Platform · Playbook
LiveFour voices around a tray is the normal case, not the edge case.
Recognition, diarization and scoring built for a premium retail floor, with no voiceprints and no third-party consumer AI APIs.
Personal details redacted at ingestion. US and India data residency options.
A scored conversation
Scored conversation
Bridal counter · 31 minutes · Northgate Fine Jewelers, Store 14
Associate
Is this for an anniversary, or something you have been planning a while?
Customer
Our tenth. I have a number in mind but I want to see the options first.
Detected behaviors
Estimated value not offered in this conversation: $1,240
Sample data on a sample playbook.
Capabilities
What the playbook layer does.
Speech recognition tuned for a retail floor
Store acoustics are hostile: music, tile, glass cases, a polishing motor, two conversations one metre apart. Recognition and diarization are tuned on floor audio rather than on clean call-centre recordings.
Multi-party conversations, handled as the normal case
A walk-in is rarely one person. It is a couple, a parent, a friend giving an opinion. Four voices around a tray, interrupting each other, is structurally harder than a two-party phone call, and it is precisely where tools built for field sales model poorly.
Speaker roles from seat and shift, not voiceprints
Gaincraft does not build biometric voice templates. Roles are assigned from the roster and the capturing zone, which keeps the system outside biometric privacy statutes and out of a category no retailer wants to explain.
Two-tier scoring
A classifier runs on every conversation, so coverage is complete. Deeper analysis runs on flagged conversations and on a random sample, which is how quality is audited without pretending every conversation needs the heaviest model.
Personal details redacted at ingestion
Card numbers, addresses, phone numbers and identity numbers are removed as the conversation is ingested, before scoring and before any human can open it.
Processed on Gaincraft infrastructure
Audio and transcripts are processed on infrastructure Gaincraft operates. Your customers’ conversations are not sent to third-party consumer AI APIs. This is a material security claim and we will document it for your review.
Where the playbook comes from
Derived from your own strongest sellers, not a template imported from another category.
Roughly seventy percent of the behaviour taxonomy is shared across premium retail. The other thirty percent is built from your own top performers, and that is the part your competitors cannot buy.
Shared across premium retail
0%
Greeting, discovery, budget framing, objection handling, follow-up. Pre-built.
Built from your top performers
0%
Your brand’s language, your assortment, your season. Yours to keep.
Conversations scored
Every one
Which is the only way a quartile comparison means anything.
Every retailer has a person who outsells the counter median and cannot explain why. When they are promoted or leave, the knowledge leaves with them. With every conversation scored, the answer stops being folklore: the behaviours that explain the spread are counted, clipped in your own store’s words, and handed to the next counter as tomorrow morning’s coaching prompt.
In practice
How this works in a store
A couple walks into the bridal counter with the bride’s mother. Three customer voices and one associate, over thirty-one minutes, with two breaks while trays are fetched. Diarization separates the speakers, and the roster tells the system which seat was staffed, so the associate is identified without any voice biometric.
As the conversation ingests, the mother reads out a card number to check a balance. It is redacted before scoring. The classifier then marks behaviors: occasion asked, budget framed as a range, second item not offered, coverage not explained.
Because this conversation was flagged — a high-value bridal consultation with a missed attach — it goes to the deeper tier, which produces the twenty-second clip and the estimated value of what was not offered. A random sample of ordinary conversations goes through the same deeper tier every week so we can measure whether the classifier is drifting.
Technical detail
The technical detail, in plain language
- Coverage
- The first tier runs on every eligible conversation on a capturing counter. Nothing is sampled at this stage.
- Diarization
- Speaker separation is acoustic. Identity comes from seat and shift, so a customer is never named and an associate is never voiceprinted.
- Languages and code-switching
- Conversations that switch language mid-sentence are handled natively, because the training corpus was built that way rather than patched for it. Supported language list: [CONFIRM].
- Redaction
- Pattern and context detection removes payment, contact and identity data at ingestion. Redaction happens before storage of the transcript.
- Processing location
- Gaincraft-operated infrastructure, with US and India data residency options. No conversation content is sent to third-party consumer AI APIs.
- Quality audit
- A weekly random sample is dual-scored and compared against manager review decisions, which is how classifier drift is caught early.
FAQ
Questions we get asked
Bring us a conversation and your playbook.
On a demo call we encode three of your behaviours and score a real conversation against them, end to end.