Before deploying anything, we establish your AI maturity baseline and target state. The maturity model has three levels:

Maturity Levels

  1. Basic - commercial LLM adoption with governance and DLP
  2. Intermediate - platform-native AI like Atlassian Rovo or IFS.ai with agentic workflows and change management
  3. Advanced - custom AI infrastructure built on a unified enterprise data platform, with precision grounding and adaptive refinement.

We map your current data environment, identify CUI and compliance constraints, prioritize use cases by business impact, and design a roadmap.

AI tools perform only as well as the data underneath them. Oxalis builds the enterprise data foundation:

  • Pattern-aware ingestion of structured and unstructured artifacts
  • Multi-dimensional indexing across semantic meaning, structure, entities, and usage outcomes
  • Context assembly that dynamically retrieves the right prior work, examples, and constraints for each task.

Partnerships

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For organizations running Atlassian, Rovo brings AI directly into the tools your teams already use (search, chat, and agents built into Jira, Confluence, and JSM). Oxalis deploys and configures agents built around your specific workflows, not out-of-the-box defaults. For regulated industries, Rovo respects existing permission structures, supports audit trails, and operates within Atlassian's FedRAMP-authorized Government Cloud environment.

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IFS Cloud includes embedded AI capabilities (predictive maintenance, scheduling optimization, anomaly detection, and operational analytics) that connect directly to your asset management, field service, and ERP data. As an IFS Platinum Partner, Oxalis configures IFS.ai for your specific operational environment, connecting field data, maintenance history, and KPIs into role-specific dashboards your teams will use.

For organizations that need precision-driven AI to drive competitive differentiation, Oxalis designs and builds advanced custom AI capability:

  • Use case definition with measurable success criteria
  • Secure data ingestion and RAG-based context assembly
  • LLM and agent workflow configuration with human-in-the-loop validation
  • Governance frameworks including usage policies, role-based access, DLP alignment, and output traceability
  • Adaptive refinement that improves retrieval, ranking, and synthesis behavior over time.