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1. Ava to be able to create Attributions

   Ava can create reports, but it would be nice if Ava could create Attributions for a customer.

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2. AI fueled insights based on cloud logs

   Cloud infrastructure has a huge number of logs (AWS CloudWatch Logs / AWS CloudTrail / AWS VPC Flow Logs / GCP Cloud Logging / GCP Audit Logs). These include an enormous amount of rapidly changing information, often repeating errors that might have appeared and no one noticed. We could utilize our capabilities in cloud infrastructure and AI to highlight insights, and even offer suggestions on how to fix issues, based on the content of these logs.

   • Cloud environments generate millions of log entries per day. The sheer volume makes it impossible for engineers to manually review logs effectively.
   • A significant portion of logs are repetitive, contain expected behavior, or have no real actionable value. Critical errors can be buried under an avalanche of trivial events.
   • Most log monitoring today is focused on reacting to incidents after they cause problems, instead of proactively identifying potential failures or quickly responding no problems before they become significant.
   • Errors often lack deeper explanations or potential fixes. Engineers may spend hours investigating a single issue, only to realize it's a known problem with a simple resolution.
   • New engineers may struggle to recognize recurring patterns, and tribal knowledge about system quirks isn’t easily shared or accessible.

   Currently, organizations rely on dashboards, search queries, and alerting systems to deal with logs, but these approaches are manual, time-consuming, and ineffective at recognizing complex patterns across services.

   By leveraging AI and deep expertise in cloud infrastructure, we can transform raw logs into actionable intelligence, allowing companies to shift from a reactive approach to a proactive, self-improving system.

   • AI can analyze millions of logs in real time, identifying patterns and anomalies across multiple services.
   • It can group related errors together, reducing noise and presenting only meaningful insights.
   • By correlating logs with historical data, AI can suggest fixes automatically. For example: If an error related to IAM permissions appears, the system can propose the correct policy update or provide a link to relevant documentation.
   • Instead of waiting for outages, the system can predict issues before they escalate. For example: If repeated database connection timeouts are detected, the system can recommend scaling adjustments or preemptive mitigation steps.
   • AI can build and maintain a knowledge base of recurring issues and their resolutions. So that new engineers can quickly see previous incidents and fixes, reducing onboarding time and reliance on tribal knowledge.
   • AI-driven suggestions cut down troubleshooting time from hours to minutes, leading to faster incident resolution and significant cost savings in engineering effort and downtime.
   • AI insights can reveal long-term trends that impact system architecture, cost, and security. For example: Identifying inefficient compute resources based on usage logs and recommending cost optimizations before the next billing cycle.

   Current approach by many cloud consumers:

   • Engineers manually review logs using Kibana, CloudWatch, GCP Logging, or Datadog.
   • Alerts are created for known issues, but unknown patterns go undetected.
   • When an incident occurs, engineers grep through logs, cross-reference with documentation, and escalate when needed.
   • Resolving critical failures can take hours or days, leading to revenue loss and operational strain.
   • Engineers spend significant time searching for issues instead of solving them.
   • Dashboards only track known issues, missing emerging problems.
   • Log reviews don’t improve system resilience beyond immediate fixes.
   • More log ingestion requires higher monitoring costs, yet insights remain limited.

   How does it look like after this solution is implemented?

   • Instead of digging through raw logs, users receive curated insights that highlight critical patterns.
   • Engineers get direct recommendations on resolving issues, including code snippets or config fixes.
   • The system detects emerging problems early, reducing downtime and preventing outages.
   • AI updates its RAG knowledge base, allowing teams to learn from past incidents without manual effort.

   What must cloud users do in order to receive these benefits?

   • Organizations must route their logs through the AI engine for analysis.
   • Teams need to trust AI insights and incorporate suggestions into their workflows.
   • Where safe, companies should allow AI to auto-fix certain issues (e.g., auto-scaling, permissions).
   • Feedback loops must be created to enhance AI predictions over time.
   • Engineers must move beyond "fixing problems as they arise" to preventing issues before they occur.

   This is the kind of step-change improvement that turns cloud logging from an expensive burden into a strategic advantage.

   Cloud infrastructure has a huge number of logs (AWS CloudWatch Logs / AWS CloudTrail / AWS VPC Flow Logs / GCP Cloud Logging / GCP Audit Logs). These include an enormous amount of rapidly changing information, often repeating errors that might have appeared and no one noticed. We could utilize our capabilities in cloud infrastructure and AI to highlight insights, and even offer suggestions on how to fix issues, based on the content of these logs.

   • Cloud environments generate millions of log entries per day. The sheer volume makes it impossible for engineers to manually review logs effectively.
   • A significant… more
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   Discovery  ·  0 comments  ·  Ava Assistant  ·  Delete…  ·  Admin →
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