Case studies
Recovering $12M in the first year through payment integrity analytics
Healthcare insurance payer
A payer was paying claims it should not have been paying, and the existing rules engine could only catch patterns somebody had already thought to write a rule for. Anomaly detection across procedure and diagnosis coding, combined with provider behavior profiling, surfaced the leakage the rules were structurally unable to see.
- Recovered in the first year of the program
- $12M
- Recovered in the first year of the program
- Coding mismatch detection at claim-line level
- CPT–ICD
- Coding mismatch detection at claim-line level
- Behavioral profiling against peer baselines
- Provider
- Behavioral profiling against peer baselines
Cutting chargebacks by millions while keeping false positives under 8%
Banking and insurance clients
High-volume transaction fraud is a balancing problem rather than a detection problem. Catching more fraud is straightforward if you accept a review queue nobody can staff and a customer experience nobody can defend. This work optimized post-screen detection to reduce annual chargebacks by $2M to $10M while holding false-positive review below 8%.
- Annual chargeback reduction, depending on book size
- $2M–$10M
- Annual chargeback reduction, depending on book size
- False positive rate on flagged transactions
- <8%
- False positive rate on flagged transactions
- Internal adjuster fraud detected through hierarchical models
- <0.1%
- Internal adjuster fraud detected through hierarchical models
Reducing customer acquisition cost by more than 20% across channels
Retail banking and card issuers
Acquisition spend was being allocated on attribution nobody fully trusted, and cross-sell into the existing deposit base was running on generic offers. Reworking next-best-action, offer targeting, and channel allocation reduced cost per acquired customer by more than 20% and lifted annual card spend by roughly $2,000 per acquired user.
- Reduction in cost per acquired customer
- 20%+
- Reduction in cost per acquired customer
- Additional annual card spend per acquired user
- $2,000
- Additional annual card spend per acquired user
- Improvement in return on advertising spend
- 7%
- Improvement in return on advertising spend
Cutting churn by up to 25% and winning back customers at five times the industry rate
Banking and insurance clients
Retention programs usually fire after the customer has already decided to leave. Modeling both the active triggers of churn risk and the quieter passive indicators moved intervention earlier, producing 10% to 25% churn reduction on a net-positive basis, alongside win-back rates of 3% to 5% against an industry norm below 1%.
- Churn reduction from trigger-based intervention
- 10–25%
- Churn reduction from trigger-based intervention
- Additional reduction from passive risk indicators
- 3–7%
- Additional reduction from passive risk indicators
- Win-back rate, against an industry norm below 1%
- 3–5%
- Win-back rate, against an industry norm below 1%
Compressing a 20-hour weekly reporting cycle into ten minutes
Regional bank and financial services operations
Operations teams were spending more than twenty hours a week assembling the reports that leadership reviewed, and underwriters were spending their capacity on intake rather than on decisions. Automating operational reporting and application intake returned that time to the work only people can do.
- Weekly operational reporting cycle
- 20 hrs → 10 min
- Weekly operational reporting cycle
- Of credit card applications processed through automated intake
- 65%
- Of credit card applications processed through automated intake
- Call handling time through routing and web redirection
- Reduced
- Call handling time through routing and web redirection
Building the model governance that stops silent drift from costing millions
Banking, insurance, and CPG clients
Models degrade quietly. Approvals and rejections drift away from what the business intended, manual overrides climb, and nobody notices until a bad quarter forces someone to look. Establishing measurement, drift detection, automated retraining, and review triggers returned $500K to $2M in the first year depending on book size.
- First-year return depending on book size
- $500K–$2M
- First-year return depending on book size
- Detection with defined review and retraining triggers
- Drift
- Detection with defined review and retraining triggers
- Documentation designed for regulatory examination
- Audit
- Documentation designed for regulatory examination
Replacing 35 disconnected systems with one analytics platform
National uniform and facility services company
A nationwide services business was running on 35 disconnected systems, which meant no comprehensive view of performance, invoice disputes that extended the cash collection cycle, and a reporting function that consumed enormous manual effort. Consolidating onto a governed Azure platform cut reporting labor by 70% and delivered around $12M in annual savings.
