Hooly is a customer support quality and compliance platform that consolidates conversations from tickets, chats, calls, and app reviews. It analyzes customer interactions to help support and QA teams identify recurring issues, monitor compliance, and make faster, data-driven decisions through a visual dashboard. Built to address the limitations of manual QA and basic operational dashboards, Hooly provides deeper insights into customer sentiment, support quality, and recurring interaction issues.
Customer support generates far more conversation data than any quality team can read. Tickets, chats, call recordings, and public app-store reviews sit in separate systems, each holding only part of the picture, while the reviewers responsible for judging quality can sample a small fraction of it. Understanding what customers actually complain about, and whether teams followed policy, was not possible at that scale.
Support teams handled thousands of tickets, chats, calls, and public reviews, but QA reviewers could inspect only a small portion of them. Those sources also lived in separate systems, so managers had no single view of what customers were complaining about or whether the same problem surfaced across several channels.
Reviewers had to judge whether agents followed the correct process, used professional language, treated the customer respectfully, and completed every required step. Manual interpretation of those rules differed from person to person, and the inconsistency became harder to manage as the support team expanded.
The engagement was approached as a decision-support workflow rather than a single AI feature. The team started from what a support manager or QA reviewer actually needs to decide, then built the data and AI pipeline around those decisions. Analysis was split into focused checks, grounded in the company's own policies, and kept connected to the original conversation evidence.
Hooly accepted historical support data from tickets, chat tools, audio calls, and public app-store reviews. Each source format was converted into one common structure holding customer and agent messages, timestamps, channel details, and interaction identifiers, so patterns could be compared across every channel.
A Mistral AI language model interpreted multi-turn conversations through focused tasks: main complaint, secondary issues, satisfaction, resolution status, repeat contacts, and unprofessional language. Retrieval-augmented generation supplied company policies, so each judgement rested on the relevant business rule.
The platform opened review beyond a small manual sample, letting managers see complaint and quality patterns across historical data, channels, agents, teams, and time periods.
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Grouping complaints and linking repeat interactions surfaced issues that appeared again and again, showing whether the root cause sat with agent handling, company process, or the product.
Company policies were applied through one evaluation workflow while evidence stayed available for human review, improving consistency without hiding the reasoning behind an AI result.
Eternalight built Hooly on React and Python, with Apache Kafka handling asynchronous ingestion of tickets, chats, calls, and app-store reviews. A Mistral AI model and retrieval-augmented generation ground every quality judgement in company policy, while human-in-the-loop review covers sensitive findings. Delivery followed a structured process from brainstorming and wireframes through development, integration, launch, and user feedback.
Brainstorming
Wireframe
Development
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Metrics such as response time and ticket status could confirm that a problem existed, but never explained why it happened. Leaders still needed to know the real complaint behind a case, whether the customer was satisfied, what the agent missed, and whether that person returned later with the identical issue.
The longer-term plan included AI-assisted and automated customer support. Before that could be considered safely, the business needed a reliable and structured understanding of its own policies, successful resolutions, escalation rules, and the kinds of responses that create legal or reputational risk.
Apache Kafka separated ingestion from long-running processing. Tickets and conversations moved through cleaning, normalization, retrieval, AI analysis, and aggregation without making the web application wait on every record, so large imports ran reliably and processing scaled independently of the dashboard.
A React dashboard presented complaint themes, satisfaction signals, protocol adherence, language risk, and agent trends through filters and charts, with drill-down to the original interaction. Compliance failures, conduct concerns, and low-confidence results stayed reviewable so a person could correct them.
User Feedback
Launch
Integration
React: A component‑based UI library for building fast, interactive web interfaces with a virtual DOM.
Powers backend logic and AI-driven processing.
Enables intelligent text analysis and generation.
Understands and processes complex customer conversations.
Extracts insights from unstructured customer data.
Structures and exchanges data between application components.
