Your automations work. They do not work together.
Automation is an infrastructure problem before it is an AI problem.
Mezza comes from finance, where every number has to be explained. So we build the layer underneath first: your systems connected, your data reconciled once. Then the automation is embedded in it, and a person approves what matters.
Every automation was a good idea on its own.
Each one solved a real task for the team that built it. Nobody was asked to make them agree with each other.
No shared data
Each automation keeps its own copy of the records it needs. The copies drift apart. Two reports on the same question return two answers.
No one watching the gap
Every automation has an author. The gap between two of them has none. A failure there is found by whoever needed the result.
No record
A step can fail quietly. When someone later asks what happened, the answer is pieced together from memory.
None of these gaps belongs to one automation, so no single task pays to close them. Adding a model does not close them either. It reads the same inconsistent copies and answers with more confidence.
Automation is the third thing we build.
Embedded means every automation has a fixed place. It reads from one layer, runs in the next, and stops at a person.
Every tool keeps its own version.
Agents read each item.
One data layer takes out what matters.
Workflows that stop at a person.
Your systems
The work starts with the tools you already run. We connect them and record where each field comes from. The aim is that no workflow depends on an export someone has to remember.
One data layer
One governed set of records, reconciled from those systems. When two of them disagree, the difference is surfaced and settled once, in the layer, not patched inside each automation.
Automated workflows
Each automation is a sequence of steps that reads from the data layer. Some steps are plain rules. A model goes where it does a step better. When its evidence is thin, the step goes back to the data and asks again.
People who approve
A named person owns each workflow. A step with consequences stops and waits for them. What was proposed, on which data, and who approved it stays on the record. When a customer, an investor or your board asks why, you read the answer.
How far a workflow may act alone
After launch a running workflow is watched. A break in the data or a stalled step raises an alert with its cause attached.
Built in finance, where an error is expensive.
Finance is where Mezza started and where it still focuses. A wrong number there can be a loss, and a named person answers for it. The method we built for that applies to any company that runs on data.
Startups
You have few systems and little history. That makes this the easiest moment to put one data layer under them, before each new tool arrives with its own version of the customer.
Mid-sized companies
You have years of tools, each added for a good reason. Automating across them before they agree only moves a wrong figure faster.
You know your industry. We do not claim to. We bring the method, and the judgment stays with your people.
Start with the map, not with another tool.
The first stage is Advise: a written plan of what to connect, what to automate, and what to leave manual. The four stages are the same ones we describe for financial institutions.
Bring the automation that breaks most often.
Or the process you would not trust to a script. Describe it and the tools it touches. We will tell you what has to be true of your data before it can run, which steps should stay with a person, and where we would start. If it should not be automated yet, we will say so.