PromoGenie is your Retail Strategist

PromoGenie transforms retail promotion management using Domo's Agent Catalyst Platform, powered by Snowflake Cortex. Experience autonomous decision-making, real-time optimisation, and intelligent customer targeting, all delivering measurable results.​

Benefits

• Improve campaign ROI, with real-time visibility into customer behaviour and optimised pricing to maximise margins.​
• Enable marketing leaders to make confident, data-driven decisions, backed by intelligent AI agents monitoring every customer interaction.​
• Secure a lasting competitive edge with continuous opzzzztimisation, greater customer engagement, and intelligent automation driving retail excellence.

How it works

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Streamline your Content Creation

Competitive intelligence is challenging and often inconsistent in a fast-growing, competitive market. Roxie (your AI Content Strategist)  monitors both internal CRM data and external sources like competitor sites and analyst reports to deliver timely insights on where the company is winning or losing deals, why that is happening, and how to improve GTM messaging.

Benefits

• Uses purpose-built agents for deep research, strategy, SEO optimization, content authoring, tagging, and categorization
• Leverages proprietary internal data and publicly-available external data to inform potential topics.
• Leverage human-in-the-loop approvals and additional integrations to improve tone and quality
• Can automatically push finished copy, tagging, and assets to your preferred content management system (CMS)

Cart Abandonment Recovery Agent

Benefits

Cart Abandonment Recovery Agent automates abandoned cart recovery through behavioral analysis, dynamic offer generation, and multi-channel outreach to maximize conversion rates.

Problem Addressed

70%+ cart abandonment leads to major revenue loss due to generic, slow, or non-existent recovery efforts. Manual segmentation is time-consuming, and batch campaigns lack personalization, resulting in poor conversion.

What the Agent Does

Performs real-time detection of abandoned carts and uses AI to personalize offers and outreach channels. Automatically triggers hyper-targeted recovery campaigns via email or SMS, offering dynamic incentives such as discounts or free shipping.

Standout Features

• Behavior-based triggers for real-time recovery
• AI-driven personalization across offers and channels
• Learns from conversion data to optimize future campaign effectiveness

Replenishment Stock Agent

Benefits

Replenishment Stock Agent automatically identifies store stockouts and calculates optimal replenishment quantities from warehouse inventory, ensuring shelves stay stocked and sales aren't lost.

Problem Addressed

Manual stock replenishment leads to costly stockouts (50%+ shelf gaps) and inefficient warehouse allocation. These issues result in lost sales, customer dissatisfaction, and excess inventory at warehouses.

What the Agent Does

• Detects Critical Stockouts: Calculates Stock Out Percentage for each store-SKU combination in real time
• Smart Matching: Pairs store deficits with warehouse inventory availability
• Recommends Action: Determines the optimal quantity to transfer from warehouse to store

Standout Features

• Threshold-based triggers
• Warehouse-aware logic to avoid overdraw
• Replenishment dashboard with real-time stockout alerts in App Studio

Discount Suggestion Agent

Benefits

The Discount Suggestion Agent automatically identifies slow-moving products and generates targeted discount strategies for customers based on stock age and sales performance, while protecting profit margins.

Problem Addressed

This agent addresses inefficient inventory turnover and revenue loss caused by stagnant products (≥60% unsold post-deadline). It eliminates manual discount guesswork and one-size-fits-all pricing that often erodes profit margins.

What the Agent Does

The agent automatically identifies slow-moving stock and applies targeted discount logic based on customer type and product age. It ensures all suggested discounts respect pre-defined profit margin thresholds and pricing floors.

Standout Features

• Stock-age-based discount tiers
• Customer type-specific discount strategies
• Margin protection with minimum price enforcement

Personalized Product & Color Palette Recommender

Benefits

This agent delivers hyper-personalized product recommendations by analyzing customer purchase behavior, product interactions, and preference trends. It batches customers, evaluates their top categories, brands, and color palettes, and returns tailored product matches with images.

Problem Addressed

Retail brands struggle to offer timely, relevant, and visually aligned product recommendations that match both short-term behavior and long-term preferences. Manual methods can't scale or personalize deeply enough.

What the Agent Does

This AI agent performs batch-wise recommendations across customers:

• Get Dataset Row Count: Determines number of customers to process in each batch
• Batch Loading: Skips batch if no rows exist; continues if rows found
• Personalized Product Recommender:
    o Extracts 1-year customer engagement (purchases + interactions)
    o Identifies top category, brand, color palette, and style tags
    o Considers long-term preferences (older than 1 year)
    o Filters products from product_details table:
        ▪ In stock = TRUE
        ▪ Matches top color palettes, category, brand, and style tags
    o Recommends up to 3 best matches per customer
• Append to Dataset: Stores final recommendations
• Add Numbers: Aggregates totals for downstream reporting

Standout Features

• Combines short-term and long-term behavior
• Color-aware personalization logic
• Returns image links for email embedding
• Fallback formatting logic for fewer than 3 matches

Smart Rostering Agent

1. Overview

The Smart Rostering Agent is an intelligent scheduling application built using Domo AI Agent technology. It is designed to automate the creation of weekly employee rosters by analyzing various critical factors such as employee performance, holidays, and leave schedules. The application not only generates optimized rosters but also provides detailed reasoning for each employee's allocation, ensuring transparency and accountability in scheduling decisions.

The system also includes features for managing employee information, leave requests, and performance tracking, making it a comprehensive workforce management solution.

2. Problem Addressed

Manual roster planning is often time-consuming, error-prone, and inefficient, especially when dealing with multiple constraints like employee availability, performance variations, and public holidays. Traditional methods may fail to fairly distribute workloads or accommodate employee needs, leading to burnout, conflicts, and reduced productivity.

The Smart Rostering Agent addresses these challenges by:

• Automating the roster generation process
• Ensuring fair distribution of work
• Incorporating employee leave and holiday data
• Considering historical performance for better task allocation

3. What the Agent Does

• Weekly Roster Generation: Automatically generates the next week's roster with intelligent logic.
• Reason Explanation: Provides detailed reasoning for each allocation, such as:
  • "Employee A is scheduled for peak hours due to high past performance.
  • "Employee B is given fewer hours due to approved leave."
• Data Management:
  • Manage and update employee records
  • Track and approve leave applications
  • Store and use historical performance data

4. Standout Features

• AI-Powered Roster Planning: Uses Domo AI Agent to generate optimized weekly schedules.
• Integrated Leave and Holiday Handling: Automatically adjusts scheduling around approved leaves and national holidays.
• Performance-Based Scheduling: Allocates employees based on past performance metrics for optimal productivity.
• Reasoning Engine:Every allocation decision is explained for clarity and transparency.
• Roster Editing & Overrides: Allows managers to manually override or edit roster data when needed.
• Employee & Leave Management: Unified interface for managing employee profiles and leave requests.
• Performance Input Form: Capture performance data to inform future scheduling decisions.

