Business operations
Orthophysio
The whole operation automated: from scheduling to billing, with health insurance integration and WhatsApp.
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Agentic platform
The agent you
teach is the one
that builds and answers
AI agents that work on your own data, with a trail of everything they did and the cost in plain sight. From the first conversation to a dashboard live in four weeks.
The problem
AI arrived at companies. The results did not.
~90%
of AI agent projects die in the pilot stage
McKinsey, 2026
22%
of mid-sized companies use AI in a structured way
G4, 2026
#1
data governance is now the biggest barrier to adoption
IDC, 2026
The market bought chatbots and agent canvases. What is missing is someone who gets AI into production on top of real data.
The tool
Automaxia Studio
A complete agentic platform: the same place where agents are created and managed is where the data they generate becomes an answer, a dashboard and a decision.
See the StudioBuild the agents. Use what they produce.
What sets us apart
The agent you teach is the one that builds and answers
Teach an inventory agent: it starts drawing the inventory dashboards and answering as an inventory specialist in the chat. Switch agents and the whole platform changes with it.
01It learns
“You take care of inventory. You know days of cover, turnover, reorder points and the seasonality of every category. You warn before things run out, not after.”
Defined in plain language, in the Console
02It draws the dashboards
- Days of cover, by item
- Turnover by category
- Items below the reorder point
- Seasonality over the last twelve months
Generated from what it knows
03It answers in the chat
What is going to run out next week?
Inventory Agent
Eleven items fall below the reorder point by Friday. Three of them come from a supplier with a fifteen-day lead time — if the order does not go out today, they run out. Want me to list those three first?
The same agent, now talking
And this is how it opens on screen
TalkInventory Agent
Connected to database XYZ
What do you want to analyse today?
Ask in plain language. The query is written, run and returned with a table, a chart and an analysis.
Average cover
18days
-3 days
Items at reorder point
11
+4
Quarterly turnover
4.2x
+0.3
Idle value
US$ 1.4M
-US$ 210k
The agent suggests
Which items fall below the reorder point this week?Which categories turned over less than last quarter?Where is the idle inventory value concentrated?
Ask anything about your data…Analyse
Illustrative examples of the mechanism
Inside the Studio
How people use it
These are not separate products to buy: it is what the client's team finds ready once the agents are already working.
Ask in plain language
Talk
You ask in plain language and get the analysis, the table and the chart — with the query the AI wrote left in plain sight, for anyone who wants to check it.
A dashboard that answers back
Vision
The dashboard is built by asking in plain language, and then it answers: the AI draws the indicators from what the agent knows and stays inside to explain what they show.
The command becomes a map
Atlas
Ask in plain language and the map draws itself: layers, cuts and concentrations appear over the territory without anyone opening a GIS.
Three ways to use the same data
Asking, looking and locating. What changes is how you reach the information; what does not change is who is on the other side — the agent your team taught.
- Ask → Talk
- Look → Vision
- Locate → Atlas
No AI lock-in
The right model for each task
One model per step, several at once, swapped the same day. Cost is measured by model, by step and by business area.
GeminiGPTClaudeLlamaLocal model
While AI providers fight each other, you switch sides whenever you want — and your bill goes down.
What the platform solves
Automation stops depending on IT
The person who knows the process is the one who best knows what to automate. The Studio exists so they need no middleman.
Whoever knows the process builds the agent
No need to be from the tech team. The person who lives the process describes in plain language what the agent should do — and it exists.
No waiting in the IT queue
The team stops waiting for a project to automate what it already knows must change. The idea and the first version happen the same day.
You know what every agent did
Every step is recorded, with its origin, version and owner. When someone asks why the agent decided that, there is an answer.
The cost shows up before the invoice
AI consumption measured by agent, by step and by area. You can decide what is worth scaling without finding out the price at month's end.
The data produced comes back to the team
What the agents produce becomes an answer on WhatsApp and a dashboard on screen — without opening another project for it.
Runs wherever your policy says
Your cloud, your data center or our infrastructure. And the AI model is swapped by configuration, not by rewriting.
How we start
From data contract to decision, in four weeks
- 01Week 1
What you want to answer
We sit down with your team for a process discovery: which questions the business needs answered and where, in your databases, the data that answers them lives. That becomes a data contract, reviewed and approved by you.
Data contract validated
- 02Week 2
Harvest learns your databases
This is where Harvest comes in, our data agent. With the contract in hand it opens your databases, finds on its own what the contract asks for — including in the columns nobody ever documented — and builds the mapping field by field, with the reason behind each match. Groundwork that took days is ready in minutes; your team reviews and approves.
Mapping validated by people, with Harvest connected
- 03Week 3
The data enters the platform
What the agent found is processed and brought into the platform, with the origin of every field recorded. From here on there is a base ready to be used.
Data available, with traceable origin
- 04Week 4
Your agents take over
The business agents your team created start consuming that data automatically: they build the analyses, answer in Talk, draw the dashboard — and talk about it.
Talk, Vision and Atlas in use
Cases
Who already put AI to work
Public health
Isatech
AI models trained to identify diseases, automating triage that used to depend entirely on human reading — with the health professional confirming every case.
- Malaria
- Tuberculosis
- DIAGEST
Renewable energy
CYTEI
The management data of a platform connecting consumers, integrators and power plants — organised and ready for decisions.
Visit siteTechnology
Solve Tecnologia
Dozens of automations delivered over the course of the partnership: process automation, AI applications and custom-built apps.
Extreme risk
São Tomás
Agents that read climate and environmental risk data — extreme event forecasting, flood susceptibility, wildfire risk.
Visit siteWhere we are
Brasília, Manaus, São Paulo
The map is not a matter of scenery: it is where the work happens. Each place got its own view.
BrasíliaDF
Our home
ManausAM
Public health in the Amazon, with Isatech
São PauloSP
Where the market happens
Shall we put AI to work on your data?
Four-week proof of value · success criteria defined together