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Ennexa Labs

From the problem to a system in production.

We design and build software, data, AI and automation for companies that need results running, not another diagnosis.

Describe my problemSee how we work

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Most projects fail before the first line of code.

Because the problem was framed wrong. We start with the problem, turn it into a metric, and only then choose the technology.

What we solve

01

The data exists, but nobody trusts it.

02

The team repeats the same manual process every week.

03

AI became a pilot and never reached production.

04

Media spend goes up and revenue does not follow.

05

The system works, but nobody can maintain it.

Eight fronts, one team

The areas connect because the same hands build them. Nothing gets handed off between vendors.

Five examples of what we can build

Ordinary situations, assembled to show how each solution works. None of them comes from a client: what we have already delivered is under Proof.

Choose an example

WhatsApp support

The same question, every day

Before
The same questions arrive on the company's WhatsApp, and somebody answers them one by one, copying the old reply when they remember where it is.
After
An agent answers what is already documented, on the same WhatsApp. What it does not know goes to a person, who gets the whole conversation instead of a new ticket.
  1. CustomerDo you cover my area?
  2. AgentWe do. Tell me the city and I will confirm the timescale.
  3. CustomerCan you invoice a company?
  4. AgentYes. I can open the account now.
  5. CustomerI want to revisit the terms of the contract I signed.
  6. Goes to a person

Whoever takes over sees everything said before.

Automation

Somebody retypes what arrived already typed

Before
A document arrives by email, a person reads it and types the fields into another system. Every day in the same format, and with mistakes on the busy days.
After
The fields are extracted, checked against the rules the company already has, and posted. What the rules do not cover goes to a human queue instead of being guessed.
  1. InputDocument arrives by email
  2. ReadingFields extracted
  3. RuleChecked against the rules
  4. ExceptionHuman queue
  5. RecordPosted and confirmed

The human queue is where the process stops on purpose.

Landing page

The ad promises, the page does not deliver

Before
The campaign leads to the home page, which talks about everything and answers nothing the ad promised. Whoever clicked looks, does not find it and leaves.
After
One page for each promise, with the sentence from the ad at the top and a single action. The form asks for the minimum the conversation needs to start.

Before

After

What changes is how many paths the page offers.

Custom web system

Which version of the spreadsheet counts?

Before
The control lives in a shared spreadsheet. Two people edit at the same time, a copy with "final" in its name appears and nobody knows which row counts.
After
One database, one screen per task and a record of who did what and when. The spreadsheet becomes an export, for whenever somebody needs one.

Before

After

Drawing: in the before, two rows repeat the same record and disagree in one field. In the after, each row appears once and carries an author and a time.

Data

Two people, two numbers

Before
The question arrives, somebody builds a spreadsheet and the answer comes out days later. Ask the same thing and you get a different number, because everyone works the indicator out their own way.
After
The definition of the indicator lives in one place, and the data arrives on its own. The same question gets the same answer, and when the number moves you can see why.

Before

After

Drawing: in the before, the same question leads to two different end points. In the after, it leads to one.

How we work

  1. 01

    Understand

    We map the process, the cost and the decision that needs to change.

  2. 02

    Measure

    We define the metrics and organize the data sources.

  3. 03

    Model

    We apply a model, an agent or a rule, whichever solves it with less risk.

  4. 04

    Automate

    We connect the systems so the flow runs without intervention.

  5. 05

    Ship

    We ship to production with monitoring and documentation.

Proof

  • Cost reduction in data infrastructure

    We moved the orchestration to self-managed infrastructure, keeping the analytical destination in place and the operation running throughout the change.

    60%lower monthly infrastructure cost

  • Sandy Pickle EventManager

    An event management platform with multi-currency finance, scenario simulation and commission calculated on net profit. Permissions are enforced in the database, not only in the interface.

  • GerminAI

    An application that combines geographic data, external APIs and a language model to produce a recommendation for a specific location, through an interface someone without a technical background can use.

Instrument on standby

Describe your bottleneck in four steps

No signup. At the end you get the recommended path.

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Use the arrow keys to move and Enter to confirm

Tell us the problem. We reply with a path.

Frequent questions

How does a project start?

It starts with a conversation about the problem, not about technology. We map the process, the cost and the decision that has to change, and only then do we propose a scope. If what you need is not software, we say so.

What size of company do you work with?

We work with mid-sized companies that have a concrete operational problem: an expensive manual process, disorganised data, an AI pilot stalled before production. Company size does not decide the fit. How clearly the problem is stated does.

How long does a project take?

It depends on the scope, and we quote no deadline before we understand the problem. What we do guarantee is that the deadline arrives with the proposal, not after the work has started.

Do you deliver and walk away?

We deliver in production, with monitoring and documentation, so the system can be maintained by someone else. If you want us to keep running the operation, that is a separate agreement, not a dependency built on purpose.

Do I need an in-house technical team?

No. Most projects start at companies without one. The documentation and the monitoring exist precisely so the system does not depend on whoever built it.

How do you handle the data you are given access to?

We access only what the agreed scope requires, under a confidentiality agreement, and we return or revoke that access at the end. Processing follows the LGPD, the Brazilian data protection law, with the legal basis and the purpose defined before the work starts.

Tell us the problem. We reply with a path.

Reply within two business days.

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