AI-first in your software house: speed, cost and quality
The decision to place artificial intelligence at the center of development strategy changes the way we deliver software. In practice, this is reflected in three dimensions that customers perceive immediately: speed, cost and quality. At VertexHub, where AI-first is the foundation of all projects, these pillars are monitored in every line of code, in each release and in the experience of end users.
Speed: from idea to product in fewer cycles
Automation of repetitive tasks
With LLMs like Claude and Groq integrated into our workflow, routine activities – boilerplate generation, API route creation, database schema definition – are assisted by AI. The Model Context Protocol (MCP) allows the AI agent to access the current state of the repository and suggest changes aligned with the team's code standards, reducing time spent on structural code and allowing greater focus on domain logic.
Real-time semantic search
The integration of Qdrant as a vector database enables semantic queries that quickly return documentation snippets, implementation examples, or related support tickets. When a developer searches for "how to implement multipart upload in Go with Echo", the AI returns the relevant answer in seconds, avoiding navigation through multiple documentation pages.
Faster deployments
Our stack based on Kubernetes (k3s) and Kata Containers offers lightweight isolation, which reduces test environment provisioning time. The CI/CD pipeline, built in a generic and reusable way, can be triggered from Vertex ChatSense, where AI agents can start builds or create staging environments on demand, reducing the need for manual interventions.
Cost: optimization of human and computational resources
Reduction of manual effort
By delegating repetitive code writing to AI, developers' workload focuses on business logic and architecture. This reduces the need to expand the team for routine tasks, allowing the team to remain lean without compromising delivery.
On-demand infrastructure
The combination of Rust (Axum, Actix) and Go (Echo, chi) produces efficient binaries that consume less CPU and memory than equivalent solutions in heavier languages. When these services run in Kata containers, virtualization overhead is minimal, contributing to a lower cloud bill. The use of PostgreSQL 17 with advanced indexing features also reduces the need for early horizontal scaling.
Reuse of AI-native components
The Retrieval-Augmented Generation (RAG) modules that power Vertex ChatSense service agents are reused in Atendis and Praxia to automate internal workflows, such as margin report generation or digital prescription filling. This reuse avoids creating parallel solutions and reduces maintenance costs.
Quality: consistency, security and user experience
More consistent code
LLMs are configured to follow the coding style adopted by the team (for example, error handling patterns in Go or async/await usage in SvelteKit). When AI generates a new module, it is already aligned with defined linting and security rules, reducing the need for extensive reviews.
Support in test creation
AI can analyze function signatures and suggest unit and integration test cases, including mock suggestions for external dependencies. This assistance helps expand test coverage without requiring the team to write each case manually.
Compliance and audit
In products like Praxia, which deal with health data, AI assists in verifying LGPD requirements and CFM/CRP/CFN standards, identifying fields that need encryption, suggesting retention policies and generating audit reports for review. In SignID Brasil, AI validates integration with SERPRO and ensures that the signature flow complies with MP 2.200-2/2001, reducing non-compliance risks.
AI-guided user experience
In production products, AI agents go beyond simple chatbots. In Vertex ChatSense, they perform ticket triage, suggest responses based on interaction history and trigger automations that reduce the need for human intervention in many cases, improving response time and end customer satisfaction.
How AI-first translates into custom projects
When a customer hires us to develop a custom project, the same AI-first mindset is applied to the specific scope. We start by mapping processes that can be automated by AI, defining integration points with our existing modules (for example, RAG for internal support or automatic document generation) and then we build the product using our high-performance stack. The result is usually a shorter time-to-market, controlled costs and a level of quality aligned with industry regulatory requirements.
If you want to experience firsthand how artificial intelligence can accelerate the development of your next software, discover our cases and see how we adapt the AI-first approach to different domains. We are ready to co-create the solution you need.