Store-to-Store Allocation & Stock Refill

Benefits

StoreStock Optimizer AI analyzes real-time retail store data to intelligently recommend profitable stock transfers between stores for underperforming products and restocking of fast-moving items.

Problem Addressed

Inventory misalignment causes major revenue and operational inefficiencies. Stores are often overstocked with low-selling products while other locations face stockouts of fast-moving items. Manual monitoring of such cases across regions is time-consuming and error-prone, leading to lost sales, heavy markdowns, and high working capital costs.

What the Agent Does

StoreStock Optimizer AI performs two intelligent tasks:

• Store-to-Store Transfers: Identifies slow-moving stock in overstocked stores and matches them with demand from understocked stores, recommending profitable transfers (after calculating logistics costs).
• High-Mover Refill Alerts: Detects fast-moving products with low inventory and recommends replenishment directly from the central warehouse, with justification and urgency.

The agent also triggers Mail Approvals and sends Buzz Notifications to ensure rapid human validation before execution.

Standout Features

• Two-in-one agent for inter-store transfers and top-priority restocking
• Dynamic calculation of transfer profitability (after fuel/toll deduction)
• Threshold-based filtering of high- and low-performing products
• Seamless Notifications alerts and email approval flow

First Allocation Product Planner

Benefits

First Allocation AI recommends optimal storewise distribution of new retail products by analyzing historical sales of similar items and matching product attributes within a specific location. It ensures inventory alignment with real demand patterns.

Problem Addressed

Retail teams often struggle with allocating new product inventory fairly and efficiently across stores. Traditional allocation ignores store-level sales trends, leading to understocking in high-demand locations and overstocking in low performers resulting in revenue loss, markdowns, and excess inventory.

What the Agent Does

First Allocation AI uses historical data to recommend how many units of a new product should go to each store within a selected location. It filters similar historical products, computes store-level demand based on multi-attribute similarity and past sales, and produces a proportional allocation plan complete with justification and human approval trigger.

Standout Features

• Intelligent matching of new products with historical counterparts
• Weighted similarity scoring across 8+ product attributes
• Store-level demand estimation using 3-week rolling sales average
• Auto-allocation based on demand signals and price or rating based tuning
• Built-in business logic avoids over-allocation beyond past capacity
• Supports manager override via Approval Queue Trigger

Benefits

A powerful trio of AI agents that work together to analyze customer behavior, detect warehouse and demand risks, and optimize pricing and discount strategies. Together, they help marketing, operations, and pricing teams make data-driven decisions, maximize conversions, and prevent fulfillment risks.

Problem Addressed:

Disconnected data across marketing, warehouse, and pricing systems creates missed personalization, stockout risk, and inefficient discounts. These agents bring actionable intelligence for audience targeting, regional prioritization, supply chain resilience, and smarter pricing.

What the Agent Does:

• Customer Behaviour Intelligence Agent
Segments customers using RFM and demographic traits, highlights high-value personas, and identifies regions ripe for personalized offers.
• Demand Intelligence AI Agent
Detects volatile product categories and warehouse-level stock risks using time-series sales data. Suggests restocking or campaign adjustments to maintain fulfilment continuity.
• Dynamic Pricing Intelligence Agent
Evaluates pricing sensitivity by category, identifies best discount ranges, and flags any categories where pricing is hurting performance.

Standout Features:

• RFM-based segmentation fused with demographics and categories
• Warehouse-level forecasted demand vs. current stock coverage
• Volatility and risk scoring for categories
• Price elasticity detection and discount optimization
• Multi-agent output in structured JSON and actionable email formats

Benefits

The Engagement Optimization Agent performs advanced behavioral cohort analysis to identify high-performing day/time slots, optimal channels, and campaign strategies that maximize engagement rates and ROI. Designed for CRM and performance marketers, it operates over campaign_historical_data to extract actionable insights using clean logic, realistic business justifications, and precise calculations.

Problem Addressed:

Aging or overstocked inventory often leads to unnecessary carrying costs, financial losses, and inefficiencies across storage locations. Manual review of inventory health is time-consuming and prone to delays.

What the Agent Does:

AI agent identifies the most impactful day and time combinations to send Email and SMS campaigns using historical CRM data. It performs cohort-based analysis on engagement metrics and provides actionable strategy insights to CRM teams.This AI agent Also automates cohort-based A/B testing simulations using real performance data across Email and SMS campaigns. It evaluates variant performance, identifies statistically significant lifts, and generates insights with marketing-grounded justification

Standout Features:

● Cohort-Driven Performance Intelligence
Uses send-day and hour-based cohort analysis to identify peak engagement windows and optimal messaging strategies across both Email and SMS channels.
● ROI-Focused Channel & Campaign Insights
Evaluates Email vs SMS using real ROI and conversion metrics to recommend the most cost-effective engagement path per campaign.
● Automated A/B Test Generation with Real Lift
Simulates 10 real-world A/B tests using historical campaign variants, ensuring at least 38% performance lift based on open, click, or conversion rates.
● Marketing-Contextual Recommendations
Every insight is supported by realistic marketing rationale no generic conclusions enabling strategic decision-making tailored to campaign tone, urgency, or promo timing.

AutoFulfill AI Intelligent Exception Handling

Benefits

AutoFulfill AI monitors delivery operations in real-time using GPS and delivery logs to detect anomalies, predict SLA breaches, and automatically trigger rerouting or escalate to managers for timely resolution.

Problem Addressed

Before AutoFulfill AI, delivery exceptions were detected too late, leading to SLA breaches, customer complaints, and excessive manual intervention by dispatchers. This resulted in poor last-mile fulfillment and inefficient response cycles.

What the Agent Does

AutoFulfill AI ingests real-time delivery and GPS data, predicts potential SLA breaches, and takes autonomous action — such as rerouting the truck or escalating the case to a manager. It ensures operational continuity and removes the burden of constant monitoring from dispatch teams.

Standout Features

• SLA prediction before breach occurs
• Automated rerouting based on traffic and route feasibility
• Manager alerting with contextual information for manual override
• Learns from past delivery exceptions to enhance decision-making

Benefits

This AI agent intelligently evaluates, optimizes, and monitors the effectiveness of retail promotions. It recommends profitable campaigns, tracks real-time performance, and provides action-based summaries to marketing teams. The agent leverages both historical and live sales data to ensure maximum ROI and revenue impact while aligning with regional, seasonal, and customer-specific trends.

