HOW IT WORKS

From raw conversations to a finding you can act on

A batch of transcripts or reviews goes in one end. Six findings come out the other, each with its evidence attached. These are the steps in between.

01

Get data in

However your data lives today — raw call audio, transcript exports, conversational data, reviews — your success manager works with your team to get it ready for Synergi.

RAW AUDIO OR TRANSCRIPTS REVIEWS AND CHAT MANAGED PREPARATION
02

Prepare

Every file is checked before it is accepted, personal identifiers are masked, and duplicates are dropped within the project.

VALIDATION AT THE DOOR REDACTION BEFORE ANALYSIS DEDUPE PER PROJECT
03

Analyze

Six analyzers read the full batch — every record, never a sample — and their results are checked against each other.

SIX PASSES EVERY RECORD, NO SAMPLING CROSS-REFERENCED
04

Read

Each analyzer returns one page that leads with its finding. Every claim opens to the words behind it, and the whole batch exports.

FINDING FIRST EVIDENCE UNDER EVERY CLAIM EXPORT

Minutes after upload, not weeks after the quarter.

PHOTO · MARVIN MEYER / UNSPLASH
THE OUTPUT

What a finding looks like

Churn Analysis 286 TRANSCRIPTS

122 customers are at immediate risk, and service — not price — is why.

Immediate
42.7%
122 CUSTOMERS
High risk
38.8%
111 CUSTOMERS
Avg prob
0.61
SCALE 0 TO 1
High 111
Medium 107
Low 68
REPRESENTATIVE OUTPUT · ONE REAL BATCH, ONE ANALYZER OF SIX

The claim comes first

The sentence at the top is the finding itself — who, how many, and why — written to be repeated in a meeting without the screen.

Counts, not just percentages

42.7% is a slope; 122 customers is a workload. Every figure carries the count it came from, so the size of the problem is never in question.

Shapes chosen for reading

A composition bar, a small KPI row, a ranked list — each shape is chosen because it answers the question fastest, not because it fills a grid.

Cross-referenced

All six analyzers read the same batch, so a churn claim can be checked against sentiment drivers and journey breaks from the same records.

DATA IN, DATA OUT

Exactly what goes in, exactly what comes back

Accepted inputs

INPUT · SHAPE
Call transcripts.txt, one call per file
Reviews.csv, one review per row
Metadatacall id, date, queue, agent code
LanguagesEN, AF, ZU, ST — mixed freely
Volumebatches of 50 to tens of thousands

Returned per batch

OUTPUT · WHERE
Six analyzer pagesin the app
Evidence drill-downper quote
ExportPowerPoint (coming soon), all six payloads
Costper record, visible in-app
Turnaroundminutes, not weeks
IN PRACTICE

The first month, week by week

WEEK 1

Access and first batch

Accounts are invited, your intake is set up, and your first real batch goes through the pipeline the same day.

WEEK 1

First findings review

You read six pages against what you believed about the batch. This is where the tool earns the second batch — or doesn't.

WEEK 2

Second batch, trends appear

With two batches in the same project, movement between them starts to mean something: what grew, what faded, what held.

WEEK 3+

Part of the operating rhythm

Batches land on a cadence you choose, and findings reach the meetings where the decisions are already being made.