- Annual cost savings from efficiency and predictive analytics
- $12M
- Annual cost savings from efficiency and predictive analytics
- Reduction in manual reporting labor
- 70%
- Reduction in manual reporting labor
- Improvement in data accuracy against a single source of truth
- 95%
- Improvement in data accuracy against a single source of truth
Moving quality control from statistical sampling to prediction
High-tech manufacturing operations
Quality control costs had risen 78% in three years while roughly 35% of defects still escaped inspection, because statistical sampling and human visual inspection could not keep pace with production complexity. Deep learning on existing microscopy, sensor analytics, and predictive quality models cut quality control costs by 45% over two years.
- Reduction in quality control costs over two years
- 45%
- Reduction in quality control costs over two years
- Defect detection accuracy, with 84% fewer false positives
- 96.7%
- Defect detection accuracy, with 84% fewer false positives
- Annual capital expenditure deferred
- $4.2M
- Annual capital expenditure deferred
Unifying student data across 125,000 learners on Google Cloud
Remote learning institution
A remote learning institution serving more than 125,000 students generated rich data across student information, learning management, and curriculum systems, none of which spoke to each other. A governed BigQuery and Looker platform brought those streams together to support early intervention, faculty insight, and executive reporting.
- From project initiation to full production deployment
- 20 weeks
- From project initiation to full production deployment
- Students covered across integrated systems
- 125,000+
- Students covered across integrated systems
- Dashboard tiers from executive to student success advisor
- 4 personas
- Dashboard tiers from executive to student success advisor
Five hours a week to thirty minutes, and 150% customer reach
Mid-market distribution and logistics operator
An operations team was manually re-typing vendor inventory emails into spreadsheets, and a sales team could only reach about 60% of its customer base each week because every outreach email had to be assembled by hand. Two connected builds removed both constraints and produced a competitive pricing database as a by-product.
- Weekly inventory processing, per person
- 5 hrs → 30 min
- Weekly inventory processing, per person
- Weekly customer reach from sales outreach
- 60% → 150%
- Weekly customer reach from sales outreach
- Incremental sales per week
- $2K–$5K
- Incremental sales per week
Benchmarking and extending a global insurer’s private AI platform
Global commercial insurer
The insurer had built a private alternative to public chat assistants, hosted on isolated infrastructure so that no company data left the boundary or reached an external training set. The engagement benchmarked the models behind it, then defined the agent roadmap that moved it from a general assistant toward specific insurance workflows.
- Benchmarked across short-form, long-form, coherence, and accuracy
- 4 task classes
- Benchmarked across short-form, long-form, coherence, and accuracy
- Scoped for loss intake, policy submission, and data transformation
- 3 agents
- Scoped for loss intake, policy submission, and data transformation
- Prototype extending retrieval coverage beyond the incumbent approach
- Lite-GraphRAG
- Prototype extending retrieval coverage beyond the incumbent approach
Keeping AI characters in character across long, unscripted conversations
AI product startup
The product lets people rehearse difficult conversations with a video avatar that stays in role and then drops character to coach them. Holding that persona steady across long unscripted exchanges is the hard engineering problem, and proving it holds is harder still. The work produced the prompt architecture, the evaluation harness, and the fine-tuned models that made both tractable.
- Prompt architecture adopted across the engineering team
- Team standard
- Prompt architecture adopted across the engineering team
- Benchmarked on fidelity, realism, adherence, and feedback quality
- 3 models
- Benchmarked on fidelity, realism, adherence, and feedback quality
- Fine-tuned small models cut token spend and improved hardware performance
- Lower cost
- Fine-tuned small models cut token spend and improved hardware performance
Why clients work with us.
Anyone considering a fractional leader is weighing three things at once, usually without saying so directly. Every case study above is written to answer all three.
We understand the business
Clients want a specific problem solved rather than an AI platform. Every engagement here began with a business question.
We are technically strong
The mathematics of predictive models as much as the technology running them. Knowing why a method fits is what makes a recommendation survive production.
We lead large programs
Hands-on enough to solve the hard technical problem, and senior enough to sequence a multi-phase program and carry the organization through it.
Business empathy, technical depth, and program leadership together are what make a fractional engagement work. Any one of them on its own produces advice that somebody else then has to translate, sequence, and defend.
Detail available on request
Confidentiality limits what can be published, and rather more can be discussed directly than written down here. If one of these resembles the problem in front of you, a call is the fastest way to get to the specifics that matter for your situation.
Start with a conversation.
Half an hour is usually enough to establish whether fractional leadership suits where your business currently sits, and you will get a straight answer either way. There is no obligation attached, and no proposal unless you ask for one.