Problem Addressed:

Retail promotions often suffer from poor ROI due to lack of predictive insight, ineffective monitoring, and limited post-campaign evaluation. Manual assessments lead to overspending, missed seasonal opportunities, and low-margin offers.

What the Agent Does:

Track 1: Predictive Promotion Strategy
• Analyzes historical promotions and calculates sales uplift and ROI
• Forecasts only high-performing, profitable future campaigns
• Ensures alignment with seasonal timing, regional preferences, and product-category demand
Track 2: Real-Time Promotion Monitoring
• Tracks live promotions and evaluates in-flight performance
• Classifies promotions as repeat, monitor, or stop
• Summarizes key metrics (ROI, revenue, uplift) and flags underperformance
At the end, the system notifies the marketing team with a structured summary.

Standout Features:

• Predictive modeling using historical uplift and ROI metrics
• Real-time evaluation and action classification
• Auto-generated marketing summaries via email
• Festival-based timing alignment (e.g., Diwali, Back-to-School)
• Region and store-segment-aware recommendations

Benefits

This AI agent evaluates marketing campaign performance by analyzing underperforming and top-performing products. It appends performance metrics into datasets and triggers alerts based on product-level ROAS (Return on Ad Spend) thresholds.

Problem Addressed:

Marketing teams struggle to identify which products are consistently over-performing or draining ad budget. Traditional campaign reviews are manual and delay quick optimizations. This agent solves that by automating product-level ROAS analysis.

What the Agent Does:

• Begins when a new marketing campaign execution starts.
• Extracts campaign performance using campaign_performance dataset.
• Identifies top 10 and bottom 10 performing products based on ROAS.
• Appends categorized records (under/over-performers) to target datasets.
• Sends targeted alerts for products below the minimum acceptable ROAS.

Standout Features:

• Product-level granularity
• Automatic alerting for low ROAS
• Dataset appending for historical tracking
• Compact and highly automated flow

Benefits

This AI-powered agent automates demand forecasting, budget validation, vendor selection, and order placement for retail supply chains. It ensures inventory readiness aligned with forecasted demand, optimizes vendor selection for cost savings, and smartly manages budget allocation across priority products. The agent generates professional purchase requests, translates communications for vendors, and logs order details for traceability streamlining procurement workflows for efficiency and profitability.

Problem Addressed:

• Manual forecasting and procurement decisions are slow, error-prone, and disconnected from real-time sales forecasts and inventory levels.
• Budget overspending risks due to reactive or misaligned purchasing.
• Inefficient vendor selection results in missed cost-saving opportunities.
• Language barriers and inconsistent communication with vendors affect fulfillment speed.

What the Agent Does:

• Analyzes forecasted product demand and current inventory levels to determine reordering needs.
• Prioritizes products based on strategic importance.
• Validates purchase plans against available budgets, optimizing allocation.
• Selects vendors offering the lowest prices while planning split purchases over days to minimize costs.
• Generates friendly, translated vendor emails and logs detailed purchase records.
• Appends updated procurement data to master datasets for traceability.

Standout Features:

• Demand-driven procurement aligned with forecasted sales
• Automated budget validation with persuasive approval summaries
• Smart vendor selection using daily price analysis
• Multilingual order emails with polite, vendor-specific messaging
• Priority-aware budget allocation strategy
• Comprehensive order logging in CSV-compatible format
• Seamless integration with datasets for workflow execution

AI Operations Interpreter

Benefits

AI Operations Interpreter reviews product development conversations and comments to automatically detect risk signals, classify sentiment, and interpret key operational issues enriching product tracking data with context-aware insights.

Problem Addressed

Product development teams often log updates and decisions via comments or conversation threads, but those insights are unstructured and hard to track at scale. Teams miss early signals of risk, miscommunication, or blockers, resulting in delays, last-minute firefighting, and reactive decision-making.

What the Agent Does

AI Operations Interpreter automatically scans the “Recent Conversations” and “Latest Comments” fields in the product tracking dataset. Using natural language understanding, it detects sentiment and classifies the most critical issue expressed in the conversation. These insights are appended to each row for better decision-making and operational review.

Standout Features

• No keyword matching uses contextual language reasoning to classify issues
• Detects conversational tone
• Dynamically categorizes issues
• Supports multi-department visibility by turning conversations into structured alerts

Benefits

The Marketing Budget Optimization Assistant is an AI-driven decision support agent that helps brand and campaign managers maximize ROI and ROAS by intelligently reallocating media budgets across top-performing campaigns. It analyzes real-time performance data, identifies high-yield opportunities, and proposes optimized budget shifts backed by data-driven justifications across product lines, regions, and channels.

Problem Addressed:

The Marketing Budget Optimization Assistant solves the challenge of inefficient media spend allocation across marketing campaigns. Brand managers often struggle to identify which campaigns truly deliver high ROI and ROAS, leading to overspending on underperforming channels and missed opportunities in high-performing ones. This agent uses real-time campaign performance data to identify top-performing campaigns, calculate ROI impact, and propose intelligent budget reallocations, maximizing revenue and return without increasing overall spend.

What the Agent Does:

• The Marketing Budget Optimization Assistant intelligently analyses active marketing campaigns by evaluating their ROI, ROAS, and historical performance trends.
• It filters valid campaigns, selects the top 10 high-performers, and recommends optimized budget reallocations based on calculated ROI impact.
• For each recommendation, it provides a clear, data-driven justification—ensuring brand managers can reallocate budgets strategically to maximize returns across different products, channels, and regions.

Standout Features:

• Smart ROI & ROAS Filtering – Automatically filters high-performing campaigns using precise financial KPIs (ROI ≥ 200%, ROAS ≥ target).
• Dynamic Budget Reallocation – Suggests realistic budget increases (up to 40%) based on historical trends and AI-calculated ROI impact.
• Diversity-Aware Selection – Prioritizes a wide spread across product names and regions for balanced marketing growth.
• AI-Generated Strategic Justifications – Delivers clear, campaign-specific reasons for budget changes using performance insights.
• Impact-Driven Forecasting – Estimates the ROI uplift from proposed budget shifts, aiding in forward-looking media planning.

Benefits

A multi-stream AI agent designed to monitor financial ecosystems for fraud behavior, customer liquidity risks, and terminal anomalies. Each stream independently evaluates patterns, triggers condition-based responses, and automates communications to relevant stakeholders for preemptive action and continuous risk reduction.

