Automation and artificial intelligence

Less repetitive work,more time to decide.

We turn what your team repeats by hand into a system rule and apply artificial intelligence where it genuinely saves reading and typing, always with a person responsible for the decision.

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Example: AI reads a billing e-mail, extracts supplier, order, amount and due date, flags the date that needs confirming, a person approves and the entry reaches your system.
When it makes sense

Where automation usually pays for itself.

The starting point is not the trendy tool. It is the task that consumes hours of the team's time every week and does not require judgment each time.

  1. 01

    Typing and retyping

    Data that arrives by email, PDF or spreadsheet and has to be entered into another system, one by one.

    Todayretyping field by fieldWith automationthe data reaches the system on its own

  2. 02

    Manual cross checking

    Cross referencing reports, checking records and hunting for discrepancies eats up the day of people who should be analyzing instead.

    Todaychecking line by lineWith automationmismatches show up already flagged

  3. 03

    Reading at volume

    Documents, tickets and messages that someone has to read just to classify, summarize or route.

    Todayreading everything to sort itWith automationAI sorts it, a person confirms

  4. 04

    Reports built by hand

    Metrics that depend on someone pulling numbers together from different sources at the end of the month.

    Todaygathering numbers at month endWith automationthe indicator updates itself

What we delivered

What we automate, and how AI fits in.

Automation and AI are the means. The result we aim for is fewer errors, less rework and a faster response.

  1. 1Input

    • Documents

      AI powered reading and extraction

      Language models read documents and messages, extract the fields and suggest a classification for a person to confirm.

    • Customer service

      Triage and assisted replies

      The AI organizes the queue, summarizes the history and proposes a reply. Your team is still the one who responds.

  2. 2Rule

    • Workflows

      Process automation

      Business rules become system routines, with a record of who did what and when.

    • Integrations

      Systems that talk to each other

      Data is entered once and reaches the other systems through an API, with no retyping and no two versions of the same information.

  3. 3Result

    • Data

      Automatic reports

      Metrics updated automatically straight from the source, instead of being built by hand.

Inside an agent

AI works within limits we set with you.

The agent does not work on the loose. It runs inside an environment we build for your company, with the instructions, the tools it may use and the rules it cannot break. The technical name for this environment is harness. A new tool plugs into it without touching the rest.

Example of an AI agent working inside the company environment. An invoice from Distribuidora Serra Azul arrives at the finance e-mail. The agent reads the company instructions and rules, extracts the invoice data, looks up order 5190 in Google Drive and checks the amount against the purchasing spreadsheet. When it tries to schedule the payment of R$ 12.640,00, the rules block the action, because the agent does not make payments and amounts above R$ 10 thousand require approval. The block becomes an approval request to Ana, in Finance. The result is a record in the system with status pending approval, a new row in the accounts payable spreadsheet and a one-sentence summary for the team.

Across every stage

Governance

Responsible AI

Client data protected, human review at every decision point, and a record of what the AI suggested and what was approved.

How it works

We start with the task that weighs the most, not with the technology.

We measure today's effort, automate in short cycles and check that the gain actually shows up before moving forward.

See the full method →

  1. 1

    Task mapping

    We map out with the team what is repetitive, how much time it consumes and where errors happen.

  2. 2

    Prioritization by gain

    We choose together what to automate first, based on the effort saved and the risk involved.

  3. 3

    Automation in cycles

    Each cycle delivers a working automation, tested with real data and with human review wherever a decision is made.

  4. 4

    Acompanhamento

    We monitor the automation in production and adjust it whenever the process or the data changes.

The next automation on the list goes back to step 3.

Frequently asked questions

Frequently asked questions about automation and AI.

01Where should we start automating?

With the repetitive tasks that take the most hours or cause the most errors. We map with the team what is done by hand, how long it takes and where it goes wrong, and we prioritize together by gain and risk. The first automation is usually small, to prove the result with real data.

02What is the difference between regular automation and AI automation?

Regular automation follows fixed rules: when this data arrives, take this action. AI comes in when the input has no fixed format, such as emails, PDFs and messages that need to be read, classified or summarized. We use each where it makes sense, and often both in the same flow.

03Will AI replace my team?

That is not the design. AI takes on the repetitive reading and typing, and your team keeps the analysis and the decision. Every workflow we build has a person responsible for the outcome.

04Is my data safe when using AI?

We handle data protection and LGPD from the design stage: we define what can be sent to a model, anonymize when necessary and keep a record of what was processed. Sensitive information does not leave the environment without an explicit rule.

05Do I need to replace the systems I already use?

In most cases, no. We integrate by API with what you already have and automate between them. When the current system is the bottleneck, we show you the cost of keeping it versus replacing it, so you can decide.

06Our data is messy. Can we still start?

In most cases, yes. We start with the process where the data is most reliable and organize the rest along the way. Often the automation itself standardizes how data comes in from then on.

07How much does an automation project cost?

It depends on the number of processes and integrations. We start with the task mapping, which shows where the biggest gain is, and the investment is defined from there. You can start with a single automation and expand later.

08What if the AI makes a mistake?

That is exactly why no AI suggestion becomes an action without going through the agreed review point. We measure the accuracy rate with real data before expanding its use, and adjust whenever the result drops.

What task would your team most like to stop doing by hand?

You don't need a ready-made scope. Describe it your way and we'll reply with the next step.

Talk to us →Call (48) 99185-9293 →