The demo takes a weekend.
The data takes years.
Every engineering team can wire an LLM to a transcript. The question isn’t whether you can build the summary — it’s whether you want to own everything underneath it.
A weekend LLM is not the product.
Pointing a model at a transcript and asking for a summary is a weekend project. We know — that’s the first thing we built too. It reads well in a demo and falls apart the first time someone asks a question of it.
The years went into the layer beneath: turning “Yapo” into Yotpo. A misheard name into a company. A rant into a labeled, cited mention with business context — who said it, about which vendor, in what category, on which deal. A summary is prose. A mention is a record you can count, filter, and stake a decision on.
That resolution layer is the difference between an AI feature and a dataset. It’s also the part nobody budgets for.
“…honestly we’ve been fighting with yapo since the redesign, the review widget keeps breaking checkout…”
Review widget conflicts with checkout after storefront redesign; frustration expressed by merchant’s ops lead.
Ecommerce-specific resolution: entity, category, sentiment, speaker role, citation — every field queryable.
The supply chain you’d own forever.
“Build” doesn’t mean writing a prompt. It means standing up four operations — and then running them every week the product exists.
Enrichment
Every name on a call needs to become a company, a role, a stack. Paid enrichment that never stops billing — and never stops going stale.
Resolution
“Yapo,” “the review tool,” “what we replaced Okendo with” — three ways one vendor shows up on real calls. Somebody has to make them one record.
Taxonomy
Reviews is not loyalty. Subscriptions is not retention. The category map drifts every quarter as the ecosystem ships — and your labels rot with it.
QA & evals
Every model upgrade, every new call source, every new category re-opens the question: is the output still right? The eval burden only grows.
None of it is a launch. All of it is a standing team. The build decision isn’t “can we ship this” — it’s “do we want to run this instead of our product.”
Even a perfect pipeline re-reads the whole archive every time someone asks.
Building the dataset is only half the bill. The other half is what it costs — every day, for every person — to get an answer back out of it. The naive way to answer “how do we beat Okendo?” is to re-read the raw call corpus at query time.
So the bill scales with people × questions × a growing archive. A hundred reps asking overlapping questions each spin up their own expensive sweep over the same transcripts. You feel it directly, in your own model bill.
Telephos inverts it. The costly pass — resolve, aggregate, cite — runs once, when a call lands. The recurring question is already a compiled, cited report. Reps read a page; nobody re-tokenizes the archive.
Compute once, read many. A summary you re-generate is a cost; a compiled, cited answer is an asset — read by everyone after.
Four layers. One dataset.
This is what you’re buying instead of building — each layer standing on the one below it.
Clean → business context
Transcripts in. Structured data out. Every call is resolved into business meaning — vendors named, categories assigned, sentiment labeled, every claim cited back to the moment it was said. Built for ecommerce, so “Recharge” is a subscriptions vendor and not a verb.
All department flows
One dataset feeds every team’s next move — battlecards for sales, churn flags for CS, roadmap input for product, referrals and handoffs for partnerships. And it flows back: each team’s input sharpens the call prep the next rep walks in with.
Partners trade the same structured signal
The de-identified provider graph — which vendors and agencies show up where, and how they’re trending — extends past the edge of your own call recordings. Their calls cover the markets yours don’t. No one’s buyer-level signal changes hands; the graph is vendor-and-agency shaped, not customer-shaped.
De-identified provider & agency signal only — never another company’s buyers.
Interact at scale
Because the layer below is structured, you can talk to it. Ask it questions in natural language, or wire it into the tools your teams already live in — Slack, your CRM, your own agents. Every answer traces back to a cited mention, not a vibe.
Most competitive-loss mentions cluster on onboarding speed; Okendo comes up in mid-market deals, Yotpo upmarket.
A horizontal AI tool summarizes a call.
Telephos speaks the Shopify ecosystem’s own vernacular.
Nobody does AI with business context like this — and nobody does it specific to ecommerce. It knows Klaviyo from Yotpo from Gorgias, an agency from an app, a migration from a rip-and-replace. That context is the product. The prompt was never the hard part.
Skip the supply chain. Keep the dataset.
30 days, your transcripts, real output — see the resolution layer work on your own calls.