Problem Addressed:

Fraudulent activity, liquidity depletion, and terminal anomalies require timely detection and action. Traditional monitoring tools often operate in silos and react after impact, failing to prevent financial and reputational damage.

What the Agent Does:

The agent initiates three parallel evaluations:
1. Fraud Behavior Intelligence Agent

• Uses customer + transaction data to detect high-risk patterns
• Applies a binary fraud classifier with confidence scoring
• Flags likely fraud transactions and emails the fraud team
• Includes approval/denial logic for further action

2. Customer Liquidity Risk Predictor

• Detects spending downturns, low forecasted balances
• Generates alerts to relationship managers
• Flags customers for follow-up
• Optionally emails customers for awareness

3. Terminal Risk Evaluator

• Monitors terminal usage spikes and outlier patterns
• Assigns risk scores
• Flags suspicious terminals to the terminal risk team
• Supports approve/deny and escalation

Standout Features:

• Multi-threaded parallel risk analysis
• Conditional logic with branching based on human review
• Email notifications segmented by function (fraud, customer, security)
• Predictive analysis based on rolling window data
• Real-time dataset updates and auto-decision points

Benefits

A chained suite of AI agents streamlining procurement decisions by forecasting SKU-level demand, selecting the best vendor using performance and cost, and simulating optimal vendor negotiation strategies all powered by clean, structured datasets and rules-based pricing logic.

Problem Addressed:

Procurement teams face bottlenecks due to siloed demand forecasting, manual supplier evaluation, and inconsistent negotiation strategies. This chain automates each stage to ensure reliable, cost-effective sourcing decisions.

What the Agent Does:

• SKU Demand Forecaster Agent
Forecasts SKU-level unit demand for the next 4 weeks using seasonality, historical patterns, and trends.
• Vendor Selection Agent
Evaluates vendors for each SKU based on vendor score and unit price. Selects the top vendor using scoring logic.
• Price Negotiator Agent
Simulates three-level negotiation strategy to reduce vendor cost using a percentage-based pricing logic.

Standout Features:

• Weekly demand prediction per SKU using actual consumption trends
• Supplier scoring and tie-breaking based on price vs performance
• Automated pricing negotiation simulation
• JSON-based output for integration and system action triggers
• In-place replacement of records in output datasets

Benefits

The Auto-Approve Maintenance Agent autonomously manages maintenance approvals in a manufacturing environment. It analyzes machine data including IoT alerts, failure history, and operational criticality to decide whether to approve, reject, or reschedule maintenance tasks. By automating decisions and updating central datasets, this agent reduces downtime, improves efficiency, and ensures timely intervention where needed.

Problem Addressed:

Manual approval and scheduling of maintenance tasks leads to delays, human bias, and reactive servicing, increasing machine downtime and reducing operational efficiency.

What the Agent Does:

Validates maintenance tasks using key factors such as machine criticality, IoT alert severity, failure history, and operational status. Flags low-risk or non-urgent tasks for rescheduling or rejection based on predefined logic. Automatically approves high-risk tasks, calculates impact and confidence scores, and updates both the main and priority maintenance datasets accordingly.

Standout Features:

● Autonomous decision-making engine with multi-factor logic
● Smart prioritization using impact and confidence scoring
● Scheduled or event-based batch execution
● Integration with existing maintenance datasets
● Validation and overwrite logic using machine ID

Benefits

Analyzes existing e-commerce product bundles for optimization and generates new high-performing combinations using customer behavior and transaction data to boost ROI, upsell rates, and conversion.

Problem Addressed:

Optimizing and enhancing product bundle performance across digital channels. Helps reduce bundle fatigue, lift AOV, and improve campaign efficiency by focusing only on high-converting combinations.

What the Agent Does:

• Flags bundles for retention, retirement, or adjustment
• Recommends pricing tweaks or bundle structure changes
• Discovers new bundles from historical non-bundle purchase patterns using frequent itemset mining
• Predicts the potential of each bundle before suggesting deployment

Standout Features:

• Classification into Recommended, Needs Adjustment, Applied, or Retire
• Predictive modeling for ROI, AOV, upsell rate, and conversion
• Automated generation of bundles by segment and season
• Enforces discount constraints and contextual relevance

Benefits

The Capacity Conflict Resolver Agent intelligently detects and resolves production capacity conflicts in real-time manufacturing environments. It evaluates job constraints such as machine overloads, labor shortages, maintenance requirements, and material readiness. Based on conflict type and efficiency calculations, it autonomously suggests job reallocations or reschedules to optimize utilization, minimize bottlenecks, and maintain smooth production flow.

Problem Addressed:

Unresolved production capacity conflicts—such as machine overloads, unavailable labor, or material delays—lead to inefficiencies, downtime, and missed production targets.

What the Agent Does:

Detects job-level production conflicts using factors like machine status, labor availability, maintenance flags, and material readiness. It classifies each conflict by type—Labor, Machine, Material, or Multi—and suggests actionable resolutions such as machine or shift reallocation. Calculates expected efficiency gains from proposed changes and prioritizes high-impact decisions. Confident suggestions are automatically updated into the production dataset, while complex conflicts are escalated to supervisors.

Standout Features:

● Identifies capacity conflicts using machine status, labor, maintenance, and material readiness.
● Classifies conflicts into Labor, Machine, Material, or Multi based on root cause.
● Recommends optimal reallocation plans or shift changes to resolve conflicts.
● Calculates expected efficiency gain to prioritize high-impact actions.
● Supports scheduled batch processing, dataset integration via job_order_id, and escalation for complex issues.

Benefits

This AI-powered safety intelligence agent detects, evaluates, and prioritizes hazardous environmental or sensor-triggered events across facilities. It correlates hazard signals with historical incidents, determines employee exposure risk, and issues real-time alert notifications with recommended safety actions. It ensures rapid, data-driven incident response and supports preventive safety management.

Problem Addressed:

Traditional hazard alert systems are reactive, fragmented, and disconnected from operational context (employee presence, historical risks). This leads to delayed response, unmanaged exposures, and missed preventive opportunities.

What the Agent Does:

• Detects hazards from real-time camera, sensor, and environmental data
• Correlates hazards with historical incident logs to assess recurrence risk
• Identifies employee exposure based on presence and shift data
• Computes a severity score and classifies priority (CRITICAL, MEDIUM, LOW)
• Generates alerts to safety teams and at-risk employees via email
• Logs hazard events with actions into datasets for compliance tracking

Standout Features:

• Multi-source hazard detection with sensor and camera fusion
• Incident correlation to derive intelligent risk scores
• Employee exposure evaluation using live operational data
• AI-based severity scoring with priority binning
• Dynamic email alerting with context-aware actions
• Full incident resolution log with append-to-dataset automation

Benefits

A comprehensive AI-powered suite that monitors marketing campaign performance across metrics such as conversions, revenue, CPA, budget efficiency, and customer sentiment. It forecasts future performance, identifies risks, and suggests strategic reallocation and content optimization actions all packaged in automation ready JSON and professional marketing summaries.

