440K+Patients Served / Year (BBH)
Healthcare & Hospitals
Augment, Don't Replace.
Read the case studyCase study · AgroTech & D2C Logistics
Voice AI for AgroTech & D2C: Context-Aware Post-Dispatch and NDR Recovery.
India's D2C logistics reality is unforgiving. A meaningful share of all shipments hit a delivery exception, and if the carrier cannot resolve it within its attempt window, the package converts to RTO and the seller absorbs the reverse-logistics cost on top of the lost sale. AgroTech compounds the problem with rural customers spread across Hindi, Telugu, and Tamil belts, narrow phone reachability windows, and seasonal cycles where a missed delivery can cancel the sale outright. Kathan Voice OS, running on the Alchemyst context layer, confirms every dispatch before the carrier knocks and recovers failed deliveries within hours of an exception, with each customer's order, address, and attempt history already in context.
41.7%Aggregate Connection Rate
Recover Revenue Before It Becomes RTO.
01 · The problem
Post-Purchase Operations Don't Scale on Old Infrastructure
A majority of online orders in India are cash-on-delivery, and a meaningful share of all shipments hit at least one delivery exception. SMS and email leave read receipts, not reschedules. Staffing human BPO agents across three or four languages is operationally heavy and economically punishing. When NDR recovery does happen at all, second-attempt cohorts collapse: legacy outbound systems treat every retry like a cold call, so the harder-to-reach leads, by definition the ones already flagged, get the worst conversion. The system forgets everything it learned on the first attempt.
02 · The context layer
Two Workflows, One Context Layer
Kathan runs two complementary motions on the same context layer, so what one agent learns the other already knows. The post-dispatch confirmation agent calls every dispatched customer to verify address, availability, and preferred delivery window before the courier attempts delivery. The NDR recovery agent calls back within hours of a failed attempt, with the specific failure reason already in context (wrong address, unavailable, refused, unreachable) and the right remedy ready: correct the address, reschedule the slot, or convert COD to prepaid. A farmer flagged as unreachable on attempt one hears a different conversation than a farmer who refused delivery.
- Per-customer state tracking so address corrections, language preferences, and prior attempts always reflect the latest truth.
- Sub-second context retrieval so real-time conversation never stalls on a lookup, even on flaky rural connections.
- Native Hindi, Telugu, Tamil, and 9+ Indian languages, with regional phrasing tuned for rural and tier-2/3 audiences.
- AI-to-human escalation for the small share of cases that need a live agent, with full transcript and context handoff.
03 · Where it fits
Where It Fits in the Order Lifecycle
Three workflow gaps drive the bulk of recoverable post-purchase loss. All three currently rely on SMS, email, or a thin BPO layer that does not scale across languages or attempt windows, and none of them remember the customer between attempts.
01Pre-Delivery Confirmation
catch address errors and scheduling conflicts before the courier knocks, in the customer's language.
02NDR Recovery
within hours of a failed attempt, call with the specific failure reason in context and the right remedy ready.
03COD-to-Prepaid Conversion
where the customer is reachable and willing, convert on the call rather than risking RTO.
04 · Why it works
Why It Works for D2C and AgroTech Specifically
Rural and tier-2/3 phone reachability is not the same problem as urban outreach. Connection windows are narrow and language-specific, and the customer is not going to navigate an English IVR to reschedule a delivery. The compounding effect of context across attempts is critical: NDR cohorts are harder leads by definition, so the only way to keep their connection rate in the same band as first-attempt outreach is for the agent to know the prior reason before the call connects. The result is a post-purchase operation where second and third touches no longer collapse, and a stubborn category of revenue loss becomes a recoverable line.
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Every conversation should start from what you already know.
Tell us which systems hold your customer's history and which workflows keep starting from zero. We will show you where the context layer slots in, without replacing what already works.