Engineering

We define the architecture, the contracts between services and the path to production before we start building. The application is born with state outside the process, configuration outside the code and deploys without a maintenance window.

We move existing applications, in stages, to an architecture that can take the next step in volume. Changes ordered by gain, without stopping the current operation.

We use coding agents every day and put everything through senior review before merge. The speed comes from the tool. Accountability for what runs in production stays with whoever reviews.

The pipeline and the Terraform go into the first commit, together with the application. An environment that can be rebuilt from scratch never becomes a dependency on one person or one console.

Format, versioning, authentication and what happens when one side changes, defined before the systems are connected. REST, FHIR, HL7, events or files, whatever the other side accepts.

Legacy with no API, a shared database, a file on a network share. We put an integration layer in front instead of touching what nobody wants to touch.

Queues and events when the volume or the downtime of one side cannot block the other. Reprocessing and idempotency designed from the start, not after the first duplicate.

What went out, what arrived, what failed and why, recorded and visible. Integration without a trace becomes a support ticket without an answer.

Accounts, identity, network, security and governance defined before migrating. We migrate in waves, each with acceptance criteria and a way back. On AWS, with AWS Organizations, Control Tower and IAM Identity Center.

Every change to the environment goes through reviewed code and up the same pipeline, from development to production. A gap between what is in the repository and what is running is detected, not discovered.

Metrics, logs and traces with OpenTelemetry; SLIs and SLOs agreed with whoever answers for the system; incident response with defined roles and blameless review. An alert only exists if someone acts when it fires.

Cost by account, environment and product, with an owner. We cut the waste and leave behind budgets, alerts and periodic review, so the spend does not come back the next cycle. Usage commitments only over consumption already observed.

Account segregation, least-privilege access and an audit trail designed with LGPD and ISO 27001 on the table, before anyone asks for evidence.

We pull data from the systems the company already operates, including third-party systems and SOUL MV, through APIs, databases or files. Automated loads, with a trace of every run.

One place, with the data cleaned and versioned. Business rules written once, not in every spreadsheet; access by role, with LGPD on the table.

The indicators the operation and the leadership follow: occupancy, queues, billing, cost. Refreshed automatically, with nobody assembling the number by hand.

When a system is replaced or implemented, we carry the history over without losing or corrupting it: mapping, validation and reconciliation before the cutover.

Quality, catalog and access permissions resolved before a model is connected to the data. It is what separates a pilot from a product.

We connect models to the systems and data the company already operates, through the APIs it already has. The model reaches internal data through tool use and MCP, with the permissions of whoever asked; no copying databases into the AI.

An agent running in production needs an identity, minimal permissions, state, an execution trace, a spending limit and a way to be interrupted. We build with all of that from day one, not after the first incident.

Before scaling, there is a set of real cases with the expected answer, written with the people who use the system. Changing prompt, tool or model without running that set is guessing.

We bring coding agents into the client’s engineering flow, with review, tests and control over what reaches production. The team leaves able to design, version and evaluate its own agents.