Approach

A methodology for creating, adapting and deploying AI solutions with real impact on your business.

1

We use existing models as the basis for our solutions

We work with the major industry benchmarks in language models (LLMs and SLMs), both commercial and open source.

This allows us to accelerate development without starting from scratch, building on proven capabilities.

Mistral · OpenAI · Gemini · Cohere · Claude · LLaMA

2

We apply key criteria for assessing viability

Before moving forward, we analyse the application environment and define what is needed for the solution to make sense and work.

We reviewed aspects such as:

  • AI governance and compliance
  • Data preparation, adequacy and governance
  • Level of customisation required in the model and flows
3

We carry out adaptation and training exercises

Not all models work the same for everyone. That’s why we apply techniques to help them understand your context and act accurately:

  • RAG (Retrieval-Augmented Generation)
  • Fine-tuning or adjustments on lightweight models
  • Advanced prompts engineering
  • Generation of synthetic data to fill in gaps
  • Other control, filtering or validation techniques
4

We lay the foundations on which to build the solution

We define the most appropriate technical approach according to the business objective:

  • Machine learning models (predictive, classificatory, segmentation, anomaly detection, optimisation…), recommendation engines, or hybrid architectures.
  • We also structure the logic by which it will be integrated into your systems, processes and decisions.
5

We developed the AI solution

We build agents, tools or modules that apply intelligence – including machine learning and language generation – to act in real time or in batches.

Conversational assistants · Intelligent automation · Content generation · Decision making systems · Recommenders

The solution can act on internal processes, digital channels or customer platforms.

6

We apply the necessary technical layers

For AI to work, it must also be integrated with the environment. We incorporate the necessary parts for its operation:

  • Infrastructure as code (IaC)
  • Development and implementation of MCP Servers
  • Integrations with internal systems (APIs, RPA, CRMs, ERPs)
  • Security, networks and access control
  • Business rules and decision logic
7

We optimise and deploy in production

We don’t just do proof of concept, we deliver functional and scalable solutions.

It includes:

  • Progressive deployment by use case, equipment or region
  • Continuous integration and delivery (CI/CD) of models, pipelines and applications
  • MLOps: version control, tracking, validation and retraining
  • FinOps: cost control, efficient use of infrastructure and economic reporting
  • Orchestration of development, testing, staging and production environments
8

We monitor, maintain and improve

The real value is seen in use. That’s why we ensure that the solution works, learns and evolves.

  • Intake and interpretation of logs
  • Detection of deviations or degradation of performance
  • Operational Dashboards (Grafana, Kibana, etc.)
  • Continuous improvement from business metrics and user feedback