The platform
Automaxia Studio
Build the agents. Use what they produce.
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.
Agnostic and installable wherever you need it — your cloud, your data center or our infrastructure.
The cycle
Build and use in the same place
Most tools do one end or the other: they assemble the agent and leave you, or they show data someone else brought in. Here the two ends meet.
- 01
Build
Agents are born from prompts, not from an engineering project. Whoever knows the process describes what the agent has to do.
- 02
Run
Agents run inside the processes, with the permissions and the integrations the business requires.
- 03
Observe
End to end: every step traceable, every answer explainable, every cent of AI accounted for.
- 04
Use
The data the agents produce comes back to the team — as an answer on WhatsApp and a dashboard on screen. And what you learn there becomes the next agent.
The modules
The Console and Harvest
One is where agents are born and governed. The other is what ties the client's data to the domain, before the first agent runs.
Administration
Console
The platform's command post: where agents are born, where you see what they are doing and how much they are costing.
- Agent creation
- Observability — runs, response time and error rate
- Financial view — cost by period, by product and by model
- Users, roles and groups — and which agents each person can reach
- AI model management
Data
Harvest
The agent that opens the client's database and builds the mapping against the data contract. Groundwork that took days comes out in minutes — and the final word still belongs to a person.
- Starts from the industry data contract and goes looking for what the client's database actually holds
- Proposes the mapping field by field, with the reason behind each match
- Says what it could not find and what stayed ambiguous, instead of filling in blind
- Human validation before anything counts: nobody approves in bulk unseen
- What used to be a week of technical meetings becomes a review of minutes
In the Console
How an agent is born
Four steps, and the second is the one that matters: the agent is defined by writing in plain language what it should do. Whoever writes it knows the process.
Browse the four steps
01 / 04
Step 01
Identity
Who the agent is and what it takes care of.
- Name
- Inventory control
- Identifier
- inventory-control
- Agent type
- SQL
- Description
- Connect to the database and fetch what matters for managing inventory
Product screens rebuilt, with an illustrative example
In the Console
And once it is working
How much it cost, how much it got wrong, how long it took — and what has not been configured yet. The platform asks for good practice instead of waiting for someone to remember.
Healthy
3 of 4 axes assessed
412 ms across 1,116 runs · 7d
0.9% error rate (10 of 1,116) · 7d
credentials in the vault, 2 unrotated
monthly cap not set yet
Cost this period
US$ 128.40
US$ 96.10 previously
Runs
1,116
982 previously
Average time
412 ms
critical ones at 1.2 s
Active alerts
0
none critical
Where the cost is going
- Inventory AgentUS$ 78.9061%
- Finance AgentUS$ 31.2024%
- Support AgentUS$ 18.3015%
Opportunity
HighA cheaper model would do the job
The Support Agent uses an expensive model for simple triage tasks, at US$ 0.0135 per call.
An axis without a score is not zero: it is a gap the platform flags
Product screen rebuilt, with illustrative figures
Why it holds up
What separates a pilot from production
End-to-end observability
From the prompt that fired to the data that came out. This is not an application log: it is the full trail of the agent's reasoning.
Traceability
Every automatic decision has an origin, a version and an owner. When someone asks why the agent did that, there is an answer.
Financial view
Not just how much was spent: where the cost is going, product by product, with a suggestion of where a cheaper model would do and how much that would save.
- Cost for the period compared with the previous one
- Where the cost is going, product by product
- A cheaper model suggested, with the savings estimated
Built by prompt
Whoever understands the process builds the agent. The platform handles governance, execution and measurement underneath.
Where data becomes decision
The solutions the client uses
These are not separate products to buy: this is the Studio's consumption layer — what the 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.
- States which data source it is connected to before answering
- The generated query stays visible and auditable, never hidden
- Results as table and chart, with the type up to whoever is reading
- Ends in a suggested action plan, not just a diagnosis
- Read-only, and only what the user's profile allows
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.
- Dashboards generated from the agent's profile, with nothing to build from scratch
- Or built by you, describing in plain language what you want to see
- Manual adjustment whenever you need fine control
- An intelligent assistant inside the dashboard itself: it takes on the personality of the agent enabled for whoever is there, and suggests where to start
- From the overview down to the individual record, without switching tools
- Access controlled by group, dashboard by dashboard
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.
- A command in plain language becomes a layer on the map
- Concentration, cuts by area and comparison between regions
- The same agent answers in the chat beside the map
- On top of the geographic base the client already uses
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
The link
The agent you built is the one that answers
The assistant inside the dashboard and the chat is not a generic one: it takes on the personality of the agents released to whoever is logged in. Which agents each person can reach is decided in user management. Building and using are not two tools talking to each other — it is the same agent, on both sides.
Watch it happen
Switch the agent and the whole platform changes
An inventory agent is not a filter inside the system: it draws the inventory dashboards and becomes the inventory specialist in the chat. Pick one and see.
“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
- Days of cover, by item
- Turnover by category
- Items below the reorder point
- Seasonality over the last twelve months
Generated from what it knows
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
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
Illustrative examples of the mechanism
The difference
Where the others stop
Google AI Studio
Prompt, model and an API key. It ends where the data problem begins.
Microsoft Copilot Studio
Conversation flows inside the Microsoft ecosystem. It does not know your legacy data and computes nothing auditable.
Generic canvases (n8n, Flowise…)
A kit of nodes for the client to assemble. ~90% dies in the pilot: data, workflow and governance stay with you.
Automaxia Studio
Builds, runs, observes and hands the data back to the team — in the same place, under the same governance.
Deliberately opinionated
The foundation
What holds every solution up
Four things none of them can do without — and that have to exist before the first agent, not after.
01
Governance from day one
Single identity, permissions by business area, an encrypted vault for credentials and a full audit trail — from day one, not as a future project.
02
Data onboarding with Harvest
Harvest deciphers legacy databases — cryptic column names included — builds the mapping against the data contract and proves every field against real data. Groundwork that took days comes out in minutes, and a person approves before it counts.
03
Permission enforced at the data
The agent sees only what the asker's profile allows — and always in read-only mode. The barrier sits in the database, not in the interface.
04
Answers that carry proof
Every number comes with its origin, the path it took and a confidence seal. When the data does not support the answer, the platform says so.
Vertical Packs
How an industry becomes a product
Data contract + domain rules + the industry's agents and prompts + canonical dashboards. A pack is an installable vertical.
01
Data contract
The industry's fields, rules and requirements — what Harvest goes looking for in the client's database.
02
Domain rules
The decisions the industry makes, with an explainability score.
03
Agents and prompts
The language and the knowledge of the domain.
04
Dashboards
The industry's canonical dashboards.
The pack says what to look for; Harvest finds it in the client's database in minutes, and what used to be a week of groundwork becomes a review. What the AI proposes, the specialist approves.
No lock-in
Agnostic about model and about place
SQL generation can use one model, the analysis another, the mapping a third — configuration in the catalogue, not code. And the platform runs wherever your data policy says.