A desktop co-pilot that listens to your sales call, and feeds you the answer, live.
No salesperson can hold the whole catalogue, the pricing, and every playbook in their head mid-call. The perfect answer usually arrives five minutes after the call ends.
- The right answer comes too late: you remember the ideal recommendation or the sharp follow-up question after the moment has passed.
- Chatbots don't help live: you can't type into an AI chat window while you're talking to a customer.
- Call audio is sensitive: shipping a customer's voice to a cloud transcription service is a non-starter for many teams.
- Off-the-shelf is English-first: our calls are in Greek with heavy English technical code-switching, which most tools mangle.
We wanted a co-pilot that listens, understands, and hands the rep the right thing to say, while the customer is still talking.
Listen
Captures the customer's voice through system audio (Meet, Teams, Zoom, or a phone bridge), no browser extension, no bot in the meeting.
Transcribe on-device
Local speech-to-text on the laptop's GPU. The audio never leaves the machine; only text goes onward. Handles Greek/English code-switching.
Reason
Every ~20 seconds, Claude reads the rolling transcript and pulls the relevant facts from your data via tool calls: recommendations plus questions to ask.
Surface
Glanceable cards stream into a small native window beside the call, paced so the rep is helped, not flooded. The call ends with an instant summary.
// figures from the delivered build; LLM cost is at the live recommendation cadence on Claude Haiku.
“A co-pilot that listens to the call and hands the rep the right answer, from their own data, while the customer is still talking.”
It listens to the call, privately
on-device · source-agnosticThe app captures the customer's voice straight from system audio, so it works with any meeting tool or a phone bridge, with no browser extension or meeting bot. Speech-to-text runs locally on the laptop's GPU: the audio never leaves the machine, only the text moves on. It's tuned for Greek with heavy English technical code-switching, so terms like "SQL Server" survive transcription intact.
Live talking points & questions
~20s cadenceOn a steady cadence, Claude reads the rolling transcript and surfaces two things: recommendations the rep can raise now, and follow-up questions to draw out more detail. They render as glanceable cards in a native window next to the call (paced so the rep gets help, not noise), and the session ends with a one-click summary.
Grounded in your data: the template
any catalog · via MCPThe model doesn't guess from a catalogue baked into a prompt; it pulls facts on demand via tool calls (MCP) from whatever knowledge you point it at: product docs, pricing, battle cards, case studies. We first wired it to live AWS documentation; swap the tools and the same app sells anything, always from fresh, citable sources.
Claude, or Claude on AWS
your billing, your choicePick the provider in settings: the Anthropic API (paste a key, go) or Claude on AWS: Claude Platform on AWS (generally available in 2026), which runs the same models and features through your AWS account for consolidated billing and EU residency, authenticated with an AWS-issued key and workspace. It also runs as an MCP server, so Claude Desktop can drive it for post-call deep-dives.
The stack is the product. We got to a hybrid local-STT + cloud-LLM design by rejecting the obvious ones.
What we rejected: a fully-cloud stack (cloud transcription + a frontier model on a 5-second cadence) ran ~$50–150/hour per meeting and sent audio off-device; a fully-local stack (a small open model on the laptop) was too slow: recommendations landed after the moment had passed.
What we shipped: transcription stays local on the GPU (private and real-time), and the reasoning runs on Claude Haiku in the cloud at a ~20-second cadence, about $1–2/hour, fast enough to stay in the conversation. We tuned the prompts, cadence, and phase logic against real recorded calls with a replay-and-diff eval harness across several industries.
Native Windows app
Ships as a single self-extracting .exe: no Python, no setup. A first-run wizard handles the speech model download and the provider key; after that it goes straight to the start screen.
A native window
A small always-there window renders live transcript, recommendations, and questions: built as a native desktop app, not a browser tab or a web service.
Preflight + sessions
A pre-call check verifies the key, audio path, and GPU so nothing fails live. Every call is a named session with history and an instant end-of-call summary.
services involved: product development · ai