Desarrollo e Implementación de IA a Medida
Resolvemos problemas técnicos complejos con aprendizaje automático adaptado a sus datos específicos. Diseñamos, construimos y desplegamos sistemas de IA seguros.
Lo que ofrecemos
Sistemas de IA en producción
Desarrollo de IA generativa y agentes
Valor empresarial medible
Inteligencia soberana
Cómo colaboramos con su empresa
Desarrollo de IA a medida
Soluciones de IA personalizadas diseñadas específicamente para los retos de su negocio, creadas por desarrolladores expertos que se integran perfectamente con su equipo.
Gestionamos el ciclo técnico completo: Desarrollo: Programación de soluciones, diseño de algoritmos y definición de APIs. Implementación: Despliegue en producción, integración con su infraestructura y garantía de ROI.
Muchos equipos empresariales carecen de experiencia profunda en ML, y ahí es donde intervenimos. Nuestro papel es cerrar esa brecha, construir las soluciones iniciales y ayudar a las empresas a desarrollar sus propias capacidades sólidas.
Por qué las soluciones de IA a medida ofrecen mejores resultados
Soluciones personalizadas
ROI maximizado
Protección de datos y cumplimiento normativo
Integración profunda
Resultados más rápidos para sus clientes
Costes predecibles
Riesgo reducido
Ventaja competitiva
Experiencia técnica en la que puede confiar
Nuestras soluciones a medida están respaldadas por un profundo conocimiento técnico documentado en publicaciones líderes del sector
Retrieval Augmented Generation, The Seminal Papers
Principles for architecting reliable and verifiable AI
Retrieval Augmented Generation (RAG) is a standard process for grounding LLM prompts in user-specified content rather than relying only on a model’s training data.
Generative AI with LangChain (2nd edition)
Build production ready LLM applications and advanced agents using Python and LangGraph
A practical guide to leveraging LangChain and LangGraph for GenAI implementation, with real-world examples ranging from customer support to data analysis.
Machine Learning for Time Series
Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods.
Artificial Intelligence with Python Cookbook
Proven recipes for applying AI algorithms and deep learning techniques
Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow and PyTorch.
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Cómo trabajamos con usted
We structure our engagements to address practical constraints: budget approval, risk management, and stakeholder confidence.
"We think this could work, but need proof before we can get budget approval."
Validation Phase
Feasibility & Proof of Concept
A self-contained proof-of-concept that validates your AI initiative's feasibility without internal dependencies or large commitments.
Real Example:
One client couldn't use internal data for the PoC due to IT security policies - we built on our infrastructure instead, delivering validation in 4 weeks without any internal IT involvement.
Best for: When you need proof an AI solution will work within your constraints before seeking larger budget approval.
"We want you as the brain of the project - doing architecture and guiding our team."
Advisory & Architecture
Technical Guidance & Oversight
A collaborative approach where we provide technical architecture and ongoing guidance while your team builds, retaining full ownership.
Real Example:
A client's technical team preferred us as 'systems engineers' - we designed the architecture, provided ongoing checks, and guided implementation while they retained full ownership and built internal expertise.
Best for: Organizations wanting to build internal AI capabilities while ensuring technical success and mitigating risk.
"The timeline has to work with the budget. We need something tangible with clear milestones."
Development & Implementation
Build, Code & Deploy
End-to-end delivery. We handle Development (coding, algorithms) and Implementation (production deployment, integration), ensuring the solution creates tangible ROI.
Real Example:
An organization had tried similar automation in 2022 that failed. We managed the Development of new algorithms and the Implementation onto their specific devices.
Best for: Projects requiring both technical build (Development) and operational integration (Implementation).
Engagement Structure
Beyond the specific collaboration model, we define who drives the process. Are we extending your existing capacity, or are we taking full ownership of the delivery?
01. Dedicated Development
"Your Process, Our People."
Best when you have technical leadership in-house but need specialized AI capacity. Our engineers plug into your existing workflows (Jira, Slack) and report directly to your Engineering Manager or Product Owner. You retain full control over the daily priorities.
02. Solution Development
"Our Process, Your Outcome."
Best when you need a specific result (e.g., "Build this MVP") but lack the bandwidth to manage a dev team day-to-day. We provide a full agile pod (PM, Devs, QA) managed by Chelsea AI. We take responsibility for the timeline and delivery; you act as the stakeholder approving milestones.
Not sure which approach fits your situation?
Every organization has different constraints. Let's discuss your preferred collaboration approach.