Case Studies
Real systems running in production. Real data. Measurable outcomes across defense, federal, healthcare, and enterprise.
Product Clustering for Pharma Pricing
A brittle, rules-based clustering engine could not keep pace with 100K+ products. Pricing teams spent weeks manually correcting misclassifications.
LLM-based clustering system that understands product relationships semantically, replacing rigid rules with adaptive intelligence. Integrated directly into the pricing workflow.
Eliminated manual reclassification cycles and delivered consistent clustering at scale across the full product catalog.
Enterprise Analytics for 50,000+ Users
Program leadership relied on weeks of manual Excel reporting to understand operational status across a massive enterprise system.
Real-time dashboards and automated data integration across the full program. Reporting that used to take weeks now updates continuously.
Shifted decision-making from stale spreadsheets to live operational intelligence accessible to 50,000+ users.
Document Intelligence System
Analysts dug through hundreds of thousands of documents manually. Finding a specific answer could take hours or days.
Natural-language search with sourced answers across 800K+ documents. Every response is traceable to its source. No hallucinated citations.
Analysts get sourced answers in seconds instead of hours. Review cycles compressed by 70% across the agency.
Predictive Anomaly Detection
Operations teams were reactive. Issues were caught after they caused impact, often too late to mitigate effectively.
ML models trained on historical time-series data that flag deviations hours before they escalate. Alerts feed directly into operator dashboards.
Moved from reactive firefighting to predictive operations. 60% fewer incidents, with hours of lead time on the ones that remain.
AI-Powered Provider Matching
A rules engine attempted to match providers to segments across millions of records. Accuracy degraded as edge cases accumulated.
ML-driven segmentation model trained on historical match data, handling the long tail of edge cases that rules could not reach. 99%+ accuracy at scale.
Replaced a failing rules engine with an adaptive model that maintains 99%+ accuracy across millions of provider records.
Embedded AI for Industrial Measurement
Measurement systems depended on cloud connectivity in environments where connectivity was unreliable or nonexistent.
Computer vision models running directly on embedded hardware. Fully on-device inference with zero cloud dependencies and real-time processing.
Delivered 99% measurement accuracy on embedded hardware in disconnected industrial environments. No cloud required.
AI Agent System for Operations
Operators manually processed every workflow step. Throughput was capped by headcount, and review backlogs grew constantly.
AI agents that execute multi-step operational workflows autonomously. Self-correcting on failure, with human-in-the-loop escalation for exceptions only.
Same team now handles 10x the volume. 85% of routine decisions are handled by agents, freeing operators for high-judgment work.
Data Pipeline Rebuild for ML Readiness
Five disconnected data sources with no unified schema. Manual data prep consumed 90% of the analytics team's time.
Unified ETL pipeline that collapses 5 sources into 1 clean, ML-ready dataset. Enabled demand forecasting deployment in under 60 days.
Unified data foundation enabled the company to ship its first ML model to production in under 60 days.
Your system could be next.
Every case study above started with a 30-minute scoping call. No pitch deck. Just a direct conversation about the problem and whether we can solve it.
We take on 3 engagements per quarter. Limited capacity means senior-level attention on every project.