IAAAM · Identity, Access, Agent & Analytics Management

JOVIS

The Middle Manager

Govern the actor — human or agent. Every model call, tool invocation and row of data an agent reaches is authorised against a policy the model cannot influence, then recorded against both the person who asked and the agent that acted.

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Watch the Film

Your infrastructure: Self-hostedYour infrastructure
Not just roles: PolicyNot just roles
On every event: Dual-identityOn every event
Data egress: ZeroData egress

The JOVIS console showing a tenant dashboard with counts for users, roles, groups and items, a recent-users table, quick actions and a panel of model calls that errored.

The Full Walkthrough

One Live Deployment, End to End

Identity, access, agents and analytics, recorded against a running system rather than a mockup — including the moment an agent is refused.

How It Governs

Four Screens, One Argument

An agent configuration page showing its instruction, prompt chain, an ordered two-model chain, and an Authority panel set to act as itself.

An agent is a principal, not a workload

Armed, it acts on its own authority — not the authority of whoever triggered it. Replacement, not addition.

Deployment

Inside Your Perimeter

Identity, policy, the vault, the audit trail, the agents and the data all sit inside your network. Point it at a model on your own hardware and the last thing that left stops leaving too.

Sovereign

The platform runs inside your perimeter, on your infrastructure and your provider accounts. Prompts, completions, tool output and database rows never leave your network.

On-premise, private cloud or air-gapped
Bring your own provider keys — always
Point it at a local runtime and nothing leaves at all
No vendor holding your prompts in their tenancy

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Responsible

Authority is enforced by the platform rather than requested from the model. Guardrails, approvals and the audit trail sit outside the conversation, where nothing said inside it can reach them.

Policy the model cannot influence
Denied-topic guardrails on both sides of a turn
Human approval before a discovered tool is callable
Dual-identity audit on every event

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Sustainable

Right-size the work rather than the cluster. Model chains put the cheapest capable model first, and every call is metered, so the bill has an owner before it arrives.

Ordered chains, cheapest capable model first
Token and cost accounting per tenant and per agent
No idle serving infrastructure to keep warm
Usage metered correctly across a client disconnect

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Ecosystem

Bring Your Own Everything

Your provider accounts, your databases, your tool servers. The platform is the gate they all pass through, not a place any of them move to.

Model providers

Anthropic
AWS Bedrock
Groq
NVIDIA
Ollama (self-hosted)
OpenAI-compatible endpoints

Your data

Read-only connections
Schema allow-lists
Statement timeouts
Row caps
No raw SQL

Agent tools

Datasource queries
Built-in tools
Registered REST calls
MCP servers, pinned
Human approval to enable

Where it runs

On-premise
Private cloud
Air-gapped
Kubernetes or Docker
Your provider accounts

Licensing

The Meter Never Starts

Licensed per deployment — not per request, and not as a percentage of what you spend. Your bill does not grow because your AI programme succeeded.

No fee per request

Volume is between you and your model provider. The platform takes no cut of it and no toll on the way past.

Bring your own keys

The discount you negotiated with a provider stays yours. Nothing here is priced against a credential you already own.

One vendor, not three

Identity, the agent runtime and the tool plane are the same product — not a gateway, plus an IAM, plus authorisation you wire yourself.

Before You Ask

The Questions That Come Up First

Where does it run?

Inside your perimeter — on-premise, in your private cloud, or fully air-gapped. It uses your infrastructure and your provider accounts, so prompts, completions, tool output and database rows never leave your network.

Which models can it call?

Anthropic, AWS Bedrock, Groq and NVIDIA directly, Ollama for models on your own hardware, and any endpoint that speaks the OpenAI-compatible wire format. Chat models today. Models are configured per tenant and tried in an ordered chain.

What happens to our provider keys?

They go into the credential vault, encrypted at rest, and are never readable back once written — not by an operator and not by a model. Bring your own keys is the only mode: the platform never resells inference or takes a percentage of it.

Can an agent reach our database directly?

No. A datasource is described once — read-only at the connection, an allow-list of schemas, a statement timeout, a cap on rows — and the agent gets derived tools rather than a connection. No connection string reaches the model and no raw SQL is accepted back from one.

What stops an agent doing more than it should?

An armed agent acts on its own capped identity rather than the authority of whoever triggered it. Every tool call is checked against its allow-list, then against policy the model cannot influence, with arguments validated before execution. It can never out-reach the group that owns it.

How is it licensed?

Per deployment. Not per request, and not as a percentage of what you spend — so the bill does not grow because your AI programme succeeded. Volume stays between you and your model provider.

See it govern your agents.

A working session against your own policies and your own data — including the part where an agent is told no. Thirty minutes.

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