Voice AI webhooks let OmniDimension push structured call data - the full transcript, a summary, sentiment, and any extracted variables - to any URL you choose the moment a call ends. Point that webhook at your backend, CRM, database, or automation tool, and every conversation turns into clean data your systems can act on automatically.
If you have ever wanted your phone calls to update a database, kick off a follow-up, or land in a system that OmniDimension does not natively integrate with, webhooks are the answer. They are the plumbing behind almost every custom integration - the building block that turns a spoken conversation into something the rest of your stack can read. This post explains what post-call webhooks are, shows an example of the payload OmniDimension sends, breaks down each field, and walks through receiving it in any backend.
Key takeaways#
- A webhook is an automatic HTTP request OmniDimension sends to a URL you specify when an event happens - here, when a call finishes.
- The post-call payload carries the transcript, a summary, sentiment, and the extracted variables your agent was told to capture.
- Webhooks are the foundation for integrations with systems that have no native connector - if it accepts an HTTP request, it can receive call data.
- Receiving one is straightforward: stand up an endpoint, read the JSON, verify it, and do something with it.
- OmniWorkflows is the most powerful way to consume webhook data without writing a receiver, and it can also send data out through a Custom API action.
- The hero use case is syncing every finished call into a custom system in real time.
What a webhook actually is#
A webhook is a "reverse API call." Instead of your code repeatedly asking OmniDimension "is the call done yet?", OmniDimension calls you the instant the call ends. It sends an HTTP POST to a URL you registered, with the call's data in the body as JSON. Your server receives it, reads it, and acts - all within seconds of the caller hanging up.
That inversion is what makes webhooks the backbone of real-time integrations. No polling, no delay, no wasted requests. The event happens, the data arrives, and your system reacts.
What the post-call payload looks like#
The best way to understand a webhook is to see one. Below is an illustrative example of a post-call payload - it shows the shape and the kinds of fields you can expect, not an exact, guaranteed schema. Field names and structure evolve, so always confirm the current format against your own test calls and the documentation before you build against it.
{
"call_id": "call_9f3a2c1b7e",
"agent_id": "agent_support_intake_01",
"status": "completed",
"duration": 184,
"transcript": "Agent: Hi, thanks for calling Acme Support. How can I help?\nCaller: My last order never arrived. Order 55231.\nAgent: I'm sorry about that - let me flag it for our team and have someone call you back this afternoon.\nCaller: That works, after 3 PM is best.\nAgent: Done. You'll hear from us after 3 today.",
"summary": "Caller reported that order 55231 was not delivered. Agent logged the issue and promised a callback the same afternoon.",
"sentiment": "slightly_negative",
"extracted_variables": {
"hot_lead": true,
"callback_time": "2026-09-01T15:00:00"
}
}
What each field means and how you would use it#
Field | What it contains | How you would use it |
|---|---|---|
call_id | A unique identifier for this specific call | Use it as the primary key when you store the record, and to de-duplicate if a webhook is retried. |
agent_id | Which agent handled the conversation | Route or report by agent - for example, group results by campaign or team. |
status | The final state of the call, such as completed, no-answer, or failed | Branch your logic: only process full conversations, and handle missed calls separately. |
duration | Length of the call in seconds | Feed dashboards, spot suspiciously short calls, and track talk-time trends. |
transcript | The full turn-by-turn text of the conversation | Archive it, run your own analysis, or attach it to a support ticket for context. |
summary | A concise, generated recap of what happened | Drop it straight into a CRM note or a Slack message so a human gets the gist in seconds. |
sentiment | How the caller came across during the call | Flag unhappy callers for a supervisor, or trigger a save-the-relationship follow-up. |
extracted_variables | The specific data points your agent was configured to capture, as named fields | Act on them directly - here, hot_lead: true routes to sales and callback_time schedules the return call. |
Extracted variables are the piece developers love most. You define what the agent should pull out of the conversation, and the webhook hands it back as clean, named fields - no transcript parsing required. "Is this a hot lead?" comes back as a value you can branch on immediately.
Hero use case: post-call sync to a custom system#
Say you run a support line on an internal tool that OmniDimension has no native connector for. You still want every call logged there automatically. With webhooks, you point OmniDimension's post-call webhook at an endpoint on your own server. When a call ends, the payload arrives - transcript, summary, sentiment, and extracted variables like issue_category and resolved. Your endpoint reads those fields, writes a ticket to your internal database, tags it by category, and flags any negative-sentiment call for a supervisor to review.
No native integration existed, yet the data flows in real time. That is the whole point - webhooks are what let OmniDimension work with systems it has never heard of. The same mechanism powers custom analytics dashboards, compliance archives, billing systems, and anything else that can accept an HTTP request.
How to set it up#
First, decide where the data should go. That destination needs a URL that can receive an HTTP POST - an endpoint on your backend, a serverless function, or an automation tool's inbound webhook URL.
Second, build the receiver. In whatever language your backend uses, expose a route that accepts POST requests, parses the JSON body, and reads the fields you care about. A minimal receiver just needs to acknowledge the request quickly (return a 2xx status) and then process the data - writing to a database, calling another API, or queuing a follow-up. Keep the heavy work asynchronous so you always respond fast.
Third, secure it. Treat the endpoint as public, so validate every incoming request before trusting it - check a shared secret or signature, and ignore anything that does not match. Log what you receive while you are building so you can see the exact shape of your own payloads.
Fourth, register the webhook URL in OmniDimension so post-call data is delivered there. Place a test call, watch the request land, and confirm your endpoint reads the transcript, summary, sentiment, and variables correctly. If you would rather originate calls programmatically as part of the same flow, the API lets you dispatch a call and then receive its results through your webhook.
If your goal is specifically to push call data into a CRM, the guide to integrating voice AI with any CRM or API shows the pattern end to end. And if you would rather not write a receiver at all, low-code tools work beautifully here - see how to build a voice AI agent with n8n, where an n8n webhook node catches the payload and routes it anywhere.
Native connectors vs. webhooks - which should you use?#
Here is the honest answer. If your destination is one of OmniDimension's native integrations - HubSpot, Salesforce, Zoho, Slack, WhatsApp, Email, Google Sheets, and others - use the native connector. It is less work and it is maintained for you.
Webhooks are for everything else: internal tools, custom databases, niche SaaS, or any bespoke logic a fixed connector cannot express. They are also the more flexible option when you need the raw payload to do something specific. Plenty of teams use both - native connectors for the common systems and a webhook for the one custom endpoint that makes their setup unique.
The most powerful middle ground is OmniWorkflows. Its visual builder can receive a webhook as a trigger and then run any action - including a Custom API call that forwards the data wherever you want, with conditions, delays, and waits in between. For a full walkthrough of chaining those steps, see how to automate a customer journey with OmniWorkflows.
The bottom line#
Webhooks are the quiet foundation under every custom voice AI integration. They take a finished conversation - transcript, summary, sentiment, extracted variables - and deliver it to any URL the moment the call ends, which means OmniDimension's voice AI platform can feed systems it has no native connector for. Use native integrations where they exist, reach for webhooks everywhere else, and let OmniWorkflows tie it all together. Once your calls become clean, real-time data, the rest of your automation practically builds itself.
Want the exact field reference and setup details? Read the OmniDimension documentation for the current webhook payload format and step-by-step configuration - then create a free account and fire your first post-call webhook.
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