Synthèse
Overall, there is too much data in engagement logs for anyone to manually review, causing a bottleneck for analysis. To combat this issue, Janus produces normalized JSON for every task and result, making operational data usable by LLMs. Users should ensure any data shared with LLM services aligns with their organization’s data handling policies. For sensitive environments, this can be done using self-hosted models or hosted options such as Amazon Bedrock. Instead of digging through logs, teams c
Overall, there is too much data in engagement logs for anyone to manually review, causing a bottleneck for analysis. To combat this issue, Janus produces normalized JSON for every task and result, making operational data usable by LLMs. Users should ensure any data shared with LLM services aligns with their organization’s data handling policies. For sensitive environments, this can be done using self-hosted models or hosted options such as Amazon Bedrock. Instead of digging through logs, teams can talk to their operations with natural language.
This turns raw telemetry into something you can ask questions about, not just stare at and store. Over time, the analyses and conversations with engagement data will create an evolving knowledge base.