Estrategias basadas en datos para acelerar el crecimiento y la eficiencia de tu negocio
¿Por qué confiar en Acid Labs para convertir tus datos en decisiones con impacto real?
Conectamos tus datos con tu negocio para tomar decisiones más inteligentes y efectivas
Soluciones pensadas desde tu negocio, no solo desde la tecnología
Nos involucramos en tus procesos y objetivos para construir herramientas analíticas que respondan a lo que realmente importa.
Datos con contexto, decisiones con sentido
Interpretamos tus datos con una mirada estratégica, transformándolos en acciones concretas que mejoran la rentabilidad y el crecimiento.
Respuestas más rápidas para equipos más ágiles
We implement systems that process information in real time, accelerating decision-making.
Inteligencia artificial que entiende tu realidad
Desarrollamos modelos personalizados que se adaptan a tu industria, tus usuarios y tus prioridades de negocio.
Automatización con propósito, no con promesas
Optimizamos tus procesos con flujos inteligentes que eliminan tareas repetitivas y aumentan la eficiencia operativa.
What technologies and platforms do we work with?
El comienzo de tu éxito digital
Data analytics
Transform raw data into business intelligence. We enable the consumption of actionable insights across the entire organization.
Data platforms
Establish the foundation for governance and security to treat data as a strategic asset. We ensure quality and compliance.
Applied AI
Design and implement AI/Machine Learning models. We optimize demand and generate predictive value in real time.
Conoce más >
Do you need urgent Data Scientists or Machine Learning Engineers?
Reinforce your team with specialists in Data Engineering, AI/ML, and Analytics to build data products and gain near real-time insights.
Real stories,
extraordinary results
eClass: Cloud-to-cloud migration (AWS to GCP)
Cloud-to-cloud migration (AWS to GCP) improves the browsing experience on the educational platform.
Cencosud: Re-engineering of digital store
Re-engineering of digital store content architecture (Vtex and Salesforce) accelerates commercial campaigns.
Preguntas frecuentes
While it is not always mandatory to have data from the start, in most AI projects, having relevant, accessible, and high-quality data is fundamental to achieving results that truly contribute to the business. Data allows for model training, generating insights, and validating hypotheses with evidence. It is essential to identify a specific problem to solve and define a clear value hypothesis. Active participation from business users and a sponsor to ensure strategic alignment are also required. Finally, willingness to iterate with PoCs, pilots, or MVPs enables rapid solution adjustments.
The time depends on the use case, data quality, and the business’s digital maturity. Using agile approaches such as PoCs or MVPs, tangible results can be achieved within 4 to 8 weeks. Some projects demonstrate impact within 2 to 4 weeks, while others require medium- or long-term approaches, especially if structural changes or technological scaling are involved. Our approach aims to generate progressive value from the start, with continuous validation alongside the business.
We start with a deep understanding of the use case: its goal, constraints, and the type of decisions it should support. We assess the type and quality of available data and define whether a supervised, unsupervised, generative, rule-based, or hybrid model is required. We then compare alternatives based on technical metrics (accuracy, response time, scalability), operational cost, interpretability, and regulatory compliance.
Artificial Intelligence (AI) aims to create systems capable of performing tasks that require human intelligence.
Machine learning is a branch of AI where systems learn patterns from data without explicit programming.
Deep learning is a subset of machine learning that uses deep neural networks to model complex abstractions, being especially powerful for image processing, natural language, and complex pattern recognition.
It depends on multiple factors: project type (data engineering, business intelligence, data science, traditional or generative AI), data quality, and required level of integration. Typically, the process begins with a PoC of 2 to 4 weeks, followed by a pilot or MVP of up to 3 months. A robust and scalable solution may take 3 to 6 months or more, depending on complex architectures, pipeline automation, data governance, or model deployment in production.
ROI measurement begins at early stages as part of our growth framework for data and AI products. Business indicators are identified from the start, where, even from an initial hypothesis, our team can make an impact. These indicators are aligned with the project’s technical objectives, making it crucial to have roles acting as a bridge between business and technology, such as our analytics translators.
We provide maintenance and continuous evolution services as a core part of our value proposition. Our data and AI solutions are designed with a high degree of automation, enabling "Ops" approaches—such as MLOps, DataOps, AIOps, or DevOps—to ensure scalability, stability, and operational efficiency. Continuous improvement is part of our DNA, working iteratively and adaptively to align technological evolution with the business’s changing needs.