The Client:
A Leading Provider Of Home Improvement Products And Services
The client is a major U.S. provider of home improvement products and services, operating 20+ locations nationwide. As one of the top 5 replacement window providers in the USA, the company serves thousands of customers annually with custom window, door, and siding installations.
Company Profile:
- 900+ employees nationwide
- ~$600M in annual sales
- Processing 8,000+ custom orders per month
- Complex multi-location operations across 20+ service centers
Every order depends on accurate measurements, product configurations, pricing, materials, and installer availability. Sales reps collected order details manually, which the operations team then converted into materials orders, custom specifications, labor assignments, and installation schedules.
This manual process created risk at every step. Wrong pricing reduced margin. Wrong measurements triggered costly reorders. Wrong configurations delayed fulfillment. Wrong labor scheduling created operational waste.
Despite a 110-person Shared Services team reviewing orders against a ~30-item post-sale checklist, errors still slipped through, costing the company approximately $5 million per year.
info JBS Dev deployed an Agentic AI validation system that auto-releases 40% of orders with zero human touch, flags 21% for exception review, and eliminates $5M in annual operational losses.
The Problem:
~$5M in Preventable Operational Losses
The client faced a perfect storm of data inconsistency, manual bottlenecks, and costly errors across thousands of monthly orders.
Manual Review Bottleneck
- 110-person Shared Services team reviewing every single order manually
- Spending 80% of time on routine validation instead of high-risk exceptions
- Installation crews returning for remeasurement and reinstallation
- Wasted capacity on rework instead of new installations
Configuration & Measurement Errors
- Product configurations sold that weren't actually available
- Small measurement errors making orders completely unusable
- Custom windows remade due to measurement errors
- Materials scrapped when configurations didn't match availability
- Rush reorder fees to meet delayed installation commitments
Data Quality Issues
- Handwritten orders with unstructured formats and missing fields
- Customer notes containing conflicts with structured order data
- Manual validation against ~30-item checklist across 20+ centers
- Re-shipping corrected products to 20+ locations
- Expedited freight to recover from delayed orders
Customer Satisfaction Impact
- Configuration conflicts and measurement errors only surfaced during installation
- Delayed installations causing customer frustration
- Discounts issued for delayed installations
- Service credits for customer inconvenience
- Lost repeat business and referrals
info Total Annual Loss: ~$5M
JBS Dev's Approach:
A Smarter Review Layer Inside the Existing Microsoft Stack
Rather than replace the client's existing systems, JBS Dev built an Agentic AI validation layer inside the client's Microsoft ecosystem, integrating seamlessly with Dynamics 365, Dataverse, Azure OpenAI, and Power BI.
The system monitors incoming orders in real-time, checking each against a ~30-item post-sale checklist: required fields, measurements, product configurations, customer notes, pricing details, and installation requirements.
Clean orders move forward automatically. Incomplete, inconsistent, or high-risk orders are flagged with risk scores and routed to Shared Services with full context for faster review.
The goal wasn't to remove people from the process. It was to stop making people review every order by hand and let them focus on solving the exceptions that truly need human expertise.
AI validation layer built on Microsoft ecosystem
The Transformation:
From Manual Bottleneck to Intelligent Automation
cancel Before: Manual Review Bottleneck
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110-person team manually reviewing every single order against a 30-item checklist
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$5M annual losses from measurement errors, configuration mismatches, and reorders
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Handwritten notes created data inconsistencies across 20+ service centers
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Errors still slipped through manual reviews, causing customer delays and costly rework
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Team drowning in routine checks instead of solving high-risk exceptions
check_circle After: AI-Powered Validation System
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40% of orders auto-released—zero human touch for clean orders
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~$5M saved annually through reduced labor, remanufacturing, shipping, and customer credits
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Real-time AI validation catches conflicts in structured data and unstructured notes
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60% reduction in manual review workload—team focuses on true exceptions
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Shared Services freed up to solve problems instead of processing routine orders
ANNUAL SAVINGS
Operational cost reduction (labor, remanufacturing, shipping, customer credits)
LESS MANUAL WORK
Reduction in manual review workload
AUTO-RELEASED
Orders auto-released with zero human touch
FLAGGED FOR REVIEW
Orders flagged for exception review
CONFIDENCE SCORE
AI confidence on sample flagged order
How We Built It: Agentic Validation Flow
Monitor & Ingest
Real-time order monitoring across Dynamics 365 and Dataverse
Evaluate & Flag
Auto-check against 30-item checklist with 0-100 risk scoring
Validate Data
Analyze structured fields and unstructured notes using NLP
Automate & Learn
Auto-release clean orders; continuously improve detection
Route Exceptions
Flag high-risk orders with context for human review
verified_user Human-in-the-Loop at Critical Points: High-risk orders are routed to human reviewers with context, not autonomous decisions. The system helps reviewers move faster without removing expert judgment from the process.
What Made This Work
Built Inside the Microsoft Ecosystem
JBS Dev did not replace the client's existing systems. The solution was built around Dynamics 365, Dataverse, Azure OpenAI, Power Automate, and Power BI—integration beats replacement every time.
Human-in-the-Loop for High-Risk Decisions
High-risk orders are routed to human reviewers with context. The system doesn't make critical decisions autonomously—it flags issues and provides summaries so reviewers can act faster with full confidence.
AI Agents for Unstructured Data
Traditional validation tools only check structured fields. JBS Dev's AI Agents analyze customer notes, catching conflicts like "frosted glass requested but order shows clear glass." 30%+ of critical details live in unstructured notes.
Continuous Learning Without Rebuild
The system learns from every flagged order and outcome, improving detection accuracy over time. New business rules can be added dynamically without rebuilding the entire validation engine—Agentic AI adapts to your business.
Senior-Led Engineering Avoided Costly Rework
A 100% senior engineering team designed a system that anticipated edge cases from day one. No junior developers guessing at business rules. The client avoided the 6-12 months of rework that plague most AI projects.