Vartalabh

Vartalabh: Observability for AI Conversations

Vartalabh measures how real users experience conversations with AI agents in production. Most tools watch the AI - its traces, prompts, and tool calls. Vartalabh watches the conversation: it surfaces silent drop-offs, the moments users disengage without complaint, and the specific turn where trust broke. For every flagged conversation, it attributes the cause across three actors - the AI, the product, and the user and routes each finding to the team that should act on it: engineering, product, or CX.

Key Features

• Message-level analysis - Pinpoint the exact turn where engagement broke, not just a conversation-level score.

• Silent drop-off detection - Surface high-intent users who leave without complaint.

• Pattern engine - Surface recurring failure patterns across thousands of conversations that no single transcript reveals: the price objection that keeps landing the same way, the intent that consistently stalls at the same turn, the policy wall that quietly costs you conversions week after week.

• Recommended actions, routed by owner - Every finding ships with a specific next step tagged to the team that owns it:

  • Tech - the exact failure mode to fix, distinguishing retrieval gaps, grounding failures, hallucination, tool errors, and prompt drift

  • Product - the policy gaps and missing capabilities callers keep asking for, ranked by what each one costs you.

  • Positioning - where the AI is selling something the product doesn't offer, and where messaging or framing needs to change to match what users actually came for.

Use Cases

• Diagnosing why conversion or retention isn't where it should be, when the AI looks like it's working.

• Ranking product and policy gaps by their actual cost to the business.

• Giving engineering, product, and CX teams a shared, owner-tagged view of what's breaking and why.

Getting Started

https://www.vartalabh.in/

Vartalabh equips technical teams with the necessary infrastructure to inspect and understand their conversational AI data, ultimately fostering greater reliability and better user experiences in automated digital environments.