The Enterprise AI CompanyAgentified IT services
Agentified DOC · Data and AI
Every company records what happens: sales, orders, service calls, production. Turning those records into understanding requires organization and analysis.
Your information organized, with meaning and available to the people and agents who have to decide. That context is what makes the operation learn.
What is it?
It is tidying up the company’s information: bringing together what is scattered across systems, standardizing names and figures, fixing what is wrong and putting it all somewhere trustworthy.
Why does it matter?
When every department has a different number, the meeting turns into an argument about whose spreadsheet is right instead of a decision. And any AI built on bad data will be confidently wrong.
How does it work?
We move data from where it is created to where it needs to be, define who can see what, spell out what each piece of information means, and publish indicators and forecasts on top of it.
What changes?
Each question now has a single answer, available in time to inform the decision.
The question is simple. The path to the answer, today, rarely is.
Today
After
And when the pattern repeats, the operation itself can be warned before the customer walks away.
Where this fits: this is where your operation learns. Organized, meaningful data forms your company’s context, which agents consult and enrich with every case.
Each front can start on its own. Together, they take raw data all the way to business value.
01 · Foundation
The foundation AI needs: integrated, reliable and up-to-date data.
02 · Governance
Who can use each piece of data, at what quality and with what traceability.
03 · Value
Data turned into forecasts, decisions and action every day.
The pattern we use to organize data in layers, from raw to business-ready.
On the cloud your company already uses:AWSGoogle CloudMicrosoftDatabricksClouderaHuawei CloudMongoDBElastic
“Each department has a different number for the same indicator.”
“A new report takes weeks to be ready.”
“Decisions are still made on gut feeling.”
“The AI project stalled because the data wasn’t ready.”
From assessment to operations, so data supports decisions — and AI.
A secure place where the company’s data is organized, integrated and ready to use.
Also known as: Data Foundations · Lakehouse
Moving old databases and reports to platforms that grow with the company.
Also known as: Data Modernization
The business numbers in one place, with the same definition for every department.
Also known as: Data Analytics & BI
Ask the question in plain language and get the answer, without building a report.
Also known as: Augmented Decision · DashToChat
Dedicated teams that deliver data and AI products in short cycles.
Also known as: Data & AI Factory
Understanding where and how customers move, to reach them at the right time.
Also known as: Geobehavior
Models that spot suspicious transactions and behavior before they turn into losses.
Also known as: Anti-fraud · predictive models
78%
of Brazilian companies are at an early stage of data maturity
Source: Cisco/OCDE · Cloudera/HBR · S&P Global · IDC/ABES · McKinsey, 2025
73%
of companies struggle to prepare their data for AI
Source: HBR Analytic Services · IBM · McKinsey, State of AI 2025
US$ 3.4B
in AI spending in Brazil in 2026, growing 30% a year
Source: IDC/ABES
Organized data is what makes AI reliable. With the base in place, AI answers questions in natural language, spots patterns and feeds agents with correct information — and governance ensures each person sees only what they are allowed to see.
Where to start
We map where the data lives, how it is used and what is missing to support decisions and AI. The result is a step-by-step plan.
Request the assessment →The same situation seen at four levels of maturity. Only the last one changes a decision.
Data · what happened
“Machine 7 stopped at 2:02 p.m.”
Information · what it adds up to
“That machine has stopped eleven times this month.”
Context · why it happens
“Ten of them came after the raw-material batch was changed.”
Knowledge · what to do
“With this batch, the machine needs adjusting before the shift — and we know how to alert maintenance in time.”
No step is skipped, and each one delivers something usable before the next begins.
Find out what exists
Which systems hold information, who uses it, what they trust, and which questions the business needs answered.
Bring it somewhere trustworthy
We move information from where it is created into a single environment, whether once a day or in real time.
Match and clean
The same customer spelled three different ways becomes one. Errors and duplicates surface before they turn into a wrong decision.
Give it meaning
“Revenue”, “active customer” and “lead time” get a single written definition that holds across the company.
Make it available, under control
Every person — and every system, agents included — reaches what they need and nothing beyond it, with a record of who saw what.
We work with an ecosystem of partners and define, together with the client, the most suitable combination of technologies for each problem, with implementation and operations experience in each of them.
Technologies in the Semantix ecosystem. Display of partner trademarks is subject to their authorization.
Technical detail
Batch and streaming ingestion · data modeling and quality · catalog and lineage · semantic layer and governed metrics · cloud or hybrid data platform · analytics, BI and predictive models · MLOps.
We call this set data engineering, data governance and decision intelligence.
Frequently asked questions
Data records what happened; knowledge explains why and what to do next. Getting from one to the other means integrating sources, ensuring quality and giving numbers business meaning — for example, a single definition of “active customer” across the company.
Only the data behind the process you want to improve — not the whole company at once. Starting with a concrete use case shows value sooner and avoids data projects that never end.
It is a business dictionary between the data and its users: it defines revenue, margin or churn once, and every dashboard, report and AI agent uses the same definition. The result is less debate about which number is right.
Yes, when the architecture is designed for it: data stays in the company’s environment, access follows each user’s permissions and sensitive information is masked before it reaches the model. Privacy regulation is treated as a project requirement.
This service is part of a single operating model, with AI agents and specialists on the Semantix AI Suite. Decisions made here feed your company’s shared context and make the other operations smarter.
Agents on the volumeSpecialists on judgmentFrom platform to operations
One context for your companyEvery operation feeds the same context and runs on the same foundation: the Semantix AI Suite and Brazilian governance.Explore Semantix AI Suite →Connect · Operate · Learn · Orchestrate