440K+Patients Served / Year (BBH)
Healthcare & Hospitals
Augment, Don't Replace.
Read the case studyCase study · Banking, Financial Services & Insurance
Voice AI for BFSI: Memory Across Every Interaction.
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.
5Use Cases Per Engagement
Stateless Automation Hits a Wall.
01 · The problem
Stateless Automation at Bank Scale
Voice automation in BFSI today handles most easy traffic: balance checks, fraud confirmations, single-turn queries. It is overwhelmingly stateless. The mortgage book, the ecosystem breadth across auto, home, and lifestyle products, and the high monthly volume of fraud-related inbound calls all demand voice agents that remember who they are talking to, why they called last time, and what was said. Without that memory, every call starts from zero, every retarget feels like a cold call, and every detractor's complaint reaches the complaints desk after the service-recovery window has closed.
02 · The context layer
Context Layer Beneath Existing Agents
Most large banks and insurers already have a substantial in-house team building voice and text bots powered by domestic models. The right move is rarely to replace that investment. Alchemyst provides the context layer that makes existing voice agents remember. The domestic model is the brain, the bank's telephony is the pipe, and Alchemyst's Context Platform is the memory layer that carries intent, history, and business data across every interaction. The pilot entry point is the Context Platform API, which slots underneath existing agents, with full Kathan Voice OS deployment as the expansion play once the context layer proves its value.
- Persistent context retrieval that survives across calls, channels, and weeks of customer history.
- CRM-bidirectional sync and AI-to-human escalation with full context handoff.
- Voice-based NPS at scale with structured qualitative capture, ingested as signal for trend analysis.
- Multilingual support including Russian, Arabic, English, Hindi, and 12+ regional languages.
03 · Where it fits
Where It Fits Across the Bank
Five workflows repeatedly come up when scoping BFSI voice deployments. Each one currently runs as either a stateless bot or a human-only motion, and each one gets meaningfully better when the agent carries memory.
01Loan Collections & Follow-Up
context-aware retargeting that remembers prior payment promises and objections, lifting retarget connection rates and improving promise-to-pay continuity vs. stateless dialing.
02Mortgage Lifecycle Engagement
staged context per lifecycle moment (application, active, renewal) so document chase, payment reminders, and refinancing each draw on the right history.
03Customer Satisfaction & NPS
voice NPS that captures qualitative sentiment at multiples of email response rates, ingested as structured signal for trend analysis.
04Ecosystem Cross-Sell
scoped, per-product context so the agent pitches what the customer actually uses rather than a generic upsell.
05Proactive Fraud Communication
inverting the reactive flow by reaching customers with full transaction context within minutes of detection, deflecting a meaningful share of inbound fraud volume.
04 · Why it works
Why It Works for BFSI Specifically
Banks live and die on continuity. A collections call that does not remember last week's promise-to-pay, a mortgage renewal that does not remember the document already submitted, a fraud confirmation that does not remember the transaction the customer disputed yesterday, all of these erode trust faster than anything else. The context layer sits below the voice and text channels and remembers all of it. Domestic models stay in place, telephony stays in place, regulators stay satisfied, and the customer hears a bank that finally remembers them.
More case studies
Same context layer, a different industry
Each of these connects different systems and runs different workflows. The memory underneath works the same way. All case studies.
38.7%Aggregate Connection Rate
Education Technology
Context, Not Cold Scripts.
Read the case study41.7%Aggregate Connection Rate
AgroTech & D2C Logistics
Recover Revenue Before It Becomes RTO.
Read the case study7Products Unified
Real Estate Services
Unify the Stack. Enrich the Agents Already in Place.
Read the case study6Workflows Per Dealership
Automotive Retail
Every Reminder, Every Renewal, Every Adviser Smarter Than the Last.
Read the case study6Workflows Across the Talent Lifecycle
HR Services & Staffing
From First Inbound Call to 90-Day Onboarding, Without Losing Context.
Read the case studyTalk to us
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.