Adastra AI Agent Catalog

AI agent reviews delinquent accounts, ranks by likelihood to pay, and drafts personalized email/SMS outreach. Suggests payment plans and responds to common inquiries.
Impact: cost reduction, higher recovery rates.

AI agent screens new customer applications, flags unusual patterns, and compiles research from public databases. Creates summary reports with risk recommendations for analyst review.
Impact: faster case processing, fewer manual reviews.

AI agent monitors client portfolios daily, identifies drift from target allocation, continuously digesting news and data to detect risk signals.
Impact: time savings, consistent portfolio maintenance, early risk detection.

AI agent analyzes transaction patterns to identify cross-sell opportunities. Sends personalized product recommendations and schedules follow-up reminders for relationship managers.
Impact: increase in qualified leads, better timing.

AI agent monitors transactions for suspicious patterns, gathers transaction history, and creates initial case files with supporting evidence for human investigators to review.
Impact: faster case preparation, better documentation.

AI agent tracks inventory levels against usage patterns and supplier lead times. Generates purchase recommendations and flags critical shortages for procurement team approval.
Impact: inventory reduction, fewer stockouts.

AI agent that is connected to optimization engine to analyze orders and resource availability to suggest optimal production sequences. Provides daily schedule recommendations considering changeover times and labor shifts.
Impact: throughput improvement, cost saving.

AI agent reviews inspection images and sensor readings to flag potential defects. Creates detailed reports highlighting patterns and suggests root cause investigations.
Impact: defect detection accuracy, faster issue identification.

AI agent analyzes sales history and market data to generate demand predictions. Provides weekly forecasts with confidence intervals for production planning teams.
Impact: inventory optimization, better production planning.

AI agent proactively analyzes equipment sensor data to identify maintenance priorities and generates optimized crew schedules. Recommends timing based on production windows and parts availability.
Impact: better resource utilization, fewer emergency repairs.

Builds and optimizes personalized campaigns on the fly based on the inputs from demographic, behavioral data and ML model predictions.
Impact: higher campaign ROI, granular campaign enablement.

Resolves routine inquiries across chat, email, and voice. Provides seamless handover to operators and maintains context across interactions.
Impact: faster response, automated queries, 24/7 availability.

Continuously monitors competitor pricing, demand signals, and inventory levels. Automatically adjusts prices within defined guardrails while preserving brand positioning.
Impact: higher gross margin, reduced markdown waste.

Proactively analyzes and connects to optimization engines to orchestrate scheduling and restocking priorities. Analyzes real-time sales velocity, traffic forecasts to send scheduling and restocking suggestions.
Impact: streamlined scheduling, higher shelf availability.

Monitors demand and automatically and generates purchase orders for approval.
Impact: fewer stockouts,  faster replenishment.

Aggregates emissions data and compares it against regulatory limits and available offsets, flagging potential compliance gaps and generating quarterly reports.
Impact: reduced manual spreadsheet work, streamlined reporting.

Monitors grid conditions and sends automated curtailment requests to enrolled customers based on contracts and incentives. Streamlines coordination that would otherwise require manual calls and emails during demand spikes.
Impact: faster response, automated communication generation.

Tracks fuel market prices and contract terms across suppliers, alerting procurement teams to favorable buying opportunities while checking against emissions budgets and delivery schedules.
Impact: reduced volatility exposure through proactive analysis.

Provides assistance during analysis of forecasted data and current grid conditions to recommend adjustments, helping operators balance the supply and demand more efficiently.
Impact: less reliance on manual calculations, faster response to demand variations.

Automates appointment booking across doctors, rooms, and equipment. Sends reminders, fills cancellations, and reduces back-and-forth.
Impact: higher clinic utilization, faster access for patients.

Searches EMR databases using predefined eligibility criteria to identify potential trial candidates and generates lists for clinical staff to review and contact.
Impact: reduces manual chart review time, easier coordination of outreach.

Reviews incoming claims against payer coverage policies and flags common errors (missing codes, eligibility mismatches) before submission, routing complex cases to billing specialists.
Impact: reduced claims rejections, faster resubmission process.

Analyzes patient volume patterns and staff availability to suggest shift schedules that balance workload with preferences, flagging understaffed periods for manager review.
Impact: streamlined scheduling, fewer staffing gaps.

Provides automated responses to common pre- and post-visit questions (preparation instructions, medication reminders) and uses decision trees to route low-acuity symptoms to self-care guidance or appropriate care settings.
Impact: automated routine inquiries, reduced call volume, higher patient engagement.