support-analytics

AI-powered solution to predict SLA breaches, prioritize support tickets, and enhance JIRA workflow efficiency using machine learning.

Automatically converts freeform customer support notes into operational KPIs by extracting issue signals, scoring customer pain, identifying escalation risk, and enabling prioritization.

🤖 Streamline JIRA ticket management with AI-driven SLA predictions for improved automation and efficiency in customer support.

Business-focused analysis of 9,715 support tickets to identify recurring demand, seasonal peaks, and operational priorities.

Apple Support automation scraper

Forecasted support incident volumes using Holt-Winters and SARIMA models to identify seasonality, optimize resource planning, and improve operational readiness.

Interactive Power BI dashboard analyzing analyst performance, SLA compliance, ticket resolution efficiency, and workload distribution to support data-driven operational decisions.

AI call QA agent for transcript scoring, policy detection, escalation misses, and coaching notes.

Support intelligence layer: classify messages, measure SLA, score refund risk and turn support symptoms into root-cause actions.

Prototype: Raw Slack support threads → structured support intelligence outputs (issue type, summaries, response/resolution metrics)

Support conversations → one coachable behavior per team member. Requires a repeated issue across enough reviewed conversations before it will say anything.

AI support-ops copilot: NL queries over tickets via guarded text-to-SQL, anomaly radar, 12-page Streamlit cockpit, FastAPI REST, DuckDB warehouse, Groq/Ollama LLM — zero cost, one command.

Support conversations → structured review records where every quote stays traceable to its source message.

📊 Forecast daily support incident volumes to enhance resource planning using advanced time series analysis and reliable forecasting models.