A QA desk listening by hand gets through about two calls in a hundred. Nirikkhon AI transcribes all of them in Bangla and English, scores each one against your own SOP, and shows the line in the transcript that earned the score.
Two hundred agents handling forty interactions each is eight thousand conversations in a day. A desk listening by hand gets through about one per agent. Everything else carries the same regulatory exposure and none of the scrutiny.
An illustrative model rather than measured client data: two hundred agents at forty interactions each, against the two per cent sample a manual team can sustain. Your own volumes will differ, and the ratio is what matters.
Inbound broadband fault, 4:12, Bangla
0:19
The agent checks who they are talking to
Agent
অ্যাকাউন্টটা দেখার আগে নিরাপত্তার জন্য আপনার জন্মতারিখটা বলবেন?
Before I open the account, may I have your date of birth for security?
What the audit did with it
Verified the customer's identity before opening the account
20The answer keeps the words that produced it. Click the timestamp in the console and the recording seeks to 0:19, so a disputed score takes about fifteen seconds to settle.
1:12
The customer's patience runs out
Customer
কাল থেকে তিনবার ফোন করেছি, কেউ কিছু করেনি।
I have called three times since yesterday and nobody has done anything.
Sentiment, turn by turn
Turn 14 of 38, the sharpest drop in the call. The same pass reads the reason the customer rang, how the call ended, and whether a cancellation was in play.
2:31
The agent commits to a time
Agent
আজ রাত এগারোটার মধ্যে ঠিক হয়ে যাবে, আমি নিজে দেখছি।
It will be fixed by eleven tonight — I am looking at it myself.
Commitment on the record
Fault cleared by 23:00, same day
Spoken at 2:31, with the agent naming themselves as the owner
Promises, dates and figures come out of the call as their own record, so a supervisor can see what was undertaken without listening to it.
3:24
The close, one step short
Agent
আর কোনো সাহায্য লাগবে? ধন্যবাদ, ভালো থাকবেন।
Anything else I can help with? Thank you, take care.
What the audit did with it
Offered the matching recharge before closing the call
15The data had run out, and the pack that fixes it was never mentioned. This is the kind of miss a two-percent sample never sees, and it is worth fifteen points on every call it happens in.
4:05
The call ends and the scorecard closes
Identity handled correctly and the fault explained plainly. The recharge was never offered, and the customer had already called three times before this one.
A repeat contact inside seven days, so your rules raise it for a supervisor to look at. A question marked fatal would have taken the whole call to zero instead.
These are screens from the running product, on a tenant loaded with demonstration data. Nothing here is a mock-up.
A recording lands and the queue takes it from there. When a stage fails it retries by itself without redoing the stages before it, and every job is visible while it moves.
Who spoke, when, and for how long, with hold and talk time split out.
Why the customer called, how it ended, and whether a sale or a cancellation was in play.
Sentiment turn by turn, plus the promises, dates and figures the agent committed to.
The weighted scorecard, answered against your SOP with the evidence attached.
A few factual lines a supervisor can read instead of the whole transcript.
Afterwards it looks for repeat contacts from the same customer and runs your alert rules over the result. A recording already ingested is recognised by its contents and skipped, so re-scanning a folder is safe.
Regulated, high-volume and multi-site environments — anywhere quality has to be evidenced rather than asserted.
Your SOPs, scorecard, roles and reference data are configured with you, then validated against calls you already know the answer to.
Map channels, volumes, skills, centres and the QA rubric you already run.
An agreed rollout plan
Load the SOP library, weighted questions, fatal rules, roles and data scope.
Your rubric, live
Recording source or watched folder, reference data, branding and model settings.
Interactions flowing
Run calls you have already scored, compare automated against human, and tune prompts and thresholds.
Scores you trust
Roll out by centre or channel, train QA and team leaders, review coverage monthly.
Ongoing optimisation
The fastest way to judge this is to let it score conversations your own QA team has already audited, then compare line by line. The demo tenant is loaded with processed interactions, audits and alerts if you would rather look around first.
Your SOPs, scorecard, fatal rules and a sample of recordings or chat transcripts.
A walkthrough on your own calls — evidence, timestamps, verdicts and the reports that follow.
Variance and agreement measured against your own reviewers, question by question, in the calibration view.
A rollout plan and commercial terms priced against your actual operation, not a feature tier.