Problem Addressed:

Marketers often lack real-time insights into how campaigns will perform in the near future or how sentiment is shifting. Without automation, budget reallocation and damage control can be delayed. This suite solves that through continuous monitoring, forecasting, and intelligent suggestions based on structured, priority-filtered data.

What the Agent Does:

• Campaign Performance Forecaster
Predicts conversions, revenue, CPA, and campaign health for the next 7 days. Flags underperformance and outputs JSON/email summaries for decision-makers.
• Campaign Budget Optimizer
Identifies top-performing and at-risk campaigns based on ROI, CPA, and spend. Suggests reallocations and urgent budget actions.
• Campaign Sentiment Forecaster
Detects sentiment trends and spikes from feedback data. Flags declining performance and customer dissatisfaction risk.
• Customer Sentiment Risk Detector
Monitors individual customers' responses and predicts likelihood of churn or disengagement from campaigns based on low sentiment.

Standout Features:

• 7-day predictive forecasting of conversion and revenue KPIs
• Budget increase suggestions tied to ROI and confidence
• CPA risk tracking with corrective action prompts
• Real-time sentiment spike alerts with reason and impact

Benefits

This AI workflow conducts a multi-stage analysis of marketing campaigns. It performs segmentation, evaluates engagement, forecasts performance, allocates budgets, and generates executive summaries. The agent suite helps marketers optimize performance, reduce waste, and drive strategic decisions based on data.

Problem Addressed:

Campaigns often run with inefficient budgets, poor targeting, and inconsistent ROI. Manual analysis of campaign data lacks scalability. There’s a need for an intelligent system that segments, forecasts, and recommends actions automatically.

What the Agent Does:

This workflow includes 11 AI agents:

1. Campaign Segmentation Agent

• Segments campaigns by traffic source.
• Scores engagement performance.
• Flags top 10 campaigns.
• Sends summary to Marketing Analytics Team.

2. Campaign Performance Analyzer

• Computes ROI and conversion efficiency.
• Diagnoses underperformance.
• Sends diagnostics to Campaign Strategy Team.

3. Audience Engagement Predictor

• Forecasts future engagement.
• Scores CTR, bounce rate, and retention.
• Sends forecasts to Audience Team.

4. Creative Format Optimizer

• Evaluates format success (video, carousel, etc.).
• Suggests format-specific changes.

5. Channel Budget Allocator

• Evaluates spend efficiency.
• Recommends increase/decrease/hold by channel.
• Emails Financial Team.

6. Lead Funnel Drop-Off Analyzer

• Tracks drop-off in marketing funnel.
• Suggests fixes per stage.

7. Attribution & ROI Calculator

• Applies first/last/linear/time-decay attribution.
• Calculates attributed ROI.

8. CLV & Retention Predictor

• Predicts 6M & 12M Customer Lifetime Value.
• Identifies churn risk.
• Suggests loyalty interventions.

9. Campaign Anomaly Detector

• Detects outliers in campaign KPIs.
• Sends anomaly report to Ops Team.

10. Budget Optimization Agent

• Final budget optimization across sources.
• Recommends budget reallocations.

11. Integrated Insights Agent

• Executive summary across all agents.
• Merges ROI, anomalies, performance, and suggestions.

Standout Features:

• 11 modular AI agents
• Automatic segmentation, scoring, and diagnostics
• Predictive modeling and real-time insights
• End-to-end automation of campaign performance reviews

Benefits

The AI-Driven Inventory Disposal Agent intelligently analyzes aging, slow-moving, or at-risk inventory and recommends optimal disposal actions. Using shelf life, sales velocity, carrying cost, and item demand data, the agent selects between Dispose, Mark Down, or Transfer strategies. The goal is to minimize financial loss, reduce excess inventory, and improve warehouse efficiency while keeping decision-making autonomous and scalable.

Problem Addressed:

Aging or overstocked inventory often leads to unnecessary carrying costs, financial losses, and inefficiencies across storage locations. Manual review of inventory health is time-consuming and prone to delays.

What the Agent Does:

This agent scans aging and slow-moving inventory items in batches of 50. Based on sales velocity, shelf life, demand forecast, and markdown eligibility, it recommends one of three actions: Dispose, Mark Down, or Transfer. Each decision includes a rationale, financial impact simulation, operational impact score, and confidence score. The results are then appended or updated into the main disposal dataset.

Standout Features:

● Analyzes shelf life, velocity, and carrying cost to recommend data-driven actions
● Supports three intelligent decisions: Dispose, Mark Down, or Transfer.
● Simulates financial loss avoidance and operational impact
● Escalates high-risk, low-confidence cases to supervisors
● Integrates seamlessly with inventory systems using inventory_id

Benefits

This suite of AI agents analyzes in-store product sales, evaluates shelf efficiency, recommends optimal planogram placement, and identifies adjacent pairings to improve physical retail performance. It delivers a closed-loop system for data-driven product placement and merchandising strategy.

Problem Addressed:

Retailers often lack visibility into how product placement and shelf utilization impact sales. This agent suite solves inefficiencies in merchandising, optimizes shelf space, and enhances product visibility by leveraging real-world sales, shelf metadata, and transaction behavior.

What the Agent Does:

• Sales Intelligence Agent: Aggregates product performance by analyzing quantity sold, revenue, discount trends, and associated promotional campaigns.
• Shelf Efficiency Evaluator: Calculates sales velocity, shelf utilization (Underutilized, Overloaded, Optimal), and identifies visibility issues based on shelf position.
• Planogram Recommendation Agent: Recommends product reallocation, repositioning, or retention based on velocity, shelf utilization, and visual access.
• Product Adjacency Recommendation Agent: Detects high co-purchase pairs and suggests side-by-side shelf placement to drive impulse sales.

Standout Features:

• Velocity-based shelf optimization
• Co-purchase analysis for adjacency recommendations
• Multi-agent collaboration across sales and shelf metadata
• Intelligent filtering using visibility and discount data
• Actionable recommendations for every SKU

Benefits

This use case orchestrates a multi-stage AI agent workflow to evaluate influencers for brand campaigns, leveraging sentiment, audience alignment, and engagement performance from analytical scoring to report delivery for campaign teams.

