Build vs buy
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.
01 · The objection01 · The objection
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.
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. That resolution layer is the difference between an AI feature and a dataset. It’s also the part nobody budgets for.
Transcript · 41:12
“…honestly we’ve been fighting with yapoYotpo since the redesign, the review widget keeps breaking checkout…”
LLM summary
The customer mentioned some frustration with a reviews tool after their redesign and said it has been affecting checkout.
A summary is prose. It reads well in a demo — and falls apart the first time someone asks a question of it.
Illustrative demo data
02 · What building really means
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.
0 standing operations on your team.
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.”
Illustrative demo data
03 · The recompute tax
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.
Naive build
0archive re-reads · 5 questions each
Telephos
0archive re-reads at question time
Compute once, read many. A summary you re-generate is a cost; a compiled, cited answer is an asset — read by everyone after.
Illustrative demo data
04 · The architecture
Four layers. One dataset.
This is what you’re buying instead of building — each layer standing on the one below it.
Raw call recordings
Layer 1
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.
One call, structured
Illustrative demo data
05 · The vernacular
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.
Six phrases, six loose strings. Nothing to count, nothing to join.
Illustrative demo data
One dataset. Every team’s next move.
Skip the supply chain. Keep the dataset.
14 days, your transcripts, real output — see the resolution layer work on your own calls.