At Acid Labs, we drive applied artificial intelligence to create real solutions: ML models, automation, and conversational agents integrated into your systems, to scale processes and achieve measurable impact without friction..
The challenge of applying AI in business environments >
Implementing applied artificial intelligence requires more than just models: it demands reliable data, integration with systems, scalability, and a focus on real and measurable impact.
Unrealistic expectations and low ROI
Many companies start projects with unrealistic expectations and without clear metrics, generating AI solutions with low returns and poorly aligned with the business.
Models without integration or scalability
Machine learning models often remain isolated, without integration with key systems, making automation with AI, conversational agents, and their operation in production difficult.
Technical complexity and operational risks
Without MLOps, monitoring, and governance, AI solutions lose accuracy, generate risks, and limit their use in retail, banking, logistics, and customer service.
Applied AI Solutions
We support the entire cycle of adopting AI-based solutions to ensure real and scalable impact
Predictive Models and Machine Learning
We build ML models for forecasting, classification, and operational optimization.
Intelligent Automation with AI
We design automated workflows that reduce time, errors, and costs in key processes.
Integrated Conversational Agents
Powerful chatbots and assistants, connected to your systems to automate customer service and support.
Assessment and Strategy of Applied AI
We identify viable use cases and design the roadmap to scale AI in your company.
AI for Operational Processes
Custom solutions for logistics, fraud, dynamic pricing, and real-time analysis.
Real stories, extraordinary results
The tools behind our data solutions
We select market-leading tools to ensure interoperability, governance, security, and scalability on each platform.
AI/ML Models and Frameworks
TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM.
Computing and cloud platforms
AWS Sagemaker, Google Vertex AI, Azure ML Studio.
Data processing and preparation for AI
Databricks, Snowflake, BigQuery, dbt.
Orchestration and MLOps
Airflow, MLflow, Kubeflow, Prefect.
Conversational agents and LLMs
OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, Rasa.
Monitoring and observability of models
Arize, WhyLabs, Datadog, Prometheus, EvidentlyAI.
Certifications and Partners
Our work process
To ensure agile, secure, and scalable data platforms, we implement an iterative methodology based on best engineering practices, DataOps, and cloud.
We analyze your infrastructure and needs
We evaluate current systems, silos, and requirements to define the scope of the data platform.
We design the ideal cloud-native architecture
We create modern data architecture by selecting scalable technologies that align with the business.
We implement and migrate your platform step by step
We build pipelines, automate processes, and migrate data to a reliable cloud platform.
We ensure quality, consistency, and reliability
We validate integrity, performance, and monitoring to ensure reliable data from day one.
We accompany you in the adoption and daily operation
We train your team and provide documentation to operate the platform seamlessly.
We continuously optimize and evolve your platform
We monitor performance, improve pipelines, and adjust the architecture according to business needs.
Specialized staffing in data platform
We have specialists in data platforms who can integrate into your team to strengthen capabilities, modernize your infrastructure, and accelerate the execution of critical projects.