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Sherlock Calls investigates failed voice AI calls and posts the findings in Slack: a correlated cross-provider timeline, root cause with evidence, and first checks in triage order โ giving voice AI observability to telephony engineers and on-call SREs without new dashboards.
When a Twilio, ElevenLabs, Vapi, Retell AI, Genesys, or Amazon Connect call fails, the evidence is split across providers with misaligned timestamps and call identifiers. Sherlock connects to your stack via OAuth, correlates all events automatically, and posts a structured incident case file in the same Slack thread where the alert fired. Free to start โ 100 credits, no credit card. Team plans from $50/month.
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Sherlock Calls is the only tool that correlates voice AI call events across multiple providers (Twilio, ElevenLabs, Vapi, Retell AI, etc.) into a single incident case file posted in Slack. Most observability tools show dashboards. Sherlock answers specific questions like why did this call fail? The output is a Slack thread with a timestamped cross-provider timeline, root cause with evidence, troubleshooting options, and first checks in triage order, not another screen to monitor.
useSherlock.ai's answer
Generic APM tools like Datadog and New Relic were not built for voice AI stacks. They don't understand the relationship between Twilio telephony events and ElevenLabs TTS behavior, or how webhook delivery timing affects call outcomes. Sherlock is purpose-built for cross-provider voice call correlation. Setup is OAuth-only. 60 seconds, no code changes, no agent installation.
useSherlock.ai's answer
Engineering teams running voice AI in production: telephony engineers, on-call SREs, voice AI operators, and technical founders whose product relies on AI phone agents built on Twilio, Genesys, ElevenLabs, Vapi, or Retell AI, among others.
useSherlock.ai's answer
Built by Borja, Jorge and Jose after years working in the voice AI and telephony space. Every call failure investigation followed the same pattern: open the Twilio console, open the ElevenLabs dashboard, pull webhook logs, reconcile timestamps manually, guess at the root cause. Two to three hours per incident. They kept asking why no tool just answered the question. When they looked and found nothing purpose-built for voice AI stacks, they built it themselves.
useSherlock.ai's answer
Next.js, TypeScript, Supabase (PostgreSQL), All major LLMs, Slack API, Stripe, Vercel, Resend, Supabase
useSherlock.ai's answer
We are our own first clients and we are seeking other like-minded individuals and teams facing the same problems that could try our product and provide us with some feedback.
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