Macaw GenAI Assistant, launched last year at Cisco Live 2023, supports conversational and precise queries for ITOps, NOCOps and SecOps service desk complex tasks. CloudFabrix Telco service assurance and Network automation customers are using Macaw GenAI as a single pane of Ops with confidence for some of these use cases –
- Productivity use cases like analyzing datasets to generate persona-based business value dashboards or low code pipelines
- Operational use cases
- Actionable Incident remediation and diagnostic recommendations
- Incident history summarization
- Actionable syslog enrichment and recommendations
- Automation use cases
- Intent driven automation, execute runbooks and workflows for device onboarding, device management and searching assets
- Single pane of Ops
- Macaw provides GenAI enabled workflows for Incident Management, Root cause analysis and remediation and summarization
Also Read: EdgeCortix Launches SAKURA-II Platform to Power the Next Wave of Generative AI at the Edge
Macaw Advanced features bring transformative benefits to Operational Service Desks
- Eliminate bias and hallucinations
- CloudFabrix’s domain specific local LLM maps a precise Intent to the conversational queries, to generate a precise context eliminating any bias or hallucinations
- CloudFabrix low code RDAF further maps these intents to curated knowledge corpus of active metadata, real-time topology information etc. to create a curated context which is then send to secondary LLM to provide a precise response
- Single pane of Ops
- Incident Management – Conversational queries about Assets, Tools, Incidents to drive remediation and financial accountability
- Intent based automation: automate tasks beyond just data analytics. It can interact with IT systems and low-code knowledge repositories and active meta-data to take actions based on insights from AIOps
- Enhanced Security and reliability:
- CloudFabrix Macaw GenAI Assistant leverages local LLM to train GenAI models and uses RAG (Retrieval Augmented Generation) implementation to generate precise context, without customer data leaving premises. RDAF manages data governance and lineage ensuring data security
- Enhanced Efficiency
- Improve training time while conserving tokens – Macaw uses innovative techniques including a local LLM and RDAF platform to transform IT Operations without the need for a lot of historical and labeled datasets
- Efficiently train with smaller LLMs (hyperparameters), conserving compute Macaw breaks down conversational prompts to multiple parallel intents to improve GPU utilization and efficiency, enabling use of smaller LLMs, like LLam2 for training with customer data
- Scalability – CloudFabrix leverages parallel intents to accelerate topological metadata querying from GraphDB
Source: BusinessWire