"Press 1 for Hindi. Press 2 for English. Press 3 for account balance. Press 4 for EMI status. Press 5 to speak to an agent. Press 6 to repeat these options."
If you've ever abandoned a call at the third nested menu level, you understand why traditional IVR is one of the most universally despised technologies in customer service. It was designed around the limitations of 1990s telephone systemsand most IVR systems haven't changed since.
Agni's IVR is designed for how customers actually communicate: in natural language, in their language, without memorizing menu trees.
How Natural Language IVR Works
Instead of presenting a numbered menu and waiting for a digit, Agni opens with a simple, open-ended prompt: "Hello! Aaj main aapki kaise madad kar sakta hoon?" The customer responds naturally"Mujhe apna EMI check karna hai" or "I want to update my address" or "Complaint dena hai"and Agni routes the call accordingly.
The routing decision is based on intent classification, not keyword matching. This means:
- "Mera paisa kata par confirm nahi mila" → classified as Payment Confirmation intent → routes to payment team
- "Account band karna chahta hoon" → classified as Churn Risk intent → routes to retention team
- "Baat karni hai kisi se" → classified as Human Agent Request → triggers live transfer
The intent classification runs in parallel with STTthe routing decision is made within 400ms of the customer finishing their sentence.
Language detection included: If the customer responds in Tamil when the opening was in Hindi, Agni detects the language switch and routes to a Tamil-language agent or flowwithout the customer having to navigate a separate language selection step.
DTMF Fallback: Critical for India
Natural language IVR has a real-world limitation: high-noise environments. A customer calling from a construction site, a busy market, or a moving vehicle may not be able to speak clearly enough for accurate STT. Forcing them into natural language when their environment doesn't support it creates exactly the friction you were trying to eliminate.
Agni handles this with intelligent DTMF fallback:
- Natural language is the defaultalways tried first
- If STT confidence falls below threshold (background noise too high), the system automatically switches to DTMF mode: "I'm having trouble hearing you clearlypress 1 for EMI, press 2 for payment, press 3 for an agent"
- The DTMF menu is contextually shortenedit only shows options relevant to the call type (inbound vs outbound, campaign type), not a generic 9-option tree
This fallback is seamless from the customer's perspective. There's no error message, no "sorry I didn't understand that" loopjust a smooth transition to the mode that works for their environment.
Multi-Level IVR Flows Without Code
Complex routing scenariosescalation paths, multilevel intent trees, time-based routingare configured in the Agni dashboard using a visual flow builder. No IVR scripting language. No developer required. You can build flows like:
- Inbound call → Intent detection → if Payment query → check if payment > ₹10,000 → if yes, route to senior agent; if no, handle via AI
- Inbound call → Time check → if after 7 PM → offer callback scheduling → else → live routing
- Inbound call → CRM lookup → if VIP account → skip IVR, direct connect to dedicated agent
Inbound vs Outbound IVR
Agni's IVR handles both inbound (customers calling you) and outbound (your AI calling customers) scenarios. For outbound campaigns, the "IVR" is actually the AI agent's conversational flowthe same intent detection and routing logic applies, but the AI leads the conversation rather than waiting for customer initiation.
For inbound customer service lines, the natural language IVR replaces your traditional IVR system entirelycustomers call the same number, but instead of a menu, they get a conversation.
"We had 28 different IVR menu options across 4 levels. After switching to Agni's natural language IVR, we collapsed it to a single open question. Call abandonment in the IVR dropped from 34% to 6%."CX Head, Telecom Distributor (Chennai)
Analytics on Every IVR Interaction
Every IVR interactionlanguage detected, intent classified, route taken, DTMF fallback triggered (y/n)is logged and available in the Agni analytics dashboard. This gives you real-time visibility into what your customers are calling about, at what volume, in what languages. Most businesses discover insights in the first week that were invisible in their traditional IVR data.
Ready to get started?
Build your first natural language IVR flow in the Agni dashboard at app.ravan.ai. Full IVR documentation available at docs.ravan.ai.
