The Optimizer

The Optimizer knows what matters today.

Most AI agents optimize their own metric. Businesses do not grow that way. Glaium coordinates every agent around the one constraint that limits the whole system, so effort goes where it pays.

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The idea

Improve the bottleneck. Everything else waits.

Theory of Constraints (TOC) is a management method built on a simple observation. A system moves only as fast as its slowest step. Improving any other step adds cost and no output. Glaium applies this idea continuously, with live data, across every part of a business.

For a mobile business, the Optimizer reads five growth systems every day: Acquire, Activate, Retain, Monetize and Financials. It flags which one is holding the others back.

From goal to action

Start from the goal. Work backward.

You set the business goal. Glaium cascades it backward into operating targets for each part of the system: how much to acquire, what to retain, what to earn. Agents work against those targets, so every local decision traces back to the goal you set.

How a decision is made

Many agents. One decision.

Each agent owns one part of the business and analyses it. The Optimizer reads every analysis against the full picture of the business and decides what today's priority is. You get one recommendation with its reasoning, never a list of agents competing for attention.

The architecture is hybrid. A quantitative engine produces every number. Language models explain, reason and compare. Every figure in a recommendation traces back to the engine. Nothing is invented.

1 2 3 4 5 6 Measure Diagnose Prioritize Decide Act Learn EVERY DAY one decision
  1. 01Measure: data from every source, reconciled. This is the data layer.
  2. 02Diagnose: the binding constraint, its score, and the confidence of the read.
  3. 03Prioritize: the levers that relieve it, ranked by dollar impact.
  4. 04Decide: one recommendation, or a deliberate hold.
  5. 05Act: a person makes the change, or approves Glaium making it.
  6. 06Learn: the real outcome is measured against the prediction.
The daily card

What you see each morning.

One card. One decision. Everything you need to act on it.

  • The action, stated plainly.
  • Why this one, and why the other agents can wait.
  • Expected impact in dollars.
  • Who acts: Glaium, or you.
  • Trust level: whether the size of the move is precise or directional.
Today's recommendationPuzzle Harbor (fictional)
Constraint: Acquire
Shift 15% of budget from Campaign A to Campaign B

Acquisition is today's constraint, and this is the largest lever on it. Retention and pricing are stable this week, so they wait.

Expected impact
+$10,000 / mo
Who acts
Glaium can make this change
Trust level
Precise
Waiting (3)
  • Lifecycle agentRetention is stable. It is not today's constraint.
  • Pricing agentThe current test needs more data before it can be read.
  • Ad monetization agentSmaller impact than today's top move.

Illustrative data. Fictional app and round figures.

Governance

Autonomy you can switch off.

Glaium only recommends until you decide otherwise. Automatic changes are off by default.

Automatic changes: off by default
Shadow mode
The Optimizer records what it would change and writes nothing.
Hard bounds
Every lever has a minimum, a maximum and a maximum step per run.
Kill switch
One control stops every automatic change.
Trust gate
When a lever has not been measured enough, the card says so and asks for explicit acknowledgement.
Full history, fully reversible
Every change is logged, and any change can be reverted.
One engine, many businesses

Built for mobile first. Built to generalize.

The Optimizer works on any system that has a goal and measurable flows. Business models run on top of the same engine. Mobile apps and games are the first. Others are in development.

Glaium is native to the Model Context Protocol (MCP), so your own AI agents can read the diagnosis, the critical path and the daily recommendation directly. A public SDK lets your apps and services call Glaium.

Live
Mobile apps and games
The first business model on the engine. Built on the Glaium data layer, from every source your apps and games use.
In development
Other business models
Any system with a goal and measurable flows. The same engine, a new model on top.
MCP
Your agents, connected
Your own AI agents read the diagnosis, the critical path and the daily recommendation directly.
SDK
Your apps and services
A public SDK lets your apps and services call Glaium.
See it on your data

Find the constraint in your business.

Connect your sources. Get your first diagnosis and your first recommendation.

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