Sherlock Calls
for PostgreSQL + Vapi
PostgreSQL stores your application's core operational data and business records. Vapi runs your AI voice agents on any telephony stack. When you need to investigate across both, the evidence is split between two dashboards neither of which knows the other exists. Sherlock Calls bridges them — no code, no exports, no manual joins. Ask once from Slack and get a sourced answer in under 5 seconds.
TL;DR — What beta users get access to
- 1
Sherlock Calls connects to PostgreSQL, Vapi simultaneously — read-only, no code changes, no webhooks — and lets you query both with a single Slack message.
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Ask questions that neither PostgreSQL nor Vapi can answer alone. PostgreSQL holds every business record your app has ever created — but turning that into an answer requires a developer to write the query. Vapi shows agent sessions — not how they map to telephony errors or CRM results. Sherlock deduces the complete picture from both.
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No dashboard switching, no manual joins, no fog of uncertainty — ask in Slack and receive a sourced answer with evidence from every connected provider in under 5 seconds. The game is afoot.
<5s
Answer to any database + voice AI query
2
Connected platforms, 1 Slack question
0
Code changes or webhooks required
The Investigation Gap
What's invisible when you use PostgreSQL + Vapi without Sherlock
Each platform shows you its own data. But the questions that matter most live in the gaps between them.
Vapi AI agents run without the PostgreSQL application context that would change their answers
PostgreSQL holds the business records, product state, and event history that would transform your Vapi AI agent's response from generic to precise. But Vapi agents call without access to PostgreSQL — so they give the same answer regardless of what the data says.
Vapi AI conversation outcomes and PostgreSQL downstream business results are never correlated
PostgreSQL holds what happened after the Vapi AI conversation — did the record update? Did the issue resolve? Did the customer return? Whether Vapi AI conversations actually produce the right PostgreSQL outcomes is a question that requires joining both datasets.
PostgreSQL data patterns that predict Vapi AI escalation are invisible
Customers with specific PostgreSQL application states — certain usage patterns, recent errors, specific record conditions — may escalate Vapi AI conversations at much higher rates. That predictive signal exists in the data but is invisible without a cross-system query.
Cross-Provider Questions
What teams ask Sherlock about PostgreSQL + Vapi
Questions that would take hours to answer manually — answered in under 5 seconds from Slack.
- SC“Which Vapi AI conversation outcomes correlate with specific PostgreSQL record states at the time of the call?”
- SC“Show me PostgreSQL tables most frequently updated in the 24 hours following a Vapi AI conversation”
- SC“Find Vapi AI conversations that ended in escalation for customers with specific PostgreSQL application states”
- SC“Which PostgreSQL data patterns best predict whether a Vapi AI conversation will succeed or escalate?”
- SC“What's the PostgreSQL account health for customers who had multiple Vapi AI conversation failures this month?”
Beta Setup
Connect PostgreSQL + Vapi to Sherlock in 2 minutes
No code, no webhooks, no new dashboards. Beta users get direct onboarding support.
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Connect PostgreSQL
Add your PostgreSQL credentials to Sherlock Calls. Read-only access — no code changes, no webhooks, no PostgreSQL configuration required.
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Connect Vapi
Add your Vapi credentials. Sherlock indexes all AI agent sessions, call transcripts, and tool call logs automatically.
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Ask your first cross-provider question. The game is afoot.
Type any question about your combined PostgreSQL + Vapi stack in Slack. Sherlock queries all connected platforms in parallel, correlates the evidence, and returns a sourced answer in under 5 seconds.
FAQ
Common questions about Sherlock + PostgreSQL + Vapi
How does Sherlock Calls connect PostgreSQL and Vapi data?
- Sherlock uses read-only API access to both platforms simultaneously. When you ask a question, it queries PostgreSQL, Vapi in parallel, correlates the results by timestamp and shared identifiers, and produces a single sourced answer — the same way a good detective correlates evidence from multiple witnesses.
Do I need to set up any data pipelines between PostgreSQL and Vapi?
- No. Sherlock Calls is entirely pull-based — it queries both APIs on demand when you ask a question. There are no webhooks, no ETL pipelines, no data warehouses, and no code changes required in any of the connected platforms.
What kinds of questions can I ask about my PostgreSQL + Vapi stack?
- You can investigate anything that spans both platforms — table row counts and query latency, conversation success rate and latency, cross-platform costs, handoff patterns, and performance comparisons. Sherlock translates your plain-English question into the right API calls and returns the deduced answer.
Is my PostgreSQL and Vapi data stored by Sherlock?
- No. Sherlock Calls queries your data in real time and returns results directly to Slack — nothing is stored, indexed, or replicated in any Sherlock database. All data remains in PostgreSQL and Vapi and is accessed only during an active investigation.
How long does it take to set up the PostgreSQL + Vapi integration?
- Elementary — typically under 5 minutes total. Connect each platform with read-only credentials, install the Sherlock Calls Slack app, and ask your first question. No engineering, no dashboards, no onboarding calls required.
Apply for early access to Sherlock + PostgreSQL + Vapi
We're accepting a select group of beta users to shape the PostgreSQL + Vapi combination. Tell us about your stack and we'll reach out personally if you're a fit.
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