Platform

AI infrastructure that acts, with a boundary

Model access, agents and automation for business work — governed so an agent can propose anything and commit only what it has been authorized to commit.

Problem

Model access is easy. Trusting the output is not.

Connecting a business to a language model takes an afternoon. Letting that model touch customer balances, reward rules or wallet operations is a different problem, and it is the one that stops most projects.

Teams end up building the same scaffolding every time: provider fallback, cost control, prompt versioning, an audit trail, and some way to stop an automated decision before it becomes an irreversible one.

Solution

AI assists, humans authorize

Every AI request passes through a gateway that resolves the provider, applies the organization policy, records the call and returns a result attributable to a specific agent, actor and environment.

Agents can read platform data, draft changes and prepare actions. Actions that move value, alter rules or touch customer records require an explicit approval unless an administrator has granted that agent the authority in advance.

Architecture

The path an AI request takes

  1. Request01

    Agent, actor and environment identified

  2. Policy02

    Organization limits and tool permissions applied

  3. Router03

    Provider and model selected for the task

  4. Execute04

    Tools run inside their granted scope

  5. Review05

    Privileged actions wait on a human decision

  6. Commit06

    Identity records the approval; the owning service acts

  7. Record07

    Prompt, tools, cost and outcome logged

Features

What the layer provides

Provider abstraction

One interface across model providers, so a provider change is configuration rather than a rewrite.

Task-aware routing

Route by capability, cost, latency and data sensitivity instead of pinning every call to one model.

Tool registry

Tools are declared, scoped and permissioned. An agent can only call what it has been granted.

Approval queue

Privileged actions stop for a human. The proposed change is shown in full before it commits.

Full attribution

Every call records the agent, actor, environment, tools used and cost incurred.

Grounded context

Agents read your organization data through the same permission checks a person would face.

Use cases

What businesses run on it

Reward tuning

An agent reviews reward performance, proposes a rule change and submits it for approval.

Customer support

Answering account and balance questions from real data, escalating anything that would change state.

Analysis

Turning a question about retention into a query, a chart and a written explanation.

Anomaly review

Flagging unusual issuance or redemption patterns for a human to confirm.

API preview

Asking an agent to prepare a change

agents.tstypescript
const proposal = await matinee.agents.run({
  agent: 'reward-manager',
  input: 'Retention dropped in the Berlin stores last month. Propose a fix.',
})

// Nothing has changed yet. The agent returns a proposal,
// and privileged actions wait for an approver.
console.log(proposal.status)  // "awaiting_approval"
console.log(proposal.actions) // [{ type: 'reward_rule.update', ... }]

Illustrative. The API surface is fully specified but not yet served; this snippet does not call a live service.

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