Universal IVR Agent
Overview
Our client who is a leading provider in the healthcare revenue cycle management (RCM) space, sought to automate navigations and optimise payer IVR interactions to streamline denial management workflows. The challenge was to develop Conversational AI equipped with handling real-time IVR conversations, dynamically navigating complex payer IVR menus, and reducing human intervention.
In Phase 1, we built a scalable AI-powered system that integrates seamlessly with the RCM platform, processes live IVR transcriptions and provides context-aware, low-latency responses. The system successfully onboarded 5+ complex IVRs, demonstrating high accuracy and efficiency.
Features:
Key features and capabilities
Agentic Mesh Architecture – A network of sub-agents handling IVR traversal, reasoning, and response generation.
Real-Time IVR Interaction – Processes live transcription and dynamically determines actions (DTMF input or TTS responses).
Adaptive Intent Recognition – Uses Llama 3.5 (via Groq) to classify IVR utterances with 90%+ accuracy.
Automated Onboarding – Onboards new IVRs without manual training by leveraging success path learning.
Direct Audio Processing – Integrates Deepgram for high-fidelity speech-to-text conversion, bypassing Bandwidth.com’s limitations.
Scalability & Load Balancing – Built on GCP with an orchestration layer that dynamically distributes LLM workload for optimal performance.
Human-in-the-Loop Mechanism – Engages human reviewers for learning-based corrections when encountering unknown IVR structures.
Applications:
List Real-world examples
Healthcare & Insurance
Automating denial management, eligibility verification, and claims processing.
Financial Services
Handling customer authentication, transaction verification, and support escalations.
Telecommunications
Enables CFOs and financial planners to extract key insights quickly.
Government & Utilities
Automating inquiry responses, document verification, and payment processing.
Problem Statement
Our client needed a future-proof, AI-driven IVR automation system capable of scaling to handle hundreds of payor IVRs. The IVR navigation Intelligence system was developed with:
- Intent Recognition & Contextual Decision-Making – Ensuring seamless IVR traversal across different payors.
- Automated & Manual Oversight – Balancing AI-driven automation with human validation for critical cases.
- Seamless API-Based Integration – Directly communicating with RCM platform’s orchestration layer for executing IVR interactions.
Managing interactions with hundreds of insurance payer IVRs was a highly manual, time-consuming, and error-prone process for the client. Their existing approach:
- Required manual training for each new IVR, leading to high operational overhead.
- Lacked real-time adaptability, making it difficult to handle dynamic payor IVR structures.
- Faced inconsistent transcription accuracy, causing misinterpretations and delays in IVR interactions.
Needed scalability to handle high call volumes without increasing human workload.
Our Approach
To address these challenges, we developed a scalable, intelligent IVR automation framework with:
1. Agentic Mesh Architecture
- Built lower-level sub-agents for path decision-making and IVR traversal.
- Designed a reasoning agent to handle new payor IVRs, ensuring minimal retraining.
- Integrated fallback human validation agents to handle unknown scenarios and improve learning.
2. AI-Powered Intent Recognition & Response Generation
- Used Llama 3.5 (70B parameters, via Groq) to process IVR transcriptions and generate accurate, real-time responses.
- Implemented a multi-shot reasoning agent that learns from prior IVR interactions to improve automation accuracy.
3. Direct Audio Processing for High-Quality Transcriptions
- Integrated Deepgram for speech-to-text conversion, eliminating inaccuracies from Bandwidth.com transcriptions.
- Enabled real-time transcription and response processing for seamless IVR interactions.
4. Scalable Cloud Infrastructure with Load Balancing
- Deployed the system on GCP (Google Cloud Platform) for high availability and low-latency processing.
Implemented an orchestration engine to distribute LLM workload across multiple instances, optimizing API request rates.
Tech Stack Used
Our tool provides automated and manual redaction capabilities to ensure compliance with privacy laws. It also allows users to highlight important sections, improving document review efficiency.
Front End
Rest API
Back End
Llama 3.5
FastAPI
Redis
Database
Google Cloud Platform (GCP)
Cloud Pub/Sub & Logging Services
The tools used
- Deepgram – High-accuracy speech-to-text for real-time IVR transcription.
- Bandwidth.com – Voice call handling and raw audio ingestion.