Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
An AI-powered accounting platform used by more than 3,500 companies, including Lululemon, Chipotle, and Shopify, whose AI agents draft journal entries, reconcile accounts, and flag exceptions directly in customers' general ledgers.
Thousands of customers close their books during the same handful of days each month, making a degraded ERP sync not an inconvenience but a...
“Our AI story is fundamentally a trust story. Agents that touch financial workflows have to be observable, auditable, and defensible. If we can’t reconstruct what an agent did and why, we haven’t earned the right to put it in front of an accountant.”
Accounting workflow automation platform for financial close management.
Open source observability platform for monitoring, visualization, and data analysis.
FloQast's Incident investigation is part of this use case:
Related implementations across industries and use cases
Scattered monitoring across 125+ AI models slowed debugging. Unified telemetry lets teams trace issues from user sessions down to the GPU.
Untangling 30-call AI interactions consumed developers. Now, support teams use system traces to resolve context issues without engineers.
Engineers lacked visibility into a slow AI agent. Rebuilt low-level tracing exposed critical bottlenecks and accelerated LLM responses.
Scattered monitoring across 125+ AI models slowed debugging. Unified telemetry lets teams trace issues from user sessions down to the GPU.
Engineers lacked visibility into a slow AI agent. Rebuilt low-level tracing exposed critical bottlenecks and accelerated LLM responses.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Misclassified tickets drove needless escalations. Now, an AI agent auto-routes cases and surfaces 1-click resolutions for support teams.
Each incident sent engineers hunting for context from scratch, inconsistently across teams. Edwin AI now delivers it upfront.
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
An AI-powered accounting platform used by more than 3,500 companies, including Lululemon, Chipotle, and Shopify, whose AI agents draft journal entries, reconcile accounts, and flag exceptions directly in customers' general ledgers.
Thousands of customers close their books during the same handful of days each month, making a degraded ERP sync not an inconvenience but a...
“Our AI story is fundamentally a trust story. Agents that touch financial workflows have to be observable, auditable, and defensible. If we can’t reconstruct what an agent did and why, we haven’t earned the right to put it in front of an accountant.”
Accounting workflow automation platform for financial close management.
Open source observability platform for monitoring, visualization, and data analysis.
FloQast's Incident investigation is part of this use case:
Related implementations across industries and use cases
Scattered monitoring across 125+ AI models slowed debugging. Unified telemetry lets teams trace issues from user sessions down to the GPU.
Untangling 30-call AI interactions consumed developers. Now, support teams use system traces to resolve context issues without engineers.
Engineers lacked visibility into a slow AI agent. Rebuilt low-level tracing exposed critical bottlenecks and accelerated LLM responses.
Scattered monitoring across 125+ AI models slowed debugging. Unified telemetry lets teams trace issues from user sessions down to the GPU.
Engineers lacked visibility into a slow AI agent. Rebuilt low-level tracing exposed critical bottlenecks and accelerated LLM responses.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Misclassified tickets drove needless escalations. Now, an AI agent auto-routes cases and surfaces 1-click resolutions for support teams.
Each incident sent engineers hunting for context from scratch, inconsistently across teams. Edwin AI now delivers it upfront.