How does Sherlock unify multiple AI voice agents?

Sherlock connects to each agent's underlying telephony and voice AI providers — Twilio, ElevenLabs, and others — using read-only API credentials. It normalizes the data across providers into a common schema, so calls, transcripts, costs, and errors from all agents are queryable together from a single Slack interface. No custom integration code is required on your end.

What is cross-provider data normalization and why does it matter?

Different providers structure their data differently — Twilio's call records look nothing like ElevenLabs' session logs. Normalization means translating both into a common format so they can be compared and queried together. Without normalization, cross-agent or cross-provider analysis requires manual work in spreadsheets. With Sherlock's normalization layer, it's a plain-English question in Slack.

How long does it take to set up Sherlock across 6 agents?

Each agent requires connecting its underlying provider accounts — typically a 2-minute process per provider using read-only API credentials. For a deployment with 6 agents across two providers, setup typically takes under 30 minutes total. There is no code, no webhook configuration, and no changes to your existing agent setup.

Can Slack queries span all agents simultaneously?

Yes. When you ask Sherlock a question from Slack, it queries all connected agents by default unless you specify a particular one. You can ask 'what was the total call volume across all agents yesterday' or 'which agent had the highest failure rate last week' and Sherlock returns a unified answer drawing on data from every connected agent.

How is call data secured in Sherlock?

Sherlock uses read-only API credentials — it can query your provider data but cannot modify it or initiate calls. Data is processed in transit and not stored beyond what is necessary to answer your query. Sherlock is SOC 2-aligned and designed for enterprise voice AI operations where data security is a primary concern.

What metrics are available when querying across multiple agents?

All core metrics are available cross-agent: call volume, duration, completion rate, transfer rate, cost, failure rate, transcript content, and error codes. You can also ask comparative questions — 'which agent improved most this month' or 'find the agent with the most calls over 5 minutes' — and Sherlock returns ranked results across all connected agents.

All case studies

Real Estatetwilio elevenlabs

Unifying 6 AI Agent Teams into a Single Source of Truth

One of the country's largest real estate servicers ran 6 different AI voice agents with a separate dashboard for each. Sherlock became their single cross-agent source of truth in Slack.

Agents unified

<5 min

Time to answer

20h

Hours freed/week

TL;DR — What Sherlock found

1
One of the country's largest real estate servicers ran 6 different AI voice agents across commercial sales and support — with a separate dashboard for each, plus no unified view.
2
Sherlock connected all 6 agents across Twilio and ElevenLabs, normalizing call data, transcripts, costs, and errors into a single natural-language interface in Slack.
3
Any team member can now ask Sherlock a question from Slack and get cross-agent answers in under 5 minutes — freeing the ops team from 20 hours of weekly data wrangling.

The Problem

With separate dashboards for each AI agent and provider, the operations team spent hours manually stitching data together just to answer basic performance questions.

The Process

Sherlock connected to all 6 agents across Twilio and ElevenLabs, unifying call data, transcripts, costs, and errors into a single queryable interface in Slack.

The Solution

Now any team member can ask Sherlock a question in Slack and get cross-agent insights in seconds — no more dashboard hopping or spreadsheet wrangling.

Results

✓6 AI agents monitored from one place
✓Any question answered in under 5 minutes
✓Ops team freed up 20h per week

Use Cases

Questions the team asked Sherlock

SC
“Compare performance across all 6 AI agents this week”
SC
“Which agent has the highest transfer rate to human agents?”
SC
“Show me all failed calls across every agent in the last 24 hours”
SC
“What is the total call cost across all agents this month?”
SC
“Which agent is handling the most commercial property inquiries?”

Deep Dive

The full story

Running six distinct AI voice agents across commercial sales and support is operationally complex by design — different agents handle different property types, geographies, and conversation types. But the operational cost of that complexity was substantial: each agent had its own dashboard, each provider had its own log format, and answering any cross-agent question required hours of manual data reconciliation.

Sherlock's integration connected all six agents simultaneously, normalizing call data, transcripts, cost records, and error events into a single unified interface. The normalization layer handled the differences in how Twilio and ElevenLabs structure their event data, so Sherlock could compare agents directly — by call volume, completion rate, transfer rate, cost, or any combination — without the team needing to do any translation work themselves.

The operational transformation was immediate. Team members who previously spent 20 hours per week pulling data from six dashboards into spreadsheets now ask Sherlock questions from Slack and get answers in under five minutes. Cross-agent performance comparisons that once took a full day can now be done on demand, any time a question arises.

Multi-agent voice AI deployments are increasingly common as organizations scale AI-assisted operations across different business units. Unified observability — a single interface that spans all agents, all providers, and all metrics — is the prerequisite for operating these systems reliably. Sherlock was built to make that possible without requiring teams to build or maintain custom integrations.

FAQ

Frequently asked questions

How does Sherlock unify multiple AI voice agents?: Sherlock connects to each agent's underlying telephony and voice AI providers — Twilio, ElevenLabs, and others — using read-only API credentials. It normalizes the data across providers into a common schema, so calls, transcripts, costs, and errors from all agents are queryable together from a single Slack interface. No custom integration code is required on your end.
What is cross-provider data normalization and why does it matter?: Different providers structure their data differently — Twilio's call records look nothing like ElevenLabs' session logs. Normalization means translating both into a common format so they can be compared and queried together. Without normalization, cross-agent or cross-provider analysis requires manual work in spreadsheets. With Sherlock's normalization layer, it's a plain-English question in Slack.
How long does it take to set up Sherlock across 6 agents?: Each agent requires connecting its underlying provider accounts — typically a 2-minute process per provider using read-only API credentials. For a deployment with 6 agents across two providers, setup typically takes under 30 minutes total. There is no code, no webhook configuration, and no changes to your existing agent setup.
Can Slack queries span all agents simultaneously?: Yes. When you ask Sherlock a question from Slack, it queries all connected agents by default unless you specify a particular one. You can ask 'what was the total call volume across all agents yesterday' or 'which agent had the highest failure rate last week' and Sherlock returns a unified answer drawing on data from every connected agent.
How is call data secured in Sherlock?: Sherlock uses read-only API credentials — it can query your provider data but cannot modify it or initiate calls. Data is processed in transit and not stored beyond what is necessary to answer your query. Sherlock is SOC 2-aligned and designed for enterprise voice AI operations where data security is a primary concern.
What metrics are available when querying across multiple agents?: All core metrics are available cross-agent: call volume, duration, completion rate, transfer rate, cost, failure rate, transcript content, and error codes. You can also ask comparative questions — 'which agent improved most this month' or 'find the agent with the most calls over 5 minutes' — and Sherlock returns ranked results across all connected agents.

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