Data & AIbuilt for production.
From data foundations and governance to scalable architecture, models and intelligent automation. We build systems that enter real operations and move measurable business metrics.
From data foundations and governance to scalable architecture, models and intelligent automation. We build systems that enter real operations and move measurable business metrics.
Where mistakes are expensive, they trust Garre
150+ projects delivered across telecom, financial services, agribusiness, retail, manufacturing, healthcare and government — in Brazil and abroad.
We integrate scattered sources, structure and govern the information, build scalable platforms and apply AI on a foundation you can trust. AI starts before the model: it starts with data quality and structure.
Organised data, applied AI and results measured in the real operation. Different sectors, same discipline.
Agribusiness · core operation
−40% response time
Context and operational data feed a multi-agent platform with hybrid LLMs, online and offline. Agronomists across Brazil gained contextual support even on unreliable connectivity.
Technical proof: Data integration · Hybrid LLMs · Multi-agent · Offline operation
Telecom · legal
−85% review time
Documents previously treated as isolated files became a structured, searchable base. OCR, NLP and LLMs extract, classify and surface expiry dates and contractual risk ahead of time.
Technical proof: OCR · NLP · LLMs · Snowflake · Databricks · Semantic indexing
Telecom · people
24/7 in operation
Voice reports are turned into structured information, classified semantically and routed according to handling and confidentiality rules.
Technical proof: Voice · NLP · Semantic classification · Workflow · Confidential escalation
A good model is only part of the solution. To actually operate, AI needs quality criteria, tests, observability, guardrails, traceability and a clear path for exceptions.
Before AI goes to production, we define what a good answer is, what counts as an error, and how each case will be tested. With no written criteria and no test set, validation becomes impression.
Data shifts, context shifts, and model behaviour shifts with them. That’s why monitoring isn’t an add-on: it’s part of the architecture, from the design stage.
Not every output should reach the user or the process automatically. Approval, blocking and escalation rules are part of the solution — not a later adjustment.
Go-live doesn’t close evaluation. It’s when the system starts meeting real data, real exceptions and real impact on the operation.
An AI project doesn’t start with the interface. It starts with how the decision will be evaluated, contained, observed — and who owns it when it fails.
We don’t start with the agent. We start with the process, the data, the rules and the systems it needs to understand. On that foundation we build agents that execute with context, control and traceability.
Triage, response and routing with the full customer history. Cuts the queue without turning support into a maze of menus.
Lead qualification, proposal drafting and automated follow-up. Your rep walks into the meeting with the homework already done.
Contract review, policy checking and deviation alerts — with an audit trail for every decision the agent makes.
Entity extraction, semantic indexing and predictive alerts for expiry, renewal and contractual risk.
Document checking, reconciliation and data entry — the repetitive work that eats your qualified people’s time.
Real-time decisions for field teams, even with no connectivity, using hybrid online and offline models.
Don’t see your process here? Tell us what’s stuck. If it isn’t a fit for an agent, we’ll say so on the first call — talk to a specialist.
Most AI projects don’t die because of the model. They die in committee, when someone asks where the data runs, who signed off on it and how it gets audited. Here that comes in on week one.
Cloud, hybrid or on-premises, according to technical and regulatory requirements. The architecture is designed to respect your perimeter and your policies.
Auditable trails of decisions, executions and relevant changes. Access controls and approval mechanisms built into the architecture, according to the client’s regulatory context.
Every agent has its own access scope. No change in behavior goes live without a recorded approval.
Multi-cloud and multi-model architectures cut unnecessary lock-in. Switching model, cloud or vendor should be a business decision, not a six-month project.
Standards and controls we apply on projects, matched to each client’s industry and requirements.
Some consultancies hand you a strategy and walk away. Some vendors execute without understanding the business. Garre does both — from diagnosing your data to the agent running in production, with governance and security at every step.
Every project starts with the business problem, never with the tool of the month. If AI isn’t the right answer, we’ll tell you before you spend a cent.
We work with proprietary and open models, across different clouds and architectures. The choice is made on technical fit, security, cost and business context — not on vendor commitment. Including ours.
MVPs in weeks, because we don’t start from scratch: we run on delivery machinery built across 150+ projects. No twelve-month build before the first result.
Telecom, agribusiness, healthcare, financial services, government, retail and manufacturing. What we learn in one shortens the path in the next.
Over 200 certified specialists, at home in regulated environments, tangled legacy systems and audit teams looking over their shoulder.
Security, data protection and traceability come in on week one, as a requirement — not as a patch the night before go-live, when it’s already too late.
Technical leadership close to delivery
Strategic projects are overseen by senior professionals in architecture, data, AI and governance — from framing the problem through design and into production.
Specialist squads that walk into complex environments to build, integrate and ship into production — without months spent forming a team from scratch. You don’t buy hours, you buy delivery capability.
Capabilities
Engagement formats — targeted reinforcement, fixed scope or dedicated squad — defined after the problem, not before.
What sustains delivery: track record, principles and the technology ecosystem we operate across.
To deliver technology and digital transformation with excellence, so that every client relationship is built on trust and made to last.
To be recognized worldwide as the best in Data & AI technology solutions.
Passion, innovation, transparency, gratitude, flexibility, quality and respect.
Technology partners
Founded by professionals with over 30 years of careers in technology. We have been delivering in production since before ChatGPT existed — here the solution runs in the client’s real environment, it doesn’t die on a slide.
First place at the Oracle Big Data World Hackathon, against teams from around the world.
Over 200 specialists serving clients in Brazil and abroad.
Proven experience across sectors
In one conversation we assess maturity, bottlenecks, available data and the opportunities with real potential to move the operation.
