GriefOps

Inspiration When a customer faces a bad service experience—whether it is a failed payment, an unresolved support loop, or a low CSAT score—the support agent typically closes the ticket and moves on. The underlying damage remains invisible, no one coordinates a recovery, and the business doesn't realize the customer is at risk until they complain again or leave silently.

The signals of churn are almost always present, but they are scattered across tools: CSAT scores sit in Freshdesk, payment errors live in Dodo Payments, and customer value resides in Freshsales. We built GriefOps to break down these enterprise data silos, moving customer service from reactive ticket-closing to autonomous, real-time churn recovery.

What it does GriefOps is a multi-agent system built on Freshworks that detects at-risk customers the moment bad patterns emerge and autonomously orchestrates their recovery before they churn.

Four specialized agents execute in sequence:

Trigger Agent: Continuously watches Freshdesk ticket streams and Dodo Payments events in real time. It automatically flags accounts when friction patterns appear—such as 3+ contacts within 48 hours, unresolved payment failures, or a CSAT rating under 3. 

Diagnosis Agent: Pulls full context across ticket history, payment logs, and CRM contract value to classify the root cause and assess customer value. 

Recovery Orchestrator: Triggers tailored, value-based recovery actions:


Low-Value Accounts: Receives an automated, personalized apology email paired with account credits. 

Mid-Value Accounts: Receives an immediate, natural AI voice call powered by ElevenLabs that calls the customer by name, acknowledges the issue, and provides resolution. 

High-Value Accounts: Gets escalated directly to a human CSM with an auto-generated briefing and pre-approved offer attached, requiring zero manual investigation. 

Outcome Tracker: Monitors whether the customer responds or returns within a 7-day window and feeds that outcome back into the system to dynamically tune trigger sensitivity. 

How we built it Freshworks Platform: Serves as the primary enterprise telemetry layer and trigger engine, pulling real-time ticket statuses and CRM contract values.

ElevenLabs: Powers high-empathy, interactive voice interactions for real-time customer retention calls. 

Dodo Payments: Integrates payment failure detection and automates instant recovery credit issuance. 

Agentic Orchestration Framework: Orchestrates structured JSON context transfers between the Trigger, Diagnosis, Orchestrator, and Tracker agents.

Mission Control Dashboard: A dark-mode web application featuring a live "Relationship Health Index," real-time event logs, and an interactive trace of each customer's recovery journey.

Challenges we ran into Unifying Scattered Telemetry: Normalizing real-time streams from ticketing systems, payment gateways, and CRM databases into a unified "Relationship Health Index" without introducing processing latency.

Contextual Agent Handoffs: Ensuring that high-value escalations generated actionable briefings for human CSMs while keeping automated ElevenLabs voice interactions natural, respectful, and focused on resolution. 

Closed-Loop Threshold Tuning: Designing a feedback mechanism so the Outcome Tracker could safely tighten or relax trigger sensitivities based on real 7-day recovery results. 

Accomplishments that we're proud of Built a complete end-to-end 4-agent recovery pipeline that converts silent customer frustration into immediate recovery actions.

Integrated ElevenLabs to make AI voice calls feel conversational, empathetic, and context-aware. 

Streamlined human CSM escalations by eliminating preliminary manual investigation entirely. 

What we learned Churn rarely happens due to a single major outage; it is usually the result of multiple uncoordinated micro-failures (e.g., a payment error followed by a delayed ticket response).

Modular agent design—where each agent has a strictly defined domain (Trigger → Diagnose → Orchestrate → Learn)—yields significantly higher reliability than monolithic LLM prompts. 

What's next for GriefOps MCP (Model Context Protocol) Integration: Expanding agent skills across Slack, Jira, and internal wikis to resolve underlying technical issues automatically.

Predictive Churn Prevention: Moving from micro-failure triggers to predictive sentiment analysis before a ticket or payment failure even occurs.

Expanded Voice Workflows: Adding multi-lingual voice outreach support powered by Sarvam for broader regional localization.

Built With

• crm
• customer-experience
• fintech
• flask
• freshworks
• javascript
• multi-agent-system
• python