Enterprise AI
Software Solutions

Challenge

The client's support team handled 2,000+ daily inquiries across policy, account, and refund categories. Average response time exceeded 8 minutes, with inconsistent answers across agents during peak periods.

Approach

We deployed Pulsar GPT Assistant as a customer-facing copilot, connecting historical ticket data and product documentation into a retrieval pipeline with intent classification, context summarization, and sensitive content filtering.

Outcome

Achieved 40%+ ticket auto-resolution rate with sub-2-second response times. Human agents now focus on complex escalations while the copilot handles routine inquiries with full audit trails.

Challenge

Monthly finance reconciliation involved manual review of 5,000+ vendor invoices. The team regularly missed irregular entries during month-end settlements, leading to overpayments and delayed reimbursements.

Approach

Trained anomaly detection models using Pulsar AI Analytics on 18 months of historical invoice data, with explainable risk flags categorized by rule type (amount deviation, vendor pattern, timing anomaly).

Outcome

Automated risk scoring reduced manual review workload by 60%. Anomaly detection rate improved by 30%+ with false positive rate controlled below 5%.

Challenge

Operations planning relied on static spreadsheets and missed seasonal demand shifts across 3 regional warehouses and 200+ SKUs, resulting in frequent stockouts and excess inventory.

Approach

Built time-series forecasting models combining sales history, seasonality patterns, and campaign data with confidence intervals and drift monitoring dashboards using Pulsar AI Analytics.

Outcome

Reduced demand prediction error by over 20%, shortened forecast cycles from weekly to daily, and improved inventory turnover efficiency across all regional warehouses.