Seven reasons teams choose Teneks

You get names, not "Speaker 2".

Everything you buy this category for rests on one question: who was talking? Per-rep coaching, talk-time, quoting a customer back to themselves, showing an auditor who committed to what. Teneks resolves the people in the room and labels them by name — not "Speaker 1", the actual names, applied throughout.

On a three-person call in a head-to-head test, a widely used notetaker produced six different speaker labels and left one turn in nine attributed to nobody. That is not a weaker version of speaker separation. It is the absence of it, and nothing downstream survives it.

When it cannot hear you, it does not invent something.

A tool that misses audio is manageable — the gap is visible and someone re-listens. A tool that produces fluent, confident, wrong text is not, because nothing in the output tells you to doubt it.

In the same test, three minutes of Estonian side-conversation came back as English sentences nobody had said, attributed to a speaker who was not in the room. Every word of it read perfectly.

A call that switches language mid-sentence is just a normal call.

"Supports 100+ languages" is a checkbox every vendor ticks. The real question is what happens when a meeting runs in English, drops into Estonian for three minutes while somebody finds a screen share, and comes back. English-first tools assume one declared meeting language, so a code-switch sits outside their model of the problem entirely.

This is architectural, not a tuning gap that closes with the next model release — which is why the difference between us and a strong English-first competitor is incremental on an English call and categorical on an Estonian one.

Every line carries a timestamp, so the transcript is a record.

Without a time on each utterance you cannot compute talk-time, jump to the audio, prove a retention policy was applied, or construct an audit trail. You have a text file, not a record — and every downstream feature you are being sold sits on top of something unprovable.

Competitor exports routinely ship with no timestamps at all. Ours puts one on every utterance, which is what makes "at 14:03 the customer raised price" something you can click.

The consent moment survives.

On a recorded call there is exactly one sentence with legal weight: the part where everyone agrees to be recorded. If that exchange does not survive transcription, the recording's compliance story does not either.

In the test, that turn was dropped entirely on one call, and the Estonian verb for "record" came out as a word that does not exist in the language.

Your numbers stay numbers.

A missing figure is a gap somebody notices. A corrupted figure is a number somebody acts on. In a sales intelligence tool that is the worst available failure mode, because nothing flags it.

In the test, a seat count of sixty was transcribed as six — restating a deal at ten times its real per-seat price, in a file that read perfectly.

Whatever you build on top inherits the transcript.

If you plan to point Claude, an internal agent, or an automated compliance sweep at your call archive, understand what a bad transcript does there: the check does not fail. It returns a confident wrong answer, and somebody acts on it. Transcript quality is not a feature sitting beside summaries and analytics — it is the floor under all of them, and no downstream model repairs a number that was never captured.