Case studies

Seven industries. One context layer underneath.

Hospitals, EdTech, D2C logistics, banking, real estate, dealerships and staffing. The voice, the channel and the workflow change from one to the next. What makes each of them work is the same: every conversation carries the customer's history, and every answer is written back for the next one.

01 · Healthcare

440K+Patients Served / Year (BBH)

Healthcare & Hospitals

Augment, Don't Replace.

Hospitals run on high-volume, repeatable patient communication: confirming appointments, checking on discharged patients, fielding 'are my results ready?' calls, supporting palliative caregivers, chasing chronic disease adherence, following up on community screening referrals, and collecting feedback. Each task competes for limited clinical and administrative bandwidth, and each one only works if the call knows who the patient is. The Alchemyst context layer gives every call the patient's live record and history. Kathan Voice OS, running on top of it, absorbs the repeatable load, escalates anything clinically meaningful to the right human in real time, and runs natively in Indian languages including Tamil, Kannada, Telugu, and Hindi.

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02 · EdTech

38.7%Aggregate Connection Rate

Education Technology

Context, Not Cold Scripts.

EdTech outbound calling in India hits a wall that generic dialers cannot solve. Students and parents expect conversations in their own language, and a single script does not work across career guidance, parent-teacher follow-up, enrollment, feedback, and re-engagement. Kathan Voice OS handles all of these natively across 12+ Indian languages on the Alchemyst context layer, which keeps a persistent memory of every lead. Retargeted cohorts consistently outperform cold outreach because context accumulates across attempts.

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03 · AgroTech

41.7%Aggregate Connection Rate

AgroTech & D2C Logistics

Recover Revenue Before It Becomes RTO.

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.

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04 · BFSI

5Use Cases Per Engagement

Banking, Financial Services & Insurance

Stateless Automation Hits a Wall.

Banks and insurers field tens of millions of voice and text requests every month. Existing automation handles most simple, single-turn queries. Anything that requires remembering what was said last time, what was promised, or what the customer is in the middle of, still routes to a human. The Alchemyst context layer slots underneath existing voice and text agents, so collections remember prior promise-to-pay, mortgage conversations carry document history, and fraud calls reach the customer with full transaction context within minutes of detection.

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05 · Real Estate

7Products Unified

Real Estate Services

Unify the Stack. Enrich the Agents Already in Place.

Full-stack real estate consultancies operate across home search portals, sales CRM, channel partner portals, community management, and flex workspace. Each platform holds a slice of the buyer journey, and none speak to the others. A buyer discovered on the search portal is re-onboarded in CRM, loses context at community handover, and is invisible to the workspace product. Lead-scoring agents see only the slice of data inside the product where they run. The context layer resolves identities across every product, enriches existing AI agents with cross-product signals at inference time, and unlocks workflows that are structurally impossible without a unified data fabric.

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06 · Auto Retail

6Workflows Per Dealership

Automotive Retail

Every Reminder, Every Renewal, Every Adviser Smarter Than the Last.

Dealerships operate across four distinct data domains that rarely speak to one another: the DMS, the CRM, service management, and the insurance and warranty stack. Free service reminders go out as generic SMS blasts. Insurance renewals are lost to aggregators because nobody calls before expiry. Test drive walk-ins disappear into CRM notes that never get acted on. Loyal customers crossing the upgrade threshold are invisible. The context layer maintains a persistent profile per vehicle and owner across every system, and the voice agent makes every inbound and outbound call context-aware from the first second.

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07 · HR Services

6Workflows Across the Talent Lifecycle

HR Services & Staffing

From First Inbound Call to 90-Day Onboarding, Without Losing Context.

HR services companies running permanent recruitment, flexi and contract staffing, RPO, and HR outsourcing process tens of thousands of candidate interactions each month across desk-based and blue-collar roles. The data is plentiful; the problem is fragmentation. ATS, HRMS, LMS, payroll, and shift scheduling each hold an isolated slice of the candidate or worker record, with no persistent memory stitching them together across touchpoints. The context layer provides that memory, and voice extends it to the mobile-first, form-averse blue-collar segment where most volume actually lives.

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In every case study

What the context layer carries into every conversation

Seven industries and 39 workflows between them. Underneath all of them, the same four things are doing the work.

Context before the first word

The customer's record, language and history are retrieved and filtered before the call connects, so the agent opens with specifics instead of a script.

Memory across attempts

Every retry starts from what the last one learned: the objection, the failure reason, the promise made. Second and third touches stop collapsing.

Written back to the system of record

Outcomes flow back to the HIMS, CRM, ATS or DMS without a human touching them, so the next agent and the next person both see the latest truth.

A sidecar, not a replacement

Existing databases, models, telephony and agents stay in place. The context layer sits underneath them and changes what they can see.

The same API runs well beyond these seven. See what teams build on it by use case, or how it handles voice agents specifically.

Your industry next

Your agents already have the channel. Give them the memory.

Tell us which systems hold your customer's history and which conversations keep starting from zero. We will show you where the context layer slots in.

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