Draft Documentation
This guide is currently in development. Content may be incomplete or subject to change.
Datasets & Enrichment
Upload your own operational data — call lists, contact-center exports, campaign rosters — and Auralytik pairs each row with the matching AI evaluation. You get the data you already have, augmented with audio quality scores, typification, sentiment, and a deep link to the recording, all in one place.
In this guide
What is a Dataset?
A Dataset is an Excel or CSV file you upload to Auralytik — typically a call-list export from your dialer or CRM. Once uploaded, Auralytik analyzes the columns, matches each row to its corresponding AI evaluation, and gives you back an enriched view that joins your operational data with Auralytik's quality & typification analysis.
Bring your file
Excel or CSV up to 100 MB / 250k rows
AI-tagged columns
Auralytik suggests what each column represents
Multi-strategy match
Customer ID → agent → time. Falls back gracefully.
Auditable
Every match shows its strategy and confidence
Excel export
One click to download original + enriched columns
Private or shared
You choose who else in your workspace can see it
When to use this: you already track calls or interactions in a spreadsheet or BI export and want each row paired with the AI's findings — call score, critical-error flag, typification, recording link — without exporting from Auralytik and reconciling by hand.
Uploading a Dataset
From the Datasets module, click New Dataset and walk through three steps:
- 1
Name & visibility
Give the dataset a name and choose Private (only you can see it) or Workspace (everyone in your workspace with the right permissions).
- 2
Pick your file
Drop in your
.xlsxor.csv. Auralytik reads the first sheet, samples the rows, and asks an AI to propose what each column is — phone, timestamp, agent name, customer RUT, and so on. - 3
Confirm the schema
Review and adjust the column types in the wizard. The dropdown values that drive the matcher are Customer ID (RUT), Agent Name, Timestamp, Phone, Campaign Name, and a few others. Pick which enrichment sources to enable, then click Confirm Import.
Dataset upload wizard with AI-discovered columns
Screenshot coming soon
Heads-up: the higher the AI confidence, the more you can trust the suggestion. Anything tagged below ~80% is worth a second look — especially the columns that the matcher depends on (RUT, Agent Name, Timestamp).
Schema & Column Types
Every column gets a semantic type. Most are display-only, but a few drive the matcher:
| Type | What it does |
|---|---|
| Customer ID (RUT) | Strongest match key. Joins directly to the customer ID on the evaluation. Strip non-digits — "12.345.678-9" and "123456789" map to the same key. |
| Agent Name | Disambiguator. Normalized (lowercased, prefixes like CBZ_C1_ stripped, accents removed). |
| Timestamp | Used to pick the nearest call when several match. Naive times are interpreted in your workspace's timezone. |
| Phone | Reserved for phone-based matching (planned). Stripped to the last 9 digits for Chilean numbering. |
| Campaign Name | Disambiguator for time-only matches. |
| Agent Typification | Marks a column that holds the agent's manual call result — used later to compare against the AI's typification. |
| Operation ID, Agent ID, etc. | Stored verbatim. Available to future match strategies and to the Excel export. |
| Pass-through / Unknown | Carried through but not used for matching. Pick this for columns that are display-only. |
How Matching Works
Auralytik tries to pair each row in your file with one evaluation in the platform. It works through a series of strategies, strongest first, and stops at the first hit. The strategy used is recorded on every match so you can audit it.
rut+agent+same-dayCustomer ID + agent name + same calendar day. The precise, high-confidence case.
rut+agent+nearest-daySame customer + same agent, but the eval is on a different day within the search window. Common for follow-up calls.
rut-only-cross-agentSame customer, agent disagrees. Useful when the file's agent column is stale or the call was transferred. Worth a quick audit before trusting the typification.
agent-name+timestamp-NminFallback when no RUT match exists. Pairs by agent name + the call's timestamp within a window you control (defaults to 15 minutes).
Why some rows don't match: the customer ID from your file may simply not have an evaluation in the time window — the call may not have been recorded, the customer may not have been contacted, or the call may have been classified as invalid. Auralytik will tell you exactly how many rows fall in each bucket and which strategy was used for the ones that did match.
Reviewing Results
Once the import finishes, the dataset detail page shows a summary and the row-by-row grid:
Total rows
Every row your file contained, including ones that didn't match.
Matches
Rows where the matcher found at least one evaluation. % shown is of total rows.
Ambiguous
Rows where two or more evaluations were equally good fits — the matcher picks the nearest by time and flags the row.
Typification disagreements
Where the agent's manual call result differs from the AI's — your strongest QA signal.
Dataset detail page with summary tiles and grid
Screenshot coming soon
Click any row to open the drill-down panel. From there, you can:
- See the AI's scoring, critical-error flag, and typification.
- Open the evaluation deep-link to listen to the recording.
- Check which match strategy was used and its confidence.
The toolbar's Export to Excel button downloads the original file plus enrichment columns — typification category, average note, sentiment, evaluation deep-link, and the matcher's confidence — so you can hand the file back to whoever provided it with the AI's analysis baked in.
Editing Schema & Mapping
After confirming the import, you can still change how the dataset is matched without re-uploading:
Edit Schema
Open the column-type dialog and re-tag any column. Saving triggers a re-parse: Auralytik rebuilds every row from your original file using the updated tagging, then re-runs enrichment. Use this if the AI mis-tagged a column or if you forgot to mark the RUT column as a Customer ID.
Edit Mapping
Tune the match settings — enabled sources, time-window minutes — without re-parsing the file. Saving triggers a re-enrich only. Use this to widen or narrow the matcher's tolerance.
Re-parse vs re-enrich: a re-parse re-reads the blob and rebuilds every record (1–3 minutes). A re-enrich keeps the records as-is and only re-runs the matcher (faster). The dialog warns you which one is about to happen.
Permissions
Datasets is governed by a dedicated Datasets permission area in your workspace settings:
- View — see workspace-visible datasets and their results.
- Create — upload new datasets.
- Edit — change schema/mapping and trigger re-enrich on existing datasets.
- Full — also delete datasets.
Visibility, separately: even with View permission, a user can only see datasets marked Workspace. Private datasets are visible only to whoever uploaded them. Switch the visibility at upload time, or never — there's no exposing a private dataset by accident.