From Raw Audio to Actionable Data, Automating Call Center Triage with Cortex AI

Last Updated on August 19, 2026 by Editorial Team Author(s): Krishnan Srinivasan Originally published on Towards AI. Powered by AI_TRANSCRIBE, turning recorded support calls into a structured, queryable feedback table. A call center runs on a routine most of us know without ever having worked one. A customer calls in. An agent listens, resolves the issue, then spends a few minutes after the call typing notes, picking a category from a dropdown, and writing a summary for the next shift or the reporting dashboard. That last part, the typing, is where most of the delay and inconsistency creeps in. This post walks through a small pipeline that removes that manual step entirely. Three short support calls go in as raw audio. AI_TRANSCRIBE is the function that makes the whole thing possible, since every downstream step, the categorization, the summary, the resolution log, all exists because the call is now text a query can read. On a personal note, I was recently named a Finalist for AI Excellence in the Snowflake Community Awards 2026 (APJ region), and voting is now open. If this series, or any of the work I have shared, has been useful to you, I would genuinely appreciate your vote here. (You can navigate to Page 7, the last page of the form, and find me, “Krishnan Srinivasan” under APJ region.) Thank you! Now, back to the pipeline. Snowflake recently extended AI_TRANSCRIBE to accept AAC directly. Most existing transcription demos still stick with WAV or MP3, so this one uses AAC end to end instead. The setup Three recorded calls stand in for a real support queue, each one manually scripted and recorded for this demo rather than pulled from an actual queue. Call one is a billing dispute. A customer was charged twice for a subscription, and the agent verifies the account before issuing a refund. Call two is a technical issue. A customer’s app crashes on photo upload, and the agent logs a bug before offering a workaround. Call three is an account access issue. A customer has been locked out since the previous day because a password reset email never arrived, and the agent traces it to a spam filter catching the reset attempts on the company’s end. All three files are AAC audio, a compressed audio format that gets used in most streaming services and modern phone recordings for its smaller file size, at a similar quality to older formats like MP3. Note: In a real deployment, these recordings would not need a manual upload step at all. A contact center platform typically writes call recordings straight to cloud storage as soon as a call ends, and an external stage or a Snowpipe trigger would pick them up automatically. For this post, uploading through Snowsight keeps the demo self contained and easy for anyone to reproduce without setting up a storage integration first. Step 1: Create the database and schema We will begin by creating the dedicated database and schema for this analysis. Step 2: Create the stage for storing the call recordings Step 3: Upload the recordings to stage. Click on Add data Choose the database, schema and the stage we just created. Click Upload. The three audio files are successfully uploaded to the stage. Step 4: Transcribe calls to searchable text AI_TRANSCRIBE is where the actual work of this pipeline begins, converting spoken audio into a plain transcript a query can act on. AI_TRANSCRIBE returns a structured object, not just a string. The transcript text sits under a text key, alongside metadata like audio duration. The query below pulls and flattens the transcript and duration out into their own columns, making them easier to reference in the queries that follow. We can examine the flattened results by querying the table: SELECT * FROM CALL_TRANSCRIPTS_FLAT; At this point the raw audio has already done its job. Everything from here forward works on text. Step 5: Classify and Summarize Every unresolved billing dispute or unlogged bug report costs real time and goodwill, and step three is where that cost starts getting recovered, the moment the call becomes searchable text instead of an audio file. This step takes the flattened transcripts and produces two AI generated columns for each call, a category and a summary, in a single pass. AI_CLASSIFY takes the category list as an argument rather than needing a separate model or lookup table, which keeps this approachable for a team that has not built a custom classifier before. With three calls spanning billing, a technical bug, and account access, all four category labels get a real workout instead of the same one or two showing up every time. It reads the transcript text and picks the best matching label from that list. The result comes back as a structured object containing the labels it assigned, so :labels[0]::STRING pulls out just the first one as a plain string, since that’s the label we want stored as the call’s category. AI_SUMMARIZE_AGG(transcript_text) generates a short natural language summary of the transcript, the same kind of note an agent would normally type up by hand after a call. In effect it summarizes the transcript text for each individual call. The output is call_analysis, a table where each row now has the original transcript plus a category and a summary sitting right next to it, ready for the next step to layer a resolution status on top. SELECT file_name, category, call_summary FROM CALL_ANALYSIS; The results confirm that the pipeline is working end to end, all three calls landed in the right category and got a concise, readable summary out of nothing but raw audio. support_call_1.aac, Billing. The transcript centers on a duplicate subscription charge and Maya issuing a fix, which AI_CLASSIFY correctly reads as a billing issue rather than, say, a general inquiry. support_call_2.aac, Technical Support. The app crashing on photo upload is a product bug report, and the model picked technical support over account access or billing, which is the right call since nothing in […]