Problem Addressed:

Brand managers often struggle with inconsistent influencer fit, poor audience alignment, and reputational risk. This chain evaluates and scores influencers programmatically, ensuring only relevant, well-aligned, and low-risk influencers are recommended.

What the Agent Does:

• AI Scoring AgentCalculates influencer fit using four scoring dimensions: ToneMatch, ValueResonance, Audience Match, and Risk.
• Fitment Ranker AgentConverts AI scores into final fitment ranks and comments on strengths/risks, generating CSV output for business review.
• Campaign Report Deliver AgentJoins fitment results with influencer profiles and generates campaign-specific reports with logic-based "Fit Status."
• Data Replacer AgentReplaces datasets (ai_analysis_results, fitment_scores, Report_Delivery) with the latest fitment results in exact formats.

Standout Features:

• 100-influencer processing limit
• Category validation to ensure relevance (e.g., Beauty, Fashion only)
• Four-dimensional AI fitment scoring
• Multi-level scoring rank + summary fit status (Recommended / Consider / Not Recommended)
• Report delivery in dataset-safe List[Text] format

Benefits

This AI-powered agent automates and enhances SEO metadata optimization for retail product and blog pages. It validates existing SEO titles, identifies issues, generates improved titles using trending keywords, and updates datasets for real-time impact. It ensures metadata quality, keyword alignment, and performance-readiness, significantly improving search visibility and content relevance.

Problem Addressed:

Validates SEO titles for completeness, correctness, keyword presence, formatting, and outdated content. Flags metadata issues like declining keywords or missing trending phrases.Generates improved titles with trending keywords and optimal SEO modifiers. Scores the improvements and calculates urgency. Updates both master and high-priority datasets automatically

Standout Features:

• Multi-step AI workflow for end-to-end SEO enhancement
• Title generator with keyword, year, and readability intelligence
• Priority-based metadata updates
• Metadata health scoring and keyword match analytics
• Integration with Domo datasets for seamless publishing

Benefits

A comprehensive suite of AI agents designed to ensure manufacturing stability and performance by forecasting production shortfalls, classifying location risks, and evaluating safety-related operational disruptions. Each agent delivers clear, structured JSON for system integration and human-readable summaries for stakeholders.

Problem Addressed:

Manufacturing operations are highly sensitive to shortfalls, safety incidents, and geographic risk. These agents help predict disruptions, diagnose root causes, and recommend mitigation strategies to ensure operational continuity.

What the Agent Does:

• Production Shortfall ForecasterPredicts plant-level production shortfalls using time-series data, flags underproduction risk, identifies root causes, and recommends immediate actions.
• Location Risk ClassifierEvaluates geographical and operational risks (logistics, safety, resources) to classify facility risk tiers and detect regional outliers.
• Safety Continuity EvaluatorAnalyzes incidents and labor impact to identify facilities at risk of disruption due to safety lapses. Alerts on safety trends and continuity concerns.

Standout Features:

• 30-day production forecasting with variance and probability scoring
• Facility-level risk scores with tier-based classification and regional deviation analysis
• One-priority-record logic ensures only the most critical insights are surfaced
• Generates structured JSON outputs and professional email summaries for leadership

Benefits

Recipe-level waste is reduced by identifying ingredient inefficiencies, tracing back to prep methods and portioning errors, and providing chef-friendly guidance — driven by historical usage data and recipe performance analytics.

Problem Addressed:

Kitchens frequently struggle with unnoticed ingredient-level waste due to lack of visibility, manual tracking limitations, and vague accountability. These inefficiencies inflate food costs and impact profitability without clear corrective actions.

What the Agent Does:

• Ingredient Waste Detection Agent
Analyzes historical kitchen data to identify recurring ingredient and recipe-level waste trends.
• Root Cause Analysis Agent
Links waste to recipe execution issues such as over-prep, portion inaccuracies, or storage problems.
• Action Recommendation Agent
Provides practical, chef-friendly recommendations tailored to reduce waste and improve recipe efficiency.

Standout Features:

• AI-driven detection of waste patterns at both ingredient and recipe levels
• Root cause insights with high operational relevance
• Contextual actions for chefs to reduce waste without complexity
• Reduced ingredient wastage and improved stock efficiency
• Lowered food cost through targeted operational improvements
• Strengthened kitchen accountability through data-backed decisions

Benefits

Warranty validation is made instant through real-time extraction of warranty card data, automated calculation of expiry based on purchase details, and accurate eligibility checks — powered by OCR and warranty rules logic.

Problem Addressed:

Manual verification of warranty details often leads to service delays, inaccurate eligibility assessments, and increased operational overhead. Without automation, support teams struggle to keep up with growing post-sales service demands.

What the Agent Does:

• Warranty Data Extraction Agent
Parses structured or unstructured warranty cards to extract purchase date, warranty period, and product information.
• End Date & Status Calculator Agent
Automatically computes the warranty end date and classifies current status as Valid or Expired.
• Eligibility Verifier Agent
Determines if the product qualifies for service actions such as repair or replacement based on warranty status.

Standout Features:

• Automated extraction and validation of warranty card data
• Accurate, date-based status classification (Valid/Expired)
• Instant eligibility checks for post-sales service
• Reduced manual effort for support teams
• Enhanced customer experience with faster service workflows

Benefits

This AI-powered agent proactively detects and mitigates return abuse in retail operations by profiling customer behaviors, analyzing product return patterns, and generating actionable insights. It flags high-risk customers and products, notifies internal teams, and automates return policy adjustments based on actual abuse trends, significantly reducing operational costs and fraud.

Problem Addressed:

Excessive product returns due to fraud, misuse of promotions, or product defects result in lost revenue, higher logistics costs, and degraded customer trust. Manual identification of such cases is time-intensive and reactive.

What the Agent Does:

• Identify customers with abnormal return behaviour patterns
• Analyse product return trends across categories and variants
• Flag return abuse cases using well-defined thresholds
• Route alerts to customer service and quality assurance teams
• Generate and deploy stricter return policies based on data-driven insights

Standout Features:

• Full behavioral profiling for customers and products using historical order/return data
• Automated return flagging with urgency and abuse type classification
• Dynamic policy rule generator per product category
• Two-way feedback loop into datasets for self-evolving optimization
• End-to-end execution in a no-code Domo workflow

Benefits

Invoice processing becomes smarter with automated validation against historical trends, anomaly detection, severity scoring, and routing to the appropriate stakeholders — accelerating resolution using SLA thresholds and financial rules.

