Every tool here claims an accuracy percentage. None of them can show you how it was measured. We took a different approach, and it shapes the whole product.
said or not said
checkable against the record
a judgement call
not reported
Look at any AI quality-assurance product and you will find a number: 90% accurate, 95% accurate, 96% accurate. Sometimes an outcome: a 25% CSAT lift, a 49× efficiency gain.
Now try to check one. There is no published methodology, no dataset, no independent evaluation. The number was measured by the company selling it, against calls you cannot see, using a ground truth they defined. It is not a lie exactly. It is just not information.
We could publish one too. It would take an afternoon, nobody would question it, and it would sit on the homepage looking exactly as credible as everyone else's. That is the problem: an unfalsifiable claim is worth the same whether or not it is true, so the market cannot tell honest vendors from careless ones.
Figures quoted publicly by tools in this category, 2026.
Instead of one global number about all calls, every parameter on every call carries its own confidence figure and the transcript lines it was judged on.
That is a smaller claim and a far more useful one. You do not have to trust an aggregate. You open the call, read the quoted line, and decide for yourself whether the score is right. That is the only verification that has ever mattered to a QA manager.
It also fails visibly. When the model is unsure, you see a low number rather than a confident wrong answer. When it reports nothing at all, you see a dash. We could substitute a plausible default there and nobody would ever know, which is precisely why we do not.
Every claim must be backed by shipped behaviour.
Each of these would help us sell. Each is absent from this site on purpose, and if you find one anywhere on it, we have made a mistake and would like to know.
We have no benchmark you could audit. Every figure in this category (90%, 95%, 96%) is measured against a private dataset by the vendor selling it. Ours would be too.
It varies with call length and scorecard size, and we do not yet have distribution data from real usage. A guess printed as a number becomes a promise, and every audit that costs more becomes a support ticket.
You can build a scorecard that checks the disclosures a regime requires. That is a control you operate. It is not us being HIPAA or PCI certified, and the two get blurred in this market constantly.
Masking is instructed during transcription and is best-effort by construction. It catches things a regex would not, and it can miss. Saying so costs us deals and it is still the right call.
There are none on this site because there are none yet. When there are, they will be named and checkable.
Speed depends on your call lengths, your scorecard and upstream capacity. "Under 30 seconds a call" is easy to write and impossible for you to verify before buying.
A buyer comparing spec sheets will find our limits tables listing no SOC 2, no SSO, no CRM integrations and no real-time assist, next to a competitor page showing green ticks all the way down. On that comparison we lose.
We would rather lose it there than three months into a deployment. The teams we are right for are the ones who read the limits and recognised their own situation in them.
If you need enterprise integrations and a signed security report, buy the bigger platform. We mean that.
QXAI is built and operated by Sow Tech, from Kancheepuram, Tamil Nadu. We are not going to describe ourselves as veterans of a 600-seat operation, because we are not, and that story is checkable.
What we are is a product priced in rupees, sold without a sales call, that works on the languages Indian floors actually run, and that will tell you what it cannot do before you pay for it.
If something on this site overstates, write to support@qxai.in and we will correct it.