Week 1: scope & success criteria
One use case, the data it needs, and the measures that define success, such as accuracy, time saved, or cases handled, written down and agreed.
Solutions · AI & Automation
Before you commit a full budget to an AI idea, prove it on your own data. Symetriz runs a fixed-scope pilot in your Azure tenant, measures it against success criteria agreed on day one, and gives you a clear go or no-go.
The problem
A polished demo says little about how the model handles your documents, your edge cases, and your volumes.
How we fix it: A pilot run on your own data.
Open-ended pilots drift for months because nobody agreed what success looks like.
How we fix it: Success criteria and a timebox fixed on day one.
A pilot proves accuracy but says nothing about the monthly cost of running it at scale.
How we fix it: Projected run costs as part of the result.
The solution
Three phases, each with a checkpoint where you can stop.
One use case, the data it needs, and the measures that define success, such as accuracy, time saved, or cases handled, written down and agreed.
A working solution in your tenant using Azure OpenAI, Azure AI Search, or Azure ML as the use case needs, tested against real examples.
Measured results against the criteria, projected run costs, risks found, and a clear recommendation: scale it, change it, or stop.
The pilot is yours, whether or not you continue with us.
Everything is built in your subscription and handed over with documentation, not hosted on our side.
The test set, the scores, and the failure cases, so a decision can be checked by anyone, not taken on trust.
If the answer is go, a plan for security, monitoring, cost controls, and ownership to take the pilot live.
Ready when you are
Tell us the idea and the data behind it. We will come back with a pilot scope, success criteria, and a fixed price.