Galliot

Galliot is a software and consultancy firm that provides engineering and AI product development services. The organisation offers software development, cloud and infrastructure management, and machine learning operations support. The company's materials reference work with clients in multiple countries, and projects indicate activity across cloud providers and on-premise environments, implying operations in regions served by those cloud platforms.

Services and Capabilities

• Cloud infrastructure and DevOps: design, build, and 24/7 support for cloud infrastructure, operations, monitoring, and CI/CD pipelines.
• Product engineering: end-to-end software product delivery including front-end and back-end engineering, UI/UX design, and quality assurance.
• MLOps and model deployment: operationalization of machine learning models, infrastructure-agnostic platforms for model training and serving, and management of GPU and CPU workloads.
• Data science and AI development: business intelligence, data pipelines, and deep learning application development.
• Integration and platform development: integrating third-party software into existing platforms and developing deployment and service architectures.
• Monitoring and automation: identifying production issues, automating infrastructure tasks, and implementing platform-level stability improvements.

Industry Focus

• Healthcare and life sciences: development and deployment of ML platforms and AI applications for healthcare customers, including managed deployments and services for clinical and related workloads.
• Startups and product-focused companies: engineering augmentation, feature development, and rapid integration work to support early-stage product delivery.
• Software platforms: integration of third-party components, platform engineering, and ongoing operations for SaaS and platform providers.

Technology and Delivery Approach

• Platform and integration focus: work routinely involves platform development, integration of third-party software, and building deployment architectures for production systems.
• Cloud and hybrid deployments: deployments and services are implemented across AWS, GCP, and on-premise environments, with support for both CPU and GPU workloads.
• Automation and monitoring: CI/CD pipelines, infrastructure automation, operations tooling, and production monitoring are recurring elements of engagements.
• Data, ML, and MLOps practices: model operationalization, data pipelines, and deployment of deep learning workloads are provided as part of services.
• Team augmentation and delivery: engagements include embedding engineers with client teams, managed engineering augmentation, and delivery-oriented collaboration models.