Cledar

Cledar is a technology firm that designs and implements data-driven applications and platforms. The organisation is led by individuals with prior leadership roles at CERN and comprises data scientists, software engineers, and technology specialists. The company operates from a headquarters in a European country (headquarters country stated as such in source content) and serves organisations across multiple regions implied by enterprise-scale client work and platform delivery models.

Services and Capabilities

• Data architecture and integration: design and delivery of unified data foundations that integrate fragmented data sources into a governed semantic context.
• Ontology-driven platform engineering: development of semantic models and a platform that provides a single, governed source of truth for downstream systems.
• Machine learning and AI agent deployment: configuration and deployment of AI agents that can reason across integrated data and execute end-to-end tasks.
• Data preparation and quality engineering: implementation of data preparation pipelines and quality controls to harmonise datasets for analytics and operational use.
• Analytics and conversational interfaces: delivery of natural language querying, visualisation components, and BI/SQL integration for technical teams.
• Platform operations and governance: implementation of governance, compliance, cost visibility, and hybrid cloud/on-premise operational controls.

Industry Focus

• Enterprise organisations: projects centred on scaling AI pilots to production, integrating systems, and optimising operational decision cycles.
• Research and large-scale science organisations: involvement by former domain leaders from CERN indicates experience with large, complex datasets and collaborative data environments.
• Information technology and infrastructure services: work includes cloud and hybrid deployments, platform configuration, and systems integration.

Technology and Delivery Approach

• Ontology-driven design: use of semantic models to create a unified data layer that supports cross-system reasoning and reduces data fragmentation.
• Hybrid deployment models: platforms designed to run across cloud, on-premise, and hybrid environments with built-in governance and cost visibility.
• Proof-of-concept to production workflow: staged delivery including initial scoping, POC validation, detailed requirements, MVP deployment, and long-term production support.
• Automation and agents: deployment of configurable AI agents to automate workflows and perform tasks across integrated datasets.
• Analytics integration: provision of conversational querying, instant visualisation, and BI/SQL interfaces to support both non-technical and technical users.