AI2GO

AI Discovery 

1-day complimentary engagement 

Identify high-value AI opportunities and define a path forward.

• Current State Analysis 
  Audit infrastructure, pipelines, data sources, and security boundaries to establish a technical baseline.
• AI Maturity Assessment 
  Benchmark organizational capabilities across model deployment, platform automation, safety, and continuous vector database operations.
• Use-Case Prioritization 
  Map proposed AI initiatives against a structured feasibility-to-impact matrix to isolate high-yield quick wins.
• Target-state AI Architecture 
  Formulate a concrete, scalable blueprint for running containerized GPU workloads on Red Hat OpenShift AI.
• AI Adoption Recommendations 
  Detail strategic operational milestones, cluster sizing models, hardware profiles, and software tooling pathways.
• Responsible AI Guidelines 
  Establish organizational guardrails for data residency, model transparency, bias controls, and generation safety.
• Actionable Phased Roadmap 
  Deliver an executive-ready execution schedule mapping incremental technical phases to strategic business outcomes.

GenAI and MLOps Bootcamps 

2-week enablement program 

Ideal for organizations looking to build the skills and capabilities needed to run AI in production.

• MLOPS 500
  Master pipeline automation, Elyra visual workflows, and active lineage tracking directly on OpenShift AI.
• GENAI 501
  Train developers on context embedding, vector search, private LLM boundaries, and multi-agent tool integrations.
• AI Factory
  Establish standardized registries and automated validation gates to continuously govern, package, and test ML models.
• AI at the Edge
  Optimize and deploy lightweight inference models to decentralized hardware nodes using Red Hat Device Edge and MicroShift.
• AI Architectural Design
  Optimize virtual GPU (vGPU) partitioning, infrastructure resource allocations, and secure high-performance networking.
• AI Hybrid Cloud 
  Architect elastic horizontal routing to shift workloads dynamically between local data centers and public cloud hyperscalers.

AI Pilots

6-week engagement

Validate AI use cases and demonstrate measurable business value.

• Models as a Service
  Expose securely managed, standardized internal API endpoints to let developers easily invoke pre-trained LLM runtimes.
• Agentic AI with Red Hat
  Deploy autonomous LLM systems using ReAct logic loops to securely execute tools and call internal microservices.
• LLM Optimization (vLLM & LLM-D)
  Implement hardware acceleration and dynamic batching configurations to maximize inference throughput and minimize latency.
• Private Code Assistants for developers 
  Deploy secure, local code-generation LLMs directly inside developer IDEs to accelerate delivery without exposing corporate IP.
• Intelligent MultiModal OCR
  Extract unstructured content, complex layouts, and tabular data from images and PDFs directly into secure databases.
• RAG Development for Internal Documentation
  Ground prompt execution with high-performance vector search indexes mapped directly to secure internal file networks.
• Agent to Agent (A2A)
  Build collaborative orchestrations where specialized, low-latency AI agents securely negotiate and pass tasks over message buses.
• Local Model Fine Tuning Apply
  LoRA and QLoRA adjustments to adapt foundation models to domain-specific enterprise vocabularies.
• AI Safety
  Deploy multi-layer guardrails, prompt validation shields, toxicity filters, and human-in-the-loop validation checkpoints into APIs.
• AIOps
  Deploy telemetry-driven ML models to monitor platform metrics, predict errors, and proactively protect cluster health.

Enterprise AI co-innovation 

6-month engagement 

Operationalize AI and build a foundation that can scale across the enterprise.

• Operationalize AI Strategy
  Define cost metrics, data lifecycles, and risk boundaries to establish a standardized AI operating model.
• Architect Agentic & Predictive Workflows
  Combine self-directing software agent routines with automated analytical models for end-to-end process automation.
• Establish Cross-Functional AI Governance & CoE
  Assemble a steering committee to audit model lineage, enforce compliance, and manage drift system-wide.
• Architect the Hybrid Open AI Stack with Red Hat AI
  Build a unified, zero-lock-in platform spanning bare-metal, clouds, and edge devices with OpenShift AI.
• Abstract Data & Secure Enterprise Knowledge
  Expose enterprise knowledge bases securely without sharing raw backend credentials or risking IP leaks.
• Implement a Multi-Model / BYOM ecosystem 
  Construct a model-agnostic gateway to shift workloads dynamically between open models and public cloud APIs.
• Automate Secure MLOps & LLMOps
  Pipelines Implement automated GitOps pipelines to continuously scan container dependencies, validate licensing, and monitor safety rails.
• Integrate AI Agents into Enterprise Tooling
  Embed custom agent capabilities directly into daily developer IDEs, task trackers, and business dashboards.
• Validate & Iterate
  Profile latency, accuracy, and token costs using automated testing frameworks to continuously optimize performance.
• Scale CoE, Upskill Team & Evolve
  Evolve internal capabilities through platform simulations and mentoring to achieve complete organizational AI autonomy.