We offer an end-to-end approach for the design, development, evaluation, deployment, and maintenance of Generative AI systems on AWS, with a strong focus on real business use cases, security, quality, and cost reduction.

Systematic evaluation and tool selection

We conduct rigorous evaluations to measure the performance of language models or conversational agents against specific business use cases. Our methodology includes:

Continuous benchmarking. Ongoing comparative evaluation of the Amazon Bedrock Generative AI providers.

Specialized testing. Custom-designed test sets for each client and use case, simulating real production conditions.

Production performance prediction. Tests that help forecast system behavior under real load and with complex queries.

Model customization and fine-tuning

We adapt and fine-tune Amazon Bedrock language models using client-specific data to improve accuracy, relevance, and business alignment. This includes:

Supervised fine-tuning and customization.

Adaptation to specific terminology, style, and business context.

Optimization for targeted tasks (e.g., customer support, report generation, etc.).

Experience in projects requiring multimodal solutions that combine text, audio, images, or video.

Security by Design

We guarantee secure deployments with the following measures:

Guardrails by design. We incorporate mechanisms to prevent inappropriate, off-topic, or sensitive outputs.

Regulatory compliance. Full alignment with standards like the AI Act (RIA) and best practices in secure software development.

Cost Reduction

We design efficient solutions that reduce the Total Cost of Ownership (TCO):

LLM optimization and compression. Reducing model size and operating costs through specialization, pruning, and distillation techniques.

Use-case-based tuning. Tailoring architectures to maximize efficiency and minimize resource consumption.

Secure and Ethical Development Methodologies

We apply secure software development standards and AI engineering best practices, including:

Ethical AI policy. Designing and deploying solutions under principles of transparency, non-discrimination, and accountability.

Quality assurance. Continuous validation with testing cycles, QA processes, and expert reviews at each phase.

Multidisciplinary Team

Our team includes experts covering every phase of the Generative AI lifecycle on AWS:

Data Scientists:

Specialized fine-tuning.

Automation of testing pipelines and large-scale evaluation.

Computational Linguists:

Creation of evaluation and training datasets.

Manual evaluation of model outputs with linguistic and business criteria.

Prompt engineering and guardrail configuration.

Data and Security Engineers:

Secure software development.

Integration into AWS production environments.

Scalability and RAG (Retrieval-Augmented Generation)

We provide a proprietary RAG platform (René) offering:

Ready to use on AWS.

High scalability. Reduced development times and faster production deployment.

• Built-in reliability and security. Pre-configured guardrails and full AI Act compliance.

Risk-minimizing evaluation methodology. Ensures quality and stability before production rollout.

Landmarks: Spanish-Centric LLMs and Scalable GenAI Solutions

Pioneers in Spanish LLMs: In-house development of Rigochat and RigoBERTa, language models tailored for Spanish. Both models are available on AWS Marketplace.

Proven LLM and RAG evaluation framework.

René: A scalable, production-ready RAG system.