Garre Solutions
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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.
An AI pilot is not AI in production. When the team has done the training and nothing has gone live, the gap is rarely the model: it's data, integration and governance. That's where mentoring and training come in — talk to a specialist.
Data and AI applied to real operations, with measurable gains in efficiency, capacity and economic return.
Agribusiness · field operations
Operational data, technical knowledge and AI now work together in a multi-agent architecture that holds up even where connectivity is limited — expanding the capacity of distributed teams without a proportional rise in headcount.
Technical foundation: Data integration · Hybrid LLMs · Multi-agent · Online and offline operation
Telecom · legal
Contracts and documents once scattered across systems became a structured, searchable base — with OCR, NLP, semantic search and language models freeing specialist capacity for risk, negotiation and decision-making.
Technical foundation: OCR · NLP · LLMs · Semantic search · Snowflake · Databricks
Manufacturing · compliance
Voice reports became structured information, classified and routed automatically by severity and handling rules — widening access across shifts and plants without growing the operation proportionally.
Technical foundation: Speech-to-text · NLP · Semantic classification · Workflow · Confidential handling
Financial services · payments
Data, automation and AI now span capture, routing, reconciliation and exception handling at scale — the team shifted from working the volume to working the exceptions, with no linear rise in OPEX.
Technical foundation: Automated reconciliation · Transactional integration · Exception workflow · Automation · Operational supervision
Economic impact, return and payback are reference estimates, derived from productivity gains, operational capacity freed, automation and avoided costs. Actual results depend on the volume, scope and structure of each operation.
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 behavior 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.
AI projects can work technically and still stall in committee, when the questions come up about 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 solution 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 requirements.
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 turn data into operational capability through engineering, artificial intelligence and governance — with technical excellence and lasting relationships.
To be a reference in enterprise Data & AI solutions that reach production.
Passion, innovation, transparency, gratitude, flexibility, quality and respect.
Technology partners
Founded by technology leaders with over 30 years of industry experience. 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, among 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.
