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Malted AI

Pulse is Malted AI's real-time, cloud-native AI for financial services, turning every customer interaction into workable
8.2
Rating
Custom
Price
8
Key Features

Overview

Malted AI delivers purpose-built, security-first artificial intelligence for financial services. Its flagship product, Pulse, analyzes 100% of customer interactions (voice, chat, email, CRM, case records) in real time and runs entirely inside the customer’s cloud, VPC, or on-premises environment so data never leaves the customer estate. Malted uses proprietary model distillation to produce highly efficient, auditable, and regulatory-aligned models tuned for banking and regulated markets. Core capabilities include complaint and harm detection, complaint prediction, journey-friction analysis, churn prediction, regulatory reporting, and a unified, searchable customer timeline. The platform emphasizes data sovereignty, auditability, and alignment with sector regulations (e.g., GDPR, Consumer Duty).

Details

Developer
malted.ai
Launch Year
2023
Free Trial
No
Updated
2025-12-07

Features

Real-time 100% interaction analysis

Converts conversations into structured signals and alerts in real time to enable proactive intervention across voice, chat, email, messaging, CRM and case records.

On-premise / in-cloud deployment & data sovereignty

Runs inside customer VPC/private cloud or on-premises so data never leaves the customer environment, ensuring data residency and no external egress.

Model distillation & efficiency

Uses proprietary distillation to produce smaller, faster models claimed to be up to 100x more efficient than large general models, enabling lower cost and faster inference.

Complaint detection and prediction

Detects current complaints and predicts emerging complaints to reduce escalations and regulator exposure.

Regulatory reporting & auditability

Produces evidence-backed Consumer Duty and regulatory reports with version-controlled models for audits and regulatory alignment.

Journey friction & churn analysis

Surfaces journey bottlenecks and early churn signals to improve customer experience and reduce attrition.

Screenshots

Malted AI Screenshot
Malted AI Screenshot

Pros & Cons

Pros

  • Strong security and data residency: processes data entirely inside customer infrastructure (no external egress).
  • Purpose-built for financial services: models trained/tuned on FS data and regulatory contexts.
  • Real-time, enterprise-scale analysis: claims to analyze 100% of interactions and surface actionable insights.
  • Efficient models: proprietary distillation claims (smaller models, lower cost, faster inference).
  • Auditability & regulatory alignment: version-controlled models and evidence-backed reporting.

Cons

  • No public pricing or self-serve plans (enterprise sales / demo-only funnel).
  • Targeted specifically at financial services — not a general-purpose, broad-market product.
  • Limited public documentation/resources visible on site (e.g., no public API docs or developer portal found).

Compare with Alternatives

FeatureMalted AIPeppercorn (PeppercornAI)PolyAI
PricingN/AN/AN/A
Rating8.2/108.2/108.5/10
Real-time CoverageYesPartialYes
Deployment SovereigntyYesPartialPartial
Regulatory AuditabilityYesYesYes
Model EfficiencyYesPartialPartial
Voice & TelephonyYesPartialYes
Fraud/Complaint DetectionYesYesPartial
Human-in-the-loopYesPartialPartial
Integration DepthBroad telephony CRM and case system integrationsIntegration overlay with implementation supportStrong voice and omnichannel contact-center integrations

Audience

BanksMonitor customer interactions to detect complaints, risk and regulatory issues in real time.
Wealth managersCapture and audit client communications to protect customers and meet compliance needs.
FintechsGain real-time operational insights and reduce friction across customer journeys while keeping data private.
Compliance & Risk teamsAutomate complaint detection, regulatory reporting, and build auditable evidence for oversight.
Customer Experience teamsIdentify journey friction and drive proactive interventions to improve outcomes and retention.

Tags

financial services AIsmall language modelsdata privacycloud-nativeregulatory alignmentcustomer analyticscomplaint detectionmodel distillation