Artificial Intelligence
in every line of code.

We integrate Gemini and Claude into every stage of development: coding, review, chatbots, and cloud architecture

How we use AI in our product development

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Code Generation

Intelligent autocomplete, boilerplate, and refactoring with project context.

✓

Smart Code Review

Bug detection, security checks, and quality improvements before each merge.

💬

Conversational ChatBot

RAG, persistent memory, and long-term context with integrated Gemini and Claude.

◆

Gemini in GCP

Vertex AI, BigQuery ML, and AI pipelines in production.

What we achieve by integrating AI

Faster code reviews

Code review with Gemini

Gemini - Claude

Reducing bugs in production

Detection before the merge

Code Review AI

Accelerate development time

More features per sprint

Automatization

More human digital experiences

Satisfaction with AI Chatbots

ChatBot - RAG
GEMINI AI

Gemini writes.
You supervise.

Generation, refactoring, and automatic documentation of code

api_service.py — Vertex AI generated 
async def get_user_data(user_id: int) -> dict:
  # Vertex AI: optimizado con caching + error handling
  try:
    cached = await redis.get_user(user_id)
    if cached:
      return cached  (cached)
Vertex AI · Añadir rate-limiting, distributed tracing & retry logic

Gemini takes care of it for you:

  • ● Contextual autocomplete by repository
  • ● Automatic function refactoring
  • ● Generated inline documentation
  • ● Vulnerability detection
  • ● Automatically generated unit tests
CLAUDE - ANTHROPIC

Claude: thorough and reliable reasoning.

We use Anthropic's Claude for tasks that require complex analysis, extensive documentation, and advanced technical support.

Where we use Claude:

  • ● Architectural analysis and design
  • ● Extensive technical documentation
  • ● Review of PRDs and specifications
  • ● Advanced technical support for the team
  • ● Complex thinking and critical analysis
Gemini Advanced · Vertex AI
Analiza este código y sugiere mejoras de arquitectura
Revisé el código. Recomiendo separar la lógica de negocio en 3 capas independientes... Ver propuesta de arquitectura →
CHATBOT

Agents with a memory
and deeper context.

Agent skills

  • ● RAG with its own knowledge base
  • ● Persistent conversation memory
  • ● Integration with APIs and internal systems
  • ● Multilingual and tone-adaptive
  • ● Seamless handoff to a human agent
Asistente IA · en línea
¿Cómo migro mi API REST a Cloud Run en GCP?
Primero containeriza tu app con Docker, luego usa gcloud run deploy... Ver el Dockerfile completo →
Escribe tu pregunta...
GEMINI IN GCP

The smartest
Cloud infrastructure.

Gemini is natively integrated throughout the Google Cloud Platform ecosystem.

Vertex AI

Fine-tuning and deploying Gemini models in production using MLOps pipelines.

BigQuery ML

At-scale analysis with SQL prompts and integrated Gemini for real-time insights.

Cloud Run

Serverless endpoints with Gemini as the AI backend, automatic scaling.

Pub/Sub + AI

Event pipelines with real-time analytics powered by Gemini.

Ready to climb with AI?

REQUEST A CONSULTATION