AI Cold Caller

Cold calling has two failure modes: low volume (humans burning out at 80 dials a day) and low quality (predictive dialers that hang up or open with awkward dead air). AI cold callers can fix both, but only if the conversation is actually good. That's where most early implementations fall apart — the latency is high, the AI talks over the prospect, and the script is rigid.

Open's stack is tuned for the first 30 seconds, where prospects decide whether to stay on the line. Median first-token latency is under 200ms, the TTS is streamed (so the AI starts speaking as soon as it has something useful), and barge-in is supported (so the AI yields when the prospect interrupts). The opener is configurable and disclosed (the agent identifies itself as AI when asked, and immediately if your compliance posture requires it up front).

On the qualification side, the AI runs the same discovery questions a good SDR would — but it does them while looking at the prospect's history in your CRM in real time. If the prospect mentions a competitor, the AI can adjust on the fly. If they mention a current pain point, it goes deeper. If they push back, it doesn't argue — it acknowledges, finds the next-best path, or graciously ends the call.

When the lead is qualified, the AI books a meeting on the right rep's calendar live (Google, Outlook, HubSpot Meetings, Calendly, Chili Piper). When it isn't, it logs the disposition in the CRM with a structured reason and the AI's reasoning trace. The rep wakes up to a calendar that's already half-full and a CRM that already has the disqualification context.

Compliance is non-optional on outbound. TCPA-style disclosures, DNC list management, time-of-day rules per region, opt-out detection, and consent capture are all included. PII redaction on transcripts is on by default.