Services

We design products where artificial intelligence is part of the experience and of the core behaviour, not a superficial add-on. We build copilots, specialized assistants, semantic search, RAG solutions and agentic workflows aimed at real use cases.

We connect models with business information, rules and processes so they can answer with context, execute controlled tasks and deliver measurable value. Every solution is designed from the start with privacy, security, operating cost and human intervention in mind.

We use the Model Context Protocol (MCP) to connect AI assistants and agents with APIs, knowledge bases, WordPress, cloud services and internal systems. We create MCP servers and tools that expose specific capabilities through structured, reusable and governable interfaces.

This lets an agent query information or run authorized operations without relying on fragile integrations. It also makes it easier to add access controls, input validation, traceability and clear limits on what each tool is allowed to do.

We build web and mobile applications and internal platforms, from working prototypes to production-ready products. We can take part in the whole cycle or reinforce an existing team in architecture, frontend, backend, integrations and user experience.

We work with technologies such as React, Angular, React Native, .NET and .NET MAUI, choosing the architecture based on the product, the team that will maintain it and its growth needs.

We help existing applications evolve without interrupting their operation. We analyze dependencies, risks and technical debt to define a gradual modernization: updating components, separating services, creating APIs, improving deployment and migrating to Azure when it brings a concrete advantage.

The goal is not to change technology for the sake of fashion, but to achieve systems that are more maintainable, observable, secure and easy to evolve.

We connect applications, APIs and data sources to reduce manual work and eliminate repetitive steps. We design automations that classify information, generate documents, synchronize systems, assist teams and coordinate tasks with explicit business rules.

Where AI is useful, we bring it into specific points of the flow. Where a traditional rule is more reliable, we use deterministic software. This combination produces practical and controllable solutions.

Taking an AI prototype to production requires far more than connecting to a model. We implement response evaluation, observability, traceability, error handling, cost control, data protection and human oversight.

We define metrics and test sets to know whether the solution actually improves with each change. We also design recovery and review mechanisms for the cases where the system does not have enough information or confidence.

We support teams that need to make technical decisions, validate an architecture or take on new capabilities. We run assessments, proofs of concept, design reviews and implementation plans that prioritize impact, risk and effort.

We also offer hands-on training so teams can understand, maintain and evolve the solutions. Knowledge transfer is part of the work, not an afterthought.