We put AI to work inside your company: in your data, in your systems, with your team.
You have already decided AI is going to work in your operation. What is missing is someone who knows how to get it running and answers for it every day.
Some of the companies that trust our work.
Everybody has tested AI. Almost nobody has put it in production.
The test is the easy part. What stalls is everything that comes after it.
The data is not ready
AI answers on top of whatever it finds. If what it finds is spread across five systems and three spreadsheets, it will answer wrong with a great deal of confidence.
Nobody answers when it is wrong
A pilot gets something wrong and becomes an in-house joke. A production system gets it wrong and somebody has to know why, how long it took, and what changes so it does not repeat.
The bill at the end of the month is a surprise
A model charges by usage. With nobody measuring, the cost shows up once it is already a problem.
We come in at three places.
In your data
We organize the base the AI is going to use. Without it, the model gets things wrong and nobody notices until it blows up in production.
In your systems
The AI goes into the ERP, the CRM, the spreadsheet, the support channel. It runs inside what your company already uses, with no change of tools.
With your team
Someone on your team learns to operate it, review it and correct it. We hand it over working and documented, not as a dependency.
Where a mistake is expensive, the arithmetic has to stay arithmetic.
Every number carries its source. When data comes from more than one place, every line says where it came from. Without that, nobody can audit a result afterwards.
Anything that is a calculation stays deterministic. Scores and sums stay tested. The model writes the text around the number, never the number. Both paths are compared before anything reaches production.
A person approves before anything goes out. You choose what needs a human approval and what can go out on its own.
Sensitive data does not leave your environment. In the hospital, the integration runs off a mirror table inside the client and the patient identifier never crosses the boundary.
A construction credit platform, with 44 agents for credit, market analysis and chart generation.
Moonxi built it and runs it: the credit and risk agents, the market-analysis and chart-generation agents, the proprietary real-estate credit model, the machine-learning engine for probability of default and cost overrun, the data pipelines and the backend, on AWS with infrastructure as code. A continuous engagement, not a closed project.
Clinical decision support running inside the hospital.
Classification models in production, wired in through the client's own medical record, with no identified patient data moving off site. The identifier never crosses the boundary of the hospital's environment.
A crypto on-off ramp: the real goes in and out through the bank.
Buying and selling crypto with money moving in and out in reais. Moonxi built and runs the backend, the banking and liquidity integrations and the infrastructure on AWS. Anything that is arithmetic — quote, fee, balance — stays deterministic and tested.
AWS partnership Your company's cloud, with a smaller bill and a bigger structure.
Moonxi is an AWS partner. We can get credits for your company, stretch out the payment of the bill and cut operating cost — and we set up security, scalability and resilience with DevOps, SRE and platform engineering.
AWS credits
We apply for credits for your company inside the partnership programs, and we get them.
Full guide →Stretched payment
The cloud bill comes off the credit card and goes onto a payment plan your cash flow can carry.
Lower operating cost
We measure what runs, switch off what does not need to run, and size the rest. The bill is auditable line by line.
Security, scale and resilience
Set up with DevOps, SRE and platform engineering. The system can take growth and it can take failure.
What each one is →Five fronts. The one at the bottom comes first.
Build, software development and the dedicated squad are delivered in the Forward Deployed Engineer format: the engineer works from inside your operation, not from a report about it.
Data and cloud on AWS
Pipelines, how the data is organized, security and cost. This is where most AI projects die before they start, and it is the one front that pays for itself even if you never do any AI.
01 · comes firstDiscovery and architecture
Where AI applies in your business, what the architecture is, what it costs to run per month, and what happens when it gets something wrong. You get a document and a plan.
02Build
The system in production: models, agents, retrieval over your own data, integration with what the company already uses. It ships with documentation and an operating manual.
03Software development
Web systems, backends, integrations and custom digital products, built with the same engineering as the AI fronts. It reaches production with documentation and an operating manual.
04Dedicated squad
An assigned team that builds and operates alongside yours, month after month. For companies with a continuous AI front rather than a project with an end date.
05How the rollout happens.
Diagnostic
We look at your data, your systems and your team, and map where AI goes first. Two weeks.
Architecture
We draw where it goes, what it talks to, and what it will never touch.
Build
We build inside your own system, with your team reviewing every step.
Operation
It goes live working. Your team runs it. We watch it and fix what gets it wrong.
What we do not do.
We do not build a new system from scratch when what you already run solves it.
We do not deliver a handsome prototype nobody can operate afterwards.
We do not sell a tool subscription. If what you need is an off-the-shelf tool, we say so in the diagnostic.
We do not let AI decide on its own where a mistake is expensive.
It starts with a diagnostic.
Two weeks. We come in, look at your data and your systems, and come back with what can genuinely be automated, what cannot yet, and what each part costs. You get a document and a fixed quote. Not one line of code.
Book a diagnostic →The questions everybody asks.
Do you replace the systems we already run?
No. The AI goes into what you already use. Replacing a system is a different kind of project, and we say so up front.
How long until we see something running?
The diagnostic takes two weeks. When the first front reaches production depends on what the diagnostic finds, and the date is fixed in the quote.
Does our data leave the company?
It depends on the case, and you decide. In the hospital, identified data never leaves the client environment. That is the standard architecture whenever the data is sensitive.
Which cloud do you work with?
AWS. It is where Moonxi operates and where the bill is auditable line by line.
What happens when the AI gets it wrong?
Someone approves before an action goes out, and every number carries its source. When it is wrong, you can find out why the same day.
Does our team need to know how to code?
No. Someone on the team needs to want to operate it, review it and correct it. We train that person.
What does it cost after the diagnostic?
The quote is fixed at the end of the diagnostic, front by front. There is no price list because no two projects are the same.
What size company do you work with?
Companies above R$ 1.5 million in revenue a year — reais, the Brazilian currency. Below that the numbers do not work for either side, and we say so in the first conversation.