Problem Addressed:

Finance teams often rely on manual invoice verification, making it difficult to detect subtle billing errors, overcharges, and data inconsistencies. This leads to increased operational risk, delayed payments, and potential financial losses.

What the Agent Does:

• Invoice Anomaly Detection Agent
Scans extracted invoice data to identify issues in dates, amounts, and unit pricing using historical benchmarks.
• Anomaly Classification Agent
Assigns severity levels, explains the reason for each anomaly, and attaches a confidence score to prioritize action.
• Finance Efficiency Booster Agent
Automates validation, reducing manual review time and enabling proactive error resolution.

Standout Features:

• Real-time detection of invoice anomalies using AI validation
• Categorization of issues by severity and confidence score
• Reduced manual effort for invoice checks
• Cost savings by identifying recurring billing errors and overcharges
• Enhanced fraud prevention and improved payment accuracy

Benefits

Supplier Catalog Ingestion Agent extracts supplier catalog from PDFs is validated by comparing it to historical norms, with discrepancies highlighted and flagged for procurement review.Supplier catalogs are intelligently parsed from PDFs, structured into clean datasets, and validated against historical norms — ensuring discrepancies are flagged and routed to procurement using predefined business logic.

Problem Addressed:

Procurement teams often face challenges due to errors or inconsistencies in supplier catalogs, leading to incorrect ordering, pricing conflicts, and delays. Manual validation is time-consuming and prone to oversight, impacting procurement speed and data quality.

What the Agent Does:

• Catalog Validation Agent
Analyzes extracted product details from PDF catalogs and validates them against historical supplier data.
• Discrepancy Detection Agent
Flags issues related to Minimum Order Quantity (MOQ), pricing mismatches, or product availability gaps.
• Tagging & Classification Agent
Applies factor-based tagging to catalog entries, categorizing based on accuracy and reliability.

Standout Features:

• AI-based PDF catalog parsing and validation
• Automated detection of data discrepancies in key fields like MOQ and pricing
• Factor-based tagging to enhance trust and traceability
• Reduced manual effort in catalog onboarding
• Improved accuracy and speed in procurement decisions

Benefits

Daily menu planning is optimized through demand forecasting and real-time inventory insights, while vendor alignment and smart procurement logic minimize food waste and improve profitability — seamlessly integrated with kitchen operations and inventory data.

Problem Addressed:

Kitchens often experience high food wastage due to overlooked ingredient-level inefficiencies, recipe misalignments, and lack of real-time, practical insights. Manual tracking fails to provide clarity, leading to rising costs and missed opportunities for improvement.

What the Agent Does:

• Ingredient Waste Detection Agent
Detects recurring ingredient-level waste using historical consumption, prep, and spoilage data.
• Recipe Association & Root Cause Agent
Maps ingredient waste to specific recipes and uncovers root causes like over-prep, incorrect portions, or storage issues.
• Chef Action Recommender Agent
Delivers contextual, chef-friendly suggestions (e.g., portion adjustment, recipe tweaks, alternate usage) to reduce waste.

Standout Features:

• AI-powered detection of ingredient-level and recipe-level waste patterns
• Root cause identification to eliminate guesswork
• Chef-friendly recommendations for operational impact
• Reduced ingredient wastage and improved stock utilization
• Lowered food cost through better kitchen efficiency
• Boosted kitchen accountability through data-driven insights

Benefits

User behavior during cart sessions is monitored to detect abandonment triggers, uncover likely reasons, and auto-deploy personalized recovery strategies — all based on historical session patterns and marketing performance data.

Problem Addressed:

High cart abandonment rates continue to impact revenue, often due to untracked behavioral triggers such as pricing concerns, user experience issues, or decision paralysis. Manual investigation is time-consuming, and recovery strategies often lack personalization and timing.

What the Agent Does:

• Abandonment Behavior Analyzer Agent
Examines cart abandonment sessions and infers key behavioral drivers using historical patterns and clickstream activity.
• Reason Classification & Strategy Agent
Categorizes abandonment causes such as price sensitivity, comparison behavior, or UX friction, and suggests recovery strategies.
• Personalized Re-engagement Recommender Agent
Generates timely, context-aware follow-up actions to re-engage users and support sales recovery.

Standout Features:

• AI-driven analysis of cart abandonment reasons using user behavior signals
• Categorization of behavioral triggers for targeted action
• Personalized recovery recommendations for each session
• Reduced funnel leakage and improved revenue recovery
• Accelerated campaign execution with system-generated insights for marketing teams

Benefits

Customer feedback is transformed into actionable insights by analyzing product reviews for sentiment, detecting recurring pain points, and auto-assigning tasks — with built-in escalation paths through Buzz and email for unresolved issues.

Problem Addressed:

Customer feedback channels often become overwhelming due to high review volumes, making it hard to triage negative comments, identify root causes, and resolve issues promptly. Manual analysis leads to delays, missed signals, and reputational impact.

What the Agent Does:

• Review Sentiment Analyzer Agent
Processes customer reviews to score sentiment, extract key phrases, and identify the main concern.
• Concern Categorization & Action Recommender Agent
Classifies the issue into actionable categories, recommends resolution steps, and assigns a priority level.
• Escalation & Routing Agent
Maps the issue to the right department, logs it in Buzz, sends emails to the assignee/owner, and triggers QA escalation if needed.

Standout Features:

• AI-powered sentiment scoring and keyword extraction
• Auto-categorization and prioritization of customer concerns
• Suggested next actions tailored to the issue type
• Seamless integration with Buzz for issue tracking and email for owner notifications
• Escalation workflows to QA for critical issues
• Faster response and improved customer satisfaction through structured feedback handling

Benefits

It analyzes historical footfall data and special event schedules to predict traffic by zone, section, and shift. It then compares predicted demand with current staffing to classify sections as Understaffed, Sufficient, or Overstaffed. It highlights urgent needs and suggests staff reallocation accordingly.

Problem Addressed:

This agent addresses inefficiencies in retail staffing by aligning workforce allocation with predicted footfall. It reduces understaffing risks during peak hours and prevents resource waste from overstaffing in low-traffic zones.

What the Agent Does:

It analyzes historical footfall data and special event schedules to predict traffic by zone, section, and shift. It then compares predicted demand with current staffing to classify sections as Understaffed, Sufficient, or Overstaffed. It highlights urgent needs and suggests staff reallocation accordingly

Standout Features:

• Hourly footfall prediction using historical and event-driven trends
• Real-time staffing sufficiency analysis with reasoning
• Urgency scoring for immediate leadership attention
• Actionable reallocation recommendations with impact projections

Benefits

Monitors tenant communications across multiple channels, classifies sentiment and urgency, summarizes top complaint themes by property, and generates daily alerts for property managers.

