Complaint Management Data Product
Transforming a manual, reactive complaint process into a data-driven intelligence product with AI.
The Business Challenge
The bank's legacy complaint process was manual, fragmented, and reactive. This led to operational bottlenecks, inconsistent data, and potential compliance risks being discovered weeks too late.
- Operational Inefficiency: Agents spent valuable minutes manually reviewing and tagging every interaction.
- Inconsistent Classification: Complaint categories varied widely across different teams and regions.
- Limited Regulatory Visibility: Potential Fair Lending or UDAAP risks were identified far too slowly.
The DataKnobs Solution
DataKnobs built a modular, API-first Complaint Management Data Product. This system transforms complaint management from a simple workflow into a data intelligence product.
Powered by a layered AI architecture, it continuously ingests, processes, and enriches complaint data for analytics, operations, and compliance teams.
Interactive AI Architecture
Click each layer to see its role in the data product.
Data Layer
Core Function: Ingests multi-channel interactions, transcripts, CRM notes.
Role in Data Product: Provides unified, governed data ingestion for all downstream processes.
LLM Layer (Prompt)
Core Function: Classifies, summarizes, and identifies risk from unstructured text.
Role in Data Product: Converts raw text into structured complaint JSON, enabling automated detection.
RAG Layer
Core Function: Retrieves internal policies, procedures, and SOPs for grounding.
Role in Data Product: Ensures all AI-generated outputs are explainable, compliant, and cited.
AI Agent Layer
Core Function: Provides a conversational interface for human users to query the data.
Role in Data Product: Delivers contextual insights and recommendations to operations and compliance teams.
Analytics Layer
Core Function: Performs clustering, trend detection, and dashboarding.
Role in Data Product: Feeds continuous insights and emerging themes back into business systems.
Key Capabilities
The data product delivers intelligence across three core areas.
1. Complaint Intelligence
Automatically detects, classifies, and summarizes complaints from raw text, identifying potential regulatory exposure.
{
"is_complaint": true,
"product": "Mortgage",
"issue_type": "Payment Posting Delay",
"severity": "Medium",
"summary": "Customer reports delayed..."
}Impact: 80% automation in detection, 92% accuracy in classification.
2. Policy-Grounded Reasoning
Uses RAG to provide explainable recommendations, complete with citations from internal policies and procedures.
{
"recommended_action": "Reverse late fee...",
"policy_citations": [
"Mortgage Servicing Policy - Sec 3.2",
"CFPB Regulation X - Error Resolution"
]
}Impact: 100% of AI recommendations backed by traceable citations.
3. Interactive AI Agent
A conversational interface for operations, compliance, and CX teams to investigate complaints and query policies in natural language.
Impact: 70% reduction in average investigation time.
See it in action ↓Explore the AI Agent Layer
This is a mock-up based on the case study. Click a suggestion to see a potential response from the AI Agent.
Compliance AI Agent
Try these examples:
Measurable Business Impact
The data product delivered significant, measurable improvements across the board.
| Metric | Before | After | Improvement |
|---|---|---|---|
| Complaint Classification Time | 3–5 min | <1 min | 80% faster |
| Regulatory Review Cycle | 10 days | 5 days | 50% faster |
| Accuracy (Complaint Detection) | ~70% | 92% | +22 pts |
| Complaint Volume Coverage | 25% | 100% | 4x increase |
| Analyst Productivity | 20 cases/day | 35+ cases/day | +75% |
“What DataKnobs built isn’t just AI — it’s a data product that gives our teams real-time visibility into customer pain points and compliance risks. It became a core data asset in our CX and risk ecosystem.”