Hire AI Development Team

12 days

Average ramp-up to deploy a senior AI squad with data pipelines ready.

95%+

Models meet accuracy targets before production deployment.

8+

AI frameworks our teams have production experience with.

We embed with your product, data, and engineering teams to deliver AI solutions that are accurate, scalable, and maintainable. Whether you need to build recommendation engines or computer vision systems, our squads adapt to your data requirements and deployment processes.

• Production-grade machine learning models built with TensorFlow, PyTorch, or scikit-learn with comprehensive testing and validation before deployment.
• Natural language processing systems using transformers, LLMs, and custom NLP pipelines to create intelligent chatbots and text analysis tools.
• Computer vision applications built with OpenCV, YOLO, and custom neural networks for image recognition, object detection, and visual analytics.
• Predictive analytics platforms that forecast demand, optimize pricing, detect anomalies, and automate decision-making at scale.
• Data-first development with proper data preprocessing, feature engineering, model validation, and continuous monitoring for model drift.
• Collaborative delivery leadership that syncs with your outsourced development initiatives and ensures AI best practices are followed.

We partner with CTOs, data science managers, and product leaders who need an AI squad that can deliver accurate, production-ready solutions. Every engagement comes with a delivery lead, AI architect, and a data quality review process integrated with your workflows.

• Data science-first staffing with engineers who have deployed production ML models in e-commerce, healthcare, finance, and manufacturing.
• Architecture for scale leveraging MLOps, model versioning, A/B testing, and monitoring infrastructure to ensure long-term performance.
• Multi-framework expertise that keeps your options open—we work with TensorFlow, PyTorch, scikit-learn, and cloud AI services.

Our team in the Americas overlaps with U.S. time zones and collaborates in English and Spanish. We maintain transparent development processes with model performance checkpoints so you always know the status of your AI deployment.

• Quality rituals that matter — data validation, cross-validation, performance metrics, and model explainability before production.
• Business impact focus with proactive monitoring, accuracy tracking, and continuous improvement tied to your business KPIs.
• Clear performance metrics tied to accuracy, precision, recall, and business outcomes you can track and optimize.

1. Data & requirements review

We review your data sources, business objectives, and success metrics. Expect data quality assessment, model architecture recommendations, and a data-first roadmap within the first week.

2. Model-focused squad

We define team composition with ML engineers, data scientists, and AI architects. Tooling includes Jupyter notebooks, MLflow, cloud AI services, and your existing data infrastructure.

3. Validated delivery

We ship in iterative increments, conduct model validation, and share accuracy, performance, and deployment readiness metrics so you can confidently deploy to production.

RetailPredict, a retail technology company, needed to build an AI-powered demand forecasting system without disrupting their existing inventory management workflows. Their team was juggling seasonal patterns, supplier lead times, and pressure to reduce stockouts while minimizing excess inventory.

We partnered with their VP of Analytics to assemble a mixed squad of data scientists, ML engineers, and MLOps specialists who worked alongside internal analysts. Together we:

• Developed time series forecasting models using LSTM and Prophet that reduced forecast error by 38%.
• Built automated data pipelines that integrated with existing ERP systems and updated predictions daily.
• Implemented model monitoring and retraining workflows that adapt to changing demand patterns automatically.

Every team blends experienced AI developers with the roles you need to deliver accurate, production-ready solutions. We frequently combine:

• Senior ML Engineers who design, train, and deploy machine learning models with production-grade code quality.
• Data Scientists who analyze data, engineer features, and validate model performance against business metrics.
• NLP Specialists for language understanding, chatbots, and text analysis applications.
• Computer Vision Engineers for image recognition, object detection, and visual analytics systems.
• MLOps Engineers who build deployment pipelines, monitor model performance, and manage retraining workflows.
• Delivery Managers who coordinate sprints, track metrics, and ensure alignment with business objectives.