention policy, no terms of service that change with a website post.
No training on your data.
A cloud provider promises this in a contract. Our machine cannot do it, because your data never reaches anyone who could.
Real deletion.
Deleting a conversation on our machine is a file operation your IT controls. No 30-day retention windows, no safety carve-outs, no backups in unknown jurisdictions.
Your logs, not theirs.
If anyone ever asks what went into the tool, a client, an auditor, a court, your answer is complete, local, and short: nothing left this office.
No per-token bill.
You buy the machine once. A team that runs every contract, every proposal, and every report through it never sees a usage invoice.
We handle everything: hardware, model installation, security configuration, on-site setup, team training, and ongoing support with a one-year hardware warranty.
What this does not do
We sell control, so we will be precise about its limits.
A local AI does not replace your security program, your NDAs, or your access policies. It removes one specific and growing exposure: the third-party record that public AI creates every time someone types.
It does not make careless behavior impossible. Someone determined to use a chatbot on a personal phone still can. What it changes is the default: when the approved tool is genuinely better, the shortcut loses its appeal, and that is what actually dries up shadow AI.
And for frontier research on public knowledge, top cloud models still lead. The work your company cannot send to them, on your own documents and data, is exactly the work this machine exists for.
Better models will come. Your company already owns the platform.
Cloud AI vendors retire models, change behavior, and reprice without asking you. If your workflow depends on their tool, every one of those decisions is operational risk you do not control. On your own machine, the version your team validated stays exactly as it is until you decide otherwise.
When stronger models are released, we install and tune them on the machine you already own, as routine work under the maintenance plan. Your investment gets better with time instead of becoming outdated, and the knowledge built into your workflows stays inside the company.
And when your team wants more than the standard interface, we build on top of it as separate projects: automations for your document types, integrations with the systems you already run, routines that turn a repeated task into one click.
Questions owners and operators ask us
We already prohibit public AI at the company.
So does almost every company we talk to. The prohibition works on paper and fails on phones. In a major industry privacy survey, nearly half of employees admitted entering non-public company information into public AI tools. The companies that actually control AI usage are the ones that provide an approved tool people prefer to use.
We have an enterprise AI contract. Isn't that enough?
Enterprise agreements are real and worth something. They also do not change three facts: your data still physically sits with a third party; every retention policy carries an exception for legal holds you are never warned about in advance; and a breach of the provider or one of its vendors ignores the contract entirely. A contract changes liability, not physics. A machine inside your company does not need the promise, because there is no third party to make it.
Is a local model as capable as ChatGPT?
For frontier research tasks, top cloud models still lead. For the work your team does every day with sensitive material, analyzing documents, comparing proposals, drafting specs and reports, working with your own data, current open models running on serious hardware are more than sufficient. We demo the machine on your own documents so you judge it on your work, not our claims.
We don't have IT staff to maintain a server.
That is what the support plan is for. You are not buying a box, you are buying a running system with a fixed, known support fee. The alternative is not zero maintenance; it is outsourcing maintenance to a vendor who also keeps your data.
How much does it cost?
It depends on team size and how many people use it at once. You buy the machine once and usage is unlimited: ten documents or ten thousand, same cost. It pays for itself against per-seat AI subscriptions, and the budget line is predictable, which no subscription can promise. Exact figures are presented in a specific proposal.
See it before you decide anything.
Bring us documents like the ones your team actually works with. We will show you the machine handling them, live, with the network cable in your hand if you want it.
No data leaves the room. That is the whole point.