The Voksha Call Intelligence Report

A million calls is enough to stop guessing. Voksha’s AI receptionist crossed that mark in roughly six months, and the pattern underneath it is consistent: demand does not respect business hours, most callers want one of a few simple things, and the moment a call goes unanswered is the moment a customer starts dialing someone else. This report breaks the corpus into five questions.

01 · When people call

A third of demand arrives after the lights go off.

Across the corpus, 32% of calls land outside standard 9-to-5 weekday hours, and 23% come in on weekends. (These overlap — weekend daytime calls sit inside both — so we report them as two separate lenses, not a sum.) The point either way is the same: a large, predictable share of inbound calls happens precisely when a traditional front desk is empty.

The two sharpest pressure points are the morning rush, before staff are settled and phones are covered, and the two hours immediately after 5pm, when offices empty but customers are just getting home and finally have time to call. Those windows are where most missed calls cluster.

32% & 23%

Share of Voksha calls arriving outside 9–5 weekday hours, and on weekends — the windows a 9-to-5 front desk cannot cover.

Voksha data · 1,000,000+ calls · Dec 2025–Jun 2026

02 · Why people call

Callers want a few simple things — and most of them are bookings.

Sorted by primary reason for the call, the corpus is dominated by scheduling. Two out of three calls are someone trying to book.

Appointment booking: 66%
Questions & information (FAQ): 32%
Other: 2%

A second, overlapping read tags the purpose behind the call. Because a single call often carries more than one intent — a new prospect asking a billing question, for instance — these tags are counted independently and add to more than 100%: 64% of calls involve a new lead or sales inquiry, 38% involve billing or support, and 8% are something else.

The takeaway for any business reading this: the calls you are most likely to miss are not edge cases. They are bookings and brand-new leads — the highest-intent, highest-value calls you get.

Voksha data · primary-intent and multi-intent tagging across the corpus

03 · Who answers

The AI finishes 85 of every 100 calls on its own.

85% of calls are handled end-to-end by the AI — booked, answered, captured, or resolved — with no human involved. The remaining 15% are handed to a person, almost always for genuinely complex or judgment-heavy situations the business wants a human to own. Average handle time across the corpus runs 7 to 8 minutes, long enough to actually resolve a request rather than deflect it.

Resolved fully by AI: 85%
Transferred to a human (complex cases): 15%

Voksha data · resolution and handoff rates · average handle time 7–8 min

04 · What language people speak

One in three callers isn’t speaking English.

67% of calls are in English — and even that figure spans a wide range of accents, from American to British to Australian, that trip up rigid speech systems. The other 33% come in other languages, led by Spanish, Chinese, Korean, Hindi, Japanese, and French.

That is a third of inbound demand that a single-language front desk either mishandles or loses outright. We go deep on this in Report R-03: The Language Gap.

English (all accents): 67%
Other languages (Spanish, Chinese, Korean, Hindi, Japanese, French, +): 33%

Voksha data · detected call language across the corpus

05 · Recovery & trust

Nearly nine in ten recovered calls convert.

When a call does slip through, Voksha calls back inside 90 seconds — and 86–90% of those callbacks connect and convert. The narrow window matters: callers who don’t hear back fast move on, so recovery is mostly a speed problem, and speed is where an always-on system wins.

86–90%

Of 90-second missed-call callbacks that connect and convert — turning a near-miss back into a customer.

A system that hardens itself

Trust is the other half of the story. Across more than a million live calls, Voksha held a 99.96% guardrail integrity rate — resisting social-engineering attempts that lead human staff to disclose information they shouldn’t. In the rare instances where an edge case surfaced, the system flagged the gap, the missing rule was added, and the guardrail closed. The corpus didn’t just measure the platform; it trained it. Every interaction is logged in a complete audit trail, and the platform operates in line with HIPAA and privacy requirements.

Voksha data · guardrail integrity across 1,000,000+ live calls

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