PickupWindow
The timing model behind every placed call
A per-contact ML model that learns best-pickup days and hours from historical call outcomes — and refuses to schedule a call outside that window.
- ± 15 min Median accuracy after one year of data
- Per contact Learning — not a global office-hours guess
- Quiet Hours and holidays honored by default
PickupWindow is the quiet reason AIM-powered outreach sounds respectful on the phone. Every contact has a rhythm — meetings in the morning, focus work at noon, calls returned between appointments, inbox clean-up in the evening. A call at the wrong hour will never work; a call at the right hour might convert for one contact and be intrusive for another. PickupWindow learns that rhythm, per contact, across campaigns.
The model reads call outcomes: picked up, listened-and-declined, ignored, voicemail, busy. Over a few waves, a per-contact distribution emerges — this contact picks up between 14:00 and 16:00 on Tuesdays and Thursdays; that contact picks up only before 9 am on weekdays; a third contact only reliably answers on Fridays. The cadence planner consults the model before every scheduled call.
New contacts start with a segment prior — an enterprise buyer has a different default window than a small-business owner; a night-shift worker has a different window than a morning commuter. The prior is overwritten by that contact's own data as soon as a few calls land. A year into a programme, the platform's guess of a contact's pickup hour is within about fifteen minutes of their actual behaviour.
PickupWindow also returns the distance to the next window — if a campaign needs to call now, but this contact's next window is thirty-six hours away, the planner can choose a WhatsApp or SMS touch instead. Timing is not just "when to call" — it's also "when to not call".
Everything PickupWindow handles for you
-
Per-contact distribution
Days and hours learned individually — no global office-hours guess.
-
Segment priors for cold starts
New contacts start with an audience-based prior, refined as signals arrive.
-
Distance to next window
Planner can choose a non-voice touch when the next good hour is too far away.
-
Outcome-driven learning
Picked-up, busy, voicemail and ignored all feed the model with weighted signals.
-
Tunable quiet hours
Operators can bound the model to business hours, holidays and jurisdictional quiet-hours.
The when of every scheduled call
PickupWindow is read by the cadence planner before every call. It reads outcome data from the recorder and dialer, and writes the learned distribution back onto the contact record.
- CadenceConductor consults best-pickup hours before every scheduled call.
- VoiceDialer emits call outcomes that feed back into the model.
- AIM CRM stores the learned window as part of the contact profile.
- AIFlow runs the refine-on-signal pipeline.
Wire PickupWindow into your product today
Book a consultation with our founders and we'll walk you through the whole microservice stack — not just this one — live on your domain.