Solutions / Role

AI / ML teams

Researchers and agents sharing hypotheses, runs, evidence, reviews, and decisions from experiment through reproducible record.You build the agents. What slows you down isn't the model — it's everything around it: identity, secrets, deploy, observability, and, in a regulated shop, the security review waiting at the end. Visca supplies the surround as one stack: your agent inherits identity, scoped credentials, an in-perimeter runtime, and an audit trail by construction, so it arrives at review with the answers instead of a remediation list. And the stack's own operators run it after you ship, so production doesn't become your second job.

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People and AI collaborating in a governed ai / ml teams workspace

Why this work needs a system boundary

Shared AI work is useful only when authority, data, and consequences remain controlled.

The operational surround is a second project

A working agent in a notebook is 20% of the work. The other 80% — identity, secrets, deploy, monitoring — is a different stack you didn't sign up to build.

The security review arrives at the end

You build for months; then the review asks who is this agent, what can it touch, where is the record — and the framework you chose has no answers. Remediation eats the launch.

Production becomes your pager

Once it ships, the model server, the secrets, and the pipeline need patching, rotation, and incident response — and in most teams that lands on the people who built the agent.

One shared workspace, not scattered agent threads

The collaboration and control plane around every participant, model, tool, and action.

01Identity

Your agent is a principal from day one

Identity isn't an integration you bolt on before launch. Everything you build acts as a named principal from its first run — the review's first question is answered before you start.

02Credentials

Scoped access, not secrets in env vars

The agent requests scoped, time-bound credentials per action. Nothing standing to leak from a notebook, a config, or a CI log.

03Runtime

Production-shaped from the start, in-perimeter

Budgets, fallback, and durable execution are handled by the runtime instead of your application code — and prompts, context, and outputs never leave the perimeter.

04Audit

Every run on the record

Every run is recorded against the agent's identity and queryable. Debugging and the security review read the same trail.

05Operators

You ship it; the stack runs it

The stack's own operators deploy, upgrade, patch, rotate credentials, and answer incidents. Your job narrows to three things: declare intent, sign approvals, hold the kill-switch.

What the institution gets

Durable control without giving up capability.

Skip building the operational surround — the stack supplies it
Arrive at security review with the answers, not a remediation list
Keep prompts, outputs, and data inside the perimeter
Hand production operations to resident operators — recorded

In practice

From prototype to approved

A team builds a document agent. From its first run it acts as a named principal with scoped, time-bound credentials, and every run lands on the record. When it's ready to ship, the review takes one query, not a quarter: who it is, what it touched, where the record lives. After launch, the resident operators carry the deploys and patches — and the team's job narrows to intent, approvals, and the kill-switch.

Give AI a place on the team—and keep it under your control.

Give every AI a persistent identity, presence, and shared workspace. Collaborate live through our cloud or entirely on infrastructure you control.

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