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AI Agents for enterprises in Sao Paulo and Brazil

On your infrastructure. Under your control.

Airbus Volkswagen Shell Renault Evonik Vattenfall Philips KPMG

Sao Paulo is Latin America’s largest business centre - and home to our regional office. Enterprises such as Itau, Bradesco, Natura, and Ambev face the same challenge: deploying AI that is auditable and regulatory-compliant. Brazil’s LGPD requires control over personal data, and PL 2338/2023 will define additional transparency requirements for AI systems. Worker representatives (unions) participate in the introduction of systems that affect work processes - our agents are architecturally prepared. From our Sao Paulo office, we support enterprises across Brazil.

Why do most AI projects fail?

Not because of technology – but because of missing governance. Without clear rules defining who makes which decision, every AI agent stays a pilot project.

That is why we build every agent exclusively with a Decision Layer. It breaks down every business process into individual decision steps and defines for each step: human, rule engine, or AI. No agent goes into production without this layer.

Decision Layer in detail →

Three agent types for your department

Document Agents

Understand documents through real language comprehension. Recognition of type, content, and context – not template matching. Every extraction verified through the Decision Layer.

Document Agents in detail

Workflow Agents

Steer business processes across multiple systems and decision points. One agent, complete orchestration. Every step in the audit trail.

HR AI Agents

Knowledge Agents

Answer questions from enterprise knowledge – with source reference, rule version, and validity date. No verified source, no answer.

Knowledge Agents in detail

Governance by Design

Auditable. Compliant. Enterprise-grade.

Human-in-the-Loop architecturally enforced – not optional

Complete audit trail for every agent decision

GDPR compliant by design – all data on your infrastructure

Works council compatible – agreements as constraints in the Decision Layer

EU AI Act compliant by design – transparency, explainability, human oversight

Model-agnostic – no vendor lock-in, you own the source code

From PoC to platform

1

Discover

1 week

Process analysis, understand rule sets, prioritise use cases.

2

Build

3–4 weeks

Productive PoC. One agent, one process, live on your infrastructure.

3

Scale

Continuous

More agents, more processes. Same governance, same auditability.

After 12–18 months, you operate your agents independently. Source code, prompts, and rule sets are yours.

Go deeper

Analysis and insights on enterprise AI, governance, and agent architecture.

Why AI Projects in HR Fail
HR & People Operations

Why AI Projects in HR Fail

Most AI projects fail not because of technology but because nobody defined the rules. Why the operating model matters more than the language model.

“Even as a global market leader, you want to keep moving forward. It is reassuring to have the technological expertise and infrastructure experience of Gosign on our side.”

Arletta Korff

Head of Innovation, Sony Music Entertainment

“Gosign is not just about speed. It's about how much essential work happens in this time.”

Truels Dentler

Head of Customer Service & Technical Support, Libri GmbH

Frequently Asked Questions

Does Gosign operate in Sao Paulo?

Yes. We have an office in Sao Paulo with local project managers for on-site support and project management across Brazil.

How is LGPD compliance handled?

LGPD-compliant by design. All data remains on your infrastructure. No data transfer to third parties.

Are the agents compatible with Brazilian labour law?

Yes. Our agents are configurable for CLT, collective bargaining agreements (CCT/ACT), and sector-specific regulations. Interaction with worker representatives (unions) is architecturally integrated into the Decision Layer.

Does Gosign work with mid-sized enterprises?

Yes. Our architecture scales from enterprises with approximately 200 employees to large corporations. The starting point is always the same: one process, one agent, productive.

Which process should your first agent handle?

Talk to us about a specific use case in your organisation.

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