Leveraging Multimodal LLMs for a Proactive Future
The Technical Challenge:
Our Technical Solution:
From Reactive Problem-Solving to Proactive System Intelligence.
Current systems provide retrospective quantitative metrics, but lack real-time qualitative depth.
ASR captures words, but misses paralinguistic cues (pitch, tone, prosody).
Traditional NLP struggles with sarcasm, implicit meaning, and complex emotional states from text alone.
Batch processing of feedback prevents real-time system adjustments or interventions.
Overcoming these limitations requires advanced AI capabilities beyond simple keyword spotting or sentiment lexicons.
This verbatim highlights the need for AI to infer implicit needs and personalization cues from conversation, moving beyond explicit requests.
Challenge: Automating the detection of implicit signals for "recognising customer loyalty", "proactively keeping you informed", and "staff having your interests at heart" without explicit verbal cues.
Source: CBA MFI Customer Verbatim, April 2025 (Page 4) - illustrating raw data.
This feedback points to systemic technical challenges in knowledge dissemination and real-time information access for staff.
Challenge: Ensuring staff provide accurate, real-time information by dynamically surfacing relevant data and solutions based on nuanced customer queries.
Source: CBA MFI Customer Verbatim, April 2025 (Page 13) - illustrating raw data.
Text-Only: Misses tone, emotion, unspoken intent. Provides lagging insights based on transcriptions.
After-the-Fact: Limited by questions, recall bias. No real-time alerts or spontaneous feedback.
Historical Data: Shows *what* happened (e.g., NPS decline), not *why* it's happening at the moment of interaction.
We are reacting to problems, not proactively preventing them. Our tech stack needs to enable foresight over hindsight.
Delayed insights prevent timely, automated system responses, leading to manual interventions.
Higher AHT for escalated issues due to lack of real-time AI guidance.
Lagging feedback cycles impede agile product development and rapid adaptation to user needs.
Vast amounts of unstructured data remain unanalyzed, representing untapped technical potential.
Transcribed Words
What is said
Tone, Pitch, Volume, Cadence
How it is said
Deep Contextual Understanding
True Intent & Emotion
Unlocking insights that were previously imperceptible.
Detects anger, calm, urgency, satisfaction.
Identifies frustration, confusion, delight, anxiety.
Understands unstated needs or hidden dissatisfaction.
Analyzes pauses, interruptions, speaking rate.
Understands nuanced phrases and sarcasm.
Flags high-priority customer situations.
Moving from 'what was said' to 'what was truly meant' programmatically.
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flowchart TD
A[Raw Audio Data] --> B(Multimodal LLM);
B --> C(Deep Understanding);
C --> D(Actionable Insights);
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A continuous feedback loop for intelligent decision-making, enabling automated system responses.
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flowchart LR
A[Capture Raw Data] -- Interpret --> B(Generate Insights);
B -- Apply --> C(Actionable Intervention);
C -- Measure --> D(Refine Strategy);
D -- Feed Back --> A;
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A continuous cycle where each stage amplifies the next, creating compounding technical value.
Quantify frustration, urgency, delight during interactions. *Technical impact: Identify triggers for automated system re-routing or enhanced agent support.*
Identify subtle problems customers don't explicitly articulate. *Technical impact: Fine-tune product feature roadmaps and enhance proactive digital nudges based on implicit signals.*
Detect early indicators of dissatisfaction or intent to leave. *Technical impact: Trigger automated retention workflows or prioritize agent follow-ups in CRM systems.*
Measure soft skills and actual problem resolution impact. *Technical impact: Develop adaptive training modules and refine conversational AI agent responses based on real-world interactions.*
Issue occurs → Manual logging → Batch processing → Delayed reporting → Manual solution implementation.
Real-time signal detection → Automated insight generation → Targeted system intervention → Problem mitigated / Feature enhanced.
Foresight over hindsight: engineering a new era of proactive system management.
Customer: "I've been waiting for my home loan approval for weeks! This is unacceptable, I need an answer *now*!" (Raised voice, agitated tone, rapid speech)
**Emotion Detected:** High Frustration, Urgency. **Key Topic:** Home Loan Approval. **Agent Prompt:** "Acknowledge frustration directly. Check loan status: [Loan ID XXXXX]. Suggest: 'I understand this delay is concerning. Let me pull up your application and get you an immediate update, or escalate to a specialist if needed.'"
**Technical Impact:** Enables real-time multimodal analysis to trigger dynamic content injection into agent UIs, improving resolution and reducing churn signals.
Customer: "The online account opening process seems... pretty straightforward. Just making sure I haven't missed anything about linking my existing accounts." (Slight hesitation, unsure tone on 'anything')
**LLM Detects:** Potential underlying confusion despite verbal assurance. **Action:** Triggers automated email post-call with detailed, step-by-step instructions for linking accounts and a direct link to a video tutorial, reducing potential future friction and improving 'Ease of using self-service channels'.
**Technical Impact:** Enables proactive, automated system responses based on subtle interaction cues, reducing future support load and enhancing user self-service adoption.
"Yes, just handling this payment. Things are busy – we just secured a new contract, looking to expand our operations soon." (Enthusiastic tone, slight underlying pressure)
**LLM Flags:** 'Expansion intent', 'Growth phase' keywords combined with positive sentiment. **Action:** Triggers automated notification to RM with tailored info on business expansion loans, cash flow solutions, or invites to business growth workshops, enhancing proactive client engagement capabilities.
**Technical Impact:** Develops a proactive AI trigger system to identify business growth opportunities and automate targeted RM engagement, improving data-driven outreach.
Drive Proactive System Decisions, AI-Powered Product Innovation, and Personalized Service Automation.
Elevating Technical Capabilities for Enhanced Customer Experience.
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