Representative Projects

Example outcomes that demonstrate our craft

Each engagement represents our systematic approach to AI implementation: deep understanding of the problem, purposeful architecture, and measurable delivery.

The Challenge

A regional home services company operating across 12 locations struggled with fragmented scheduling, inconsistent customer communication, and manual dispatch coordination that led to missed appointments and revenue leakage.

Our Solution

We deployed an AI-powered operations hub that unified scheduling across all locations, automated customer confirmations and follow-ups, and implemented intelligent dispatch routing based on technician skills, location, and workload.

Integration Stack

CRM IntegrationCustom Scheduling EngineSMS/Email AutomationReal-Time Dashboard

Outcome Metrics

42%
Reduction in missed appointments
3.2x
Faster dispatch response time
28%
Increase in daily job completion
$340K
Estimated annual revenue recovered
Delivered in 8 weeks
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The Challenge

A property management team overseeing 2,000+ units was drowning in maintenance requests, lease renewals, and tenant communications. Response times averaged 72 hours, and tenant satisfaction scores were declining.

Our Solution

We built an AI-driven tenant management system with automated request triage, predictive maintenance scheduling, and intelligent lease renewal workflows that prioritized high-value tenant retention.

Integration Stack

Document ProcessingMaintenance AI TriageTenant PortalAnalytics Dashboard

Outcome Metrics

68%
Faster maintenance response time
91%
Tenant satisfaction score
35%
Reduction in operational overhead
12%
Improvement in lease renewal rates
Delivered in 10 weeks
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The Challenge

A logistics company managing 200+ daily routes was relying on manual planning, resulting in suboptimal routes, excess fuel costs, and inconsistent delivery windows that frustrated commercial clients.

Our Solution

We designed an AI route optimization system that factors in traffic patterns, delivery priorities, vehicle capacity, and driver availability to generate optimal daily plans with real-time adjustments.

Integration Stack

Route Optimization EngineFleet Tracking IntegrationClient PortalPerformance Analytics

Outcome Metrics

23%
Reduction in fuel costs
31%
Improvement in on-time delivery
18%
Increase in daily delivery capacity
45min
Saved per driver per day
Delivered in 10 weeks
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The Challenge

A healthcare admin group managing intake for 8 practices faced chronic bottlenecks: manual form processing, insurance verification delays, and scheduling conflicts that reduced patient throughput by 30%.

Our Solution

We built an automated intake pipeline that digitizes patient forms, verifies insurance eligibility in real-time, and schedules appointments based on provider availability and patient priority scoring.

Integration Stack

Form Processing EngineInsurance API IntegrationSmart SchedulingStaff Dashboard

Outcome Metrics

74%
Reduction in intake processing time
40%
Decrease in scheduling conflicts
22%
Increase in patient throughput
60%
Reduction in manual data entry
Delivered in 9 weeks
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The Challenge

A fast-growing e-commerce brand handling 2,000+ support tickets weekly couldn't scale their team fast enough. Average resolution time exceeded 48 hours, and CSAT was dropping below acceptable thresholds.

Our Solution

We deployed an AI-powered support system that autonomously resolves routine inquiries (order status, returns, FAQs), intelligently routes complex issues, and provides agents with real-time context and suggested responses.

Integration Stack

AI Support AgentTicket Routing EngineCRM IntegrationCSAT Analytics

Outcome Metrics

62%
Of tickets resolved autonomously
78%
Reduction in average response time
94%
Customer satisfaction score
4x
Support capacity without new hires
Delivered in 7 weeks
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The Challenge

A regional accounting firm reconciling thousands of transactions monthly across 40+ client accounts was losing significant billable hours to manual data matching, exception handling, and report generation.

Our Solution

We built an AI reconciliation engine that automatically matches transactions across bank feeds and ledgers, flags discrepancies with context-aware explanations, and generates exception reports for human review.

Integration Stack

Transaction Matching EngineAnomaly Detection ModelClient PortalReporting Dashboard

Outcome Metrics

88%
Reduction in manual reconciliation time
99.2%
Transaction matching accuracy
320hrs
Saved monthly across all clients
$180K
Annual savings in labor costs
Delivered in 8 weeks
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The Challenge

An insurance agency network processing 1,200+ claims monthly was plagued by inconsistent documentation, slow handoffs between departments, and a 15-day average claims cycle that frustrated policyholders.

Our Solution

We implemented an end-to-end claims processing pipeline with AI-powered document extraction, automated damage assessment classification, intelligent routing based on claim complexity, and real-time status tracking for policyholders.

Integration Stack

Document AIClaims Classification ModelWorkflow AutomationPolicyholder Portal

Outcome Metrics

58%
Reduction in claims cycle time
71%
Faster initial claim assessment
34%
Decrease in documentation errors
89%
Policyholder satisfaction score
Delivered in 11 weeks
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The Challenge

A staffing agency onboarding 300+ contractors monthly across multiple states was struggling with compliance paperwork, credential verification delays, and inconsistent onboarding experiences that led to high early attrition.

Our Solution

We designed an AI-powered onboarding platform that automates document collection and verification, ensures state-specific compliance requirements, personalizes training paths, and provides real-time onboarding progress tracking for hiring managers.

Integration Stack

Document Verification AICompliance EngineOnboarding PortalHR Dashboard

Outcome Metrics

65%
Faster onboarding completion
92%
Compliance audit pass rate
27%
Reduction in early attrition
3x
Increase in onboarding capacity
Delivered in 9 weeks
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The Challenge

A 25-location retail chain was losing revenue through both stockouts and overstock situations. Manual forecasting based on historical averages failed to account for seasonal trends, promotions, and local demand variations.

Our Solution

We deployed a predictive inventory system that analyzes sales velocity, seasonal patterns, promotional calendars, and external signals to generate location-specific demand forecasts and automated reorder recommendations.

Integration Stack

Demand Forecasting ModelPOS IntegrationInventory Optimization EngineManager Dashboard

Outcome Metrics

41%
Reduction in stockout incidents
33%
Decrease in excess inventory costs
$890K
Annual revenue recovered
94%
Forecast accuracy rate
Delivered in 10 weeks
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