Real Transformations, Real Results

Challenge:

We needed to rebuild our entire supply chain prediction system with AI, but the risk was enormous. One wrong decision could cost millions.

Solution:

We completed the transformation on schedule with no major setbacks. Our prediction accuracy improved from 72% to 94%, saving $12M annually.

Challenge:

We had attempted this project twice before, burning $5M total with nothing to show. Vague requirements led to endless revisions and vendor disputes.

Solution:

The project delivered in 6 months exactly as specified. Our routing efficiency improved 40%, and we avoided the usual $2M in scope changes.

Challenge:

We were confident in our approach until Durante's audit revealed that our data pipeline couldn't scale, our ML model was overfitted, and our vendor's architecture was proprietary.

Solution:

Our AI tutoring platform launched to 500K students without a hitch. The platform scales beautifully, and we own our technology stack.

Challenge:

We had been stuck in planning mode for 12 months, unable to decide between vendors, architectures, and approaches. Every meeting ended with more questions.

Solution:

Our recommendation engine now powers 80% of user engagement. Time-to-decision dropped from years to weeks. We're now leading our market segment.

Challenge:

Our claims AI was 18 months late, $8M over budget, and still didn't work. The vendor blamed us, we blamed them. The board was ready to kill the project.

Solution:

Durante rewrote the specs in 6 weeks, we switched vendors, and launched in 5 months. The system now processes 10K claims/day with 98% accuracy.

Challenge:

Our fraud detection AI had a 40% false positive rateunusable. We'd invested $15M over 2 years and were about to write it off as a total loss.

Solution:

We rebuilt the training pipeline in 3 months. False positives dropped to 2%. The system now saves us $30M annually in fraud prevention.

Challenge:

After 2 years and $10M, our predictive maintenance AI predicted nothing. Accuracy was 35%worse than random. The CEO wanted answers, and I had none.

Solution:

We launched a scaled-down version in 3 months that actually delivered value. Now we're expanding it based on proven results. Downtime reduced 60%.

Challenge:

Solution:

Challenge:

Solution:

Challenge:

We needed to rebuild our entire supply chain prediction system with AI, but the risk was enormous. One wrong decision could cost millions.

Solution:

We completed the transformation on schedule with no major setbacks. Our prediction accuracy improved from 72% to 94%, saving $12M annually.

Challenge:

Solution:

Challenge:

We had attempted this project twice before, burning $5M total with nothing to show. Vague requirements led to endless revisions and vendor disputes.

Solution:

The project delivered in 6 months exactly as specified. Our routing efficiency improved 40%, and we avoided the usual $2M in scope changes.

Challenge:

We were burning through $3M annually on failed AI initiatives. Our team was demoralized, and I was losing credibility with the board. Every vendor promised the moon but delivered chaos.

Solution:

Today, all our AI projects launch on time and on spec. Our development velocity increased 3x, and we've saved $4.5M in prevented failures. The board now sees AI as our strategic advantage.

Challenge:

We were confident in our approach until Durante's audit revealed that our data pipeline couldn't scale, our ML model was overfitted, and our vendor's architecture was proprietary.

Solution:

Our AI tutoring platform launched to 500K students without a hitch. The platform scales beautifully, and we own our technology stack.

Challenge:

Our AI initiatives were siloed, expensive, and unpredictable. We had 4 different teams building similar solutions, wasting resources and creating technical debt.

Solution:

We now have a cohesive AI strategy, reusable components, and predictable delivery. Our time-to-market dropped from 9 months to 6 weeks for new AI features.

Challenge:

We had been stuck in planning mode for 12 months, unable to decide between vendors, architectures, and approaches. Every meeting ended with more questions.

Solution:

Our recommendation engine now powers 80% of user engagement. Time-to-decision dropped from years to weeks. We're now leading our market segment.

Challenge:

We were locked into an expensive AI vendor whose solution didn't scale. Every change request cost $100K+ and took months. We felt trapped.

Solution:

We migrated to an open architecture in 4 months, cut our AI infrastructure costs by 60%, and now iterate 10x faster. Best decision we ever made.

Challenge:

Our claims AI was 18 months late, $8M over budget, and still didn't work. The vendor blamed us, we blamed them. The board was ready to kill the project.

Solution:

Durante rewrote the specs in 6 weeks, we switched vendors, and launched in 5 months. The system now processes 10K claims/day with 98% accuracy.

Challenge:

Our fraud detection AI had a 40% false positive rateunusable. We'd invested $15M over 2 years and were about to write it off as a total loss.

Solution:

We rebuilt the training pipeline in 3 months. False positives dropped to 2%. The system now saves us $30M annually in fraud prevention.

Challenge:

After 2 years and $10M, our predictive maintenance AI predicted nothing. Accuracy was 35%worse than random. The CEO wanted answers, and I had none.

Solution:

We launched a scaled-down version in 3 months that actually delivered value. Now we're expanding it based on proven results. Downtime reduced 60%.