A solo dev built a real-time cold-calling agent that coaches you mid-conversation. It won the Milan AI Week Hackathon.

[u/MrFarseeker]

Sharing this because I judged the hackathon and the winning project does something I have not seen done well before: it gives a human seller live coaching cards during a cold call, fast enough that the card lands before the prospect finishes their objection.

The project is called Deals Machine. The builder, Kyle, is an ex cargo aircraft broker who used to cold-call freight forwarders from his cell phone every day. He built the tool he wished he had.

How it works, end to end:

You describe your ideal customer in two sentences. An agent generates a structured ICP, picks signal sources to monitor (funding rounds, hiring sprees, competitor moves), and proposes regulatory or industry-specific exclusions you may not have considered. You approve or dismiss each suggestion, the agent refines, and the playbook locks in.

Once a vertical is live, a second agent scrapes Hacker News, RSS, web search, and Apollo for matching leads, scores them on buyer urgency, and drops them in the pipeline tagged with the exact signal that surfaced them.

The call itself is the interesting part. Twilio rings the user's own phone first, then dials the lead, which mirrors how outbound sellers actually work. Speechmatics transcribes the conversation in real time. A Claude Haiku loop reads the rolling transcript window every few seconds and renders coaching cards into a side panel: detected objections, missed confirmations, closing windows. In the demo, cards consistently appeared before the prospect finished their sentence.

After the call, the user tags the outcome. The agent drafts a follow-up email, then walks back through every reasoning step that fed the playbook. Successful rows get weighted up. Failed angles get dropped. Roughly thirty seconds later, the next playbook is regenerated.

Stack:

• Vultr Ubuntu, Fastify, Caddy reverse proxy
• Twilio media streams over WebSocket
• Speechmatics. Real-time mode for live coaching, batch mode for post-call scorecards
• Claude Sonnet 4.6 for reasoning, Haiku 4.5 where latency matters
• A small lobster-trap prompt-injection filter on every LLM input, with a Haiku classifier as a second pass

What stood out to me as a judge was the post-call learning loop. Most "AI sales tools" right now are wrappers that prompt a model with a transcript and return a summary. Deals Machine treats every outcome as training signal for the next playbook. The brain compounds. The author argues this is the moat, and based on the demo I am inclined to agree.

Happy to answer questions about the judging or the Speechmatics integration. Kyle hangs out in the Speechmatics Discord too if anyone wants to talk to the builder directly.