Problem Addressed:

Before this agent, property teams had to manually sift through emails, feedback forms, and chat logs to identify complaints. This reactive process often led to missed negative sentiment trends, recurring tenant frustrations, and delayed escalation of high-priority issues.

What the Agent Does:

• The agent continuously scans tenant interactions, detects sentiment and urgency levels, surfaces recurring complaint themes, and sends daily summaries and alerts to building managers.
• It enables proactive service and faster issue resolution.

Standout Features:

• Multi-source sentiment detection (email, chat, feedback forms)
• Complaint theme clustering at the building level
• Urgency classification for escalations
• Daily summaries to managers with top issues
• Customizable thresholds and feedback taxonomy

Benefits

Automates resume parsing, evaluates candidates using AI-based scoring models, and shortlists top matches based on skills, experience, notice period, and compensation fit. Notifies recruiters with best-fit profiles.

Problem Addressed:

Recruiters previously relied on manual screening processes, often leading to inconsistent evaluations, delayed role fulfillment, and difficulty comparing resumes due to unstandardized skill sets or fragmented applicant data.

What the Agent Does:

• The agent parses incoming resumes, applies AI scoring models to evaluate candidates against open job roles, and shortlists top matches.
• It also pushes recruiter alerts and updates the hiring CRM with prioritized candidate profiles.

Standout Features:

• Resume parsing and structuring from various file formats
• AI-based scoring on skill match, notice period, CTC alignment, and history
• Total match score ranking
• Automated recruiter notifications with top profiles
• CRM integration for candidate tracking and status updates

Benefits

Analyzes new IT incident tickets, recommends resolution steps, auto-assigns tickets to the most suitable resolver, and alerts managers about SLA risks.

Problem Addressed:

Before SmartResolver, IT teams were stuck manually triaging incoming tickets, often without historical context. Poor assignment logic led to delays, and critical tickets would silently breach SLAs due to a lack of real-time monitoring.

What the Agent Does:

• SmartResolver applies machine learning on past ticket data to recommend resolutions.
• Assign to the best-suited resolver based on ticket context and performance history, and notifies managers of SLA breaches automatically.

Standout Features:

• AI-driven resolver assignment
• Real-time SLA breach alerts
• Context-aware resolution suggestions
• SLA-based prioritization for escalations
• Auto-summarized ticket insights for management

Benefits

When a competitor is identified in a deal, the agent classifies competitor strengths, recommends tailored internal pitch decks, and surfaces key differentiation points to help sales teams position effectively.

Problem Addressed:

Sales teams often rely on generic content when competing against known rivals. Without real-time competitor insights or differentiated messaging, reps miss critical opportunities to address buyer objections and highlight strengths.

What the Agent Does:

• This agent identifies named competitors in lead or opportunity records, classifies them based on historical deal data.
• Recommends the most relevant pitch decks tagged against those competitors, and provides a list of talking points for strategic differentiation.

Standout Features:

• Competitor recognition from opportunity records
• Classification of competitor strengths based on win/loss history
• Tailored pitch deck recommendations
• Auto-generation of value differentiation points
• Delivery via Buzz, CRM notification, or custom App Studio dashboard

Benefits

24 hours before a discovery call, this agent compiles lead details, competitor insights, and known pain points to generate personalized talking points and pre-drafted outreach emails. Delivers content via Buzz and dashboard.

Problem Addressed:

Sales reps often enter discovery calls underprepared, with inconsistent messaging and limited insights into lead context, resulting in missed opportunities and slower relationship-building.

What the Agent Does:

• The agent automatically gathers relevant data 24 hours before a scheduled call including lead info, competitive landscape, and historical pain points to generate custom talking points and draft emails.
• It then delivers these assets directly to the rep via Buzz or a call prep dashboard.

Standout Features:

• Automated call prep 24 hours in advance
• AI-generated talking points tailored to the lead and context
• Email draft creation for follow-up or Benefits
• Competitive and pain point insights embedded
• One-click access via Buzz and dashboard

Benefits

Automatically assigns inbound leads to the most suitable sales representative based on geographic region and language compatibility. Sends notifications or updates the CRM accordingly.

Problem Addressed:

Previously, leads were manually routed to sales reps, leading to delays, mismatches in regional/language support, and inconsistent follow-ups. High-potential leads were often lost due to misalignment or response time issues.

What the Agent Does:

• The agent automatically evaluates new leads against the Sales_Reps table, assigning them based on region and language fit.
• It then either sends an instant notification to the assigned rep or updates the CRM, ensuring quick and accurate follow-up.

Standout Features:

• Auto-assignment using region and language logic
• Instant rep notifications or CRM updates
• Dynamic match based on real-time rep availability or workload
• Audit trail of lead assignment
• Seamless integration with lead intake systems

Benefits

Extracts structured lease data from PDFs and scanned documents, validates key terms, sends compliance alerts, and auto-populates lease dashboards with ERP/CRM integration

Problem Addressed:

Before automation, lease data was manually extracted from PDFs and images, leading to errors, compliance gaps, delayed renewals, and fragmented data across systems. Teams lacked visibility into lease terms and expirations in real time.

What the Agent Does:

• The agent scans lease agreements, extracts structured fields like start/end dates, rent clauses, and obligations.
• It validates extracted content, raises alerts for compliance and renewals, and populates dashboards integrated with ERP/CRM systems for full lease lifecycle visibility.

Standout Features:

• Automated extraction from PDFs, scanned images, and email attachments
• Clause-level validation for compliance risks
• Proactive alerts for expirations and critical terms
• Lease analytics dashboards
• Seamless sync with ERP and CRM platforms

Benefits

Recommends the top three most relevant pitch decks from a content repository based on a lead’s industry and expressed pain points. Uses contextual scoring and delivers content links directly to the sales team via Buzz or email.

Problem Addressed:

Sales reps often spend time searching manually through vast content libraries for relevant pitch decks. This causes delays, inconsistencies in pitch messaging, and missed opportunities due to mismatched or generic presentations.

What the Agent Does:

• The agent leverages contextual information from the lead, such as industry, problem statement, or campaign to rank and recommend the top three pitch decks.
• It then delivers the links directly to the rep via Buzz or email, accelerating response time and standardizing quality.

Standout Features:

• Context-aware scoring of decks based on industry and pain points
• Automated retrieval of top 3 matching pitch decks
• Seamless delivery to sales channels (Buzz, email)
• Maintains a dynamic repository for up-to-date content
• Integrates with lead intake systems for proactive recommendations