Expansion
Every new location, evaluated before signing.
One financial model for every candidate location: score, signal, NPV and payback before committing the investment.
Common use cases.
Score and signal per location
Go, Conditional go or No go with clear criteria.
Expected result
[X] locations evaluated
NPV and payback
Each location's financial case with the same model.
Expected result
[X] months average payback
Comparison
Locations side by side to decide with data.
Expected result
[X] decisions per month
Overview map
Every location and context layer on one map.
Expected result
[X] data layers
Evaluation audit
Who evaluated what, with which assumptions and when.
Expected result
100% traceable
Every solved case teaches the next.
Every agent works on the same company context and records what it decides. What it learns in one process makes the next more accurate: connections, permissions and memory are already there.
Why Adopt.ia.
In production, not a pilot
We start from agents already running at companies like yours.
Connected to what you use
ERP, banks, SAT, CRM and spreadsheets. What works stays; what doesn't goes.
A pair that understands business
An AI engineer and a business operator accountable for the result.
How we work.
- 1
Build
We adapt the agent to your process, your data and your rules.
- 2
Test
We test it against real cases before it touches your operation.
- 3
Deploy
We put it in production and measure it every week.