Proof over promises.
What We've Built in Production
Three engagements across financial services, healthcare, and logistics. All production systems. All high-stakes environments. All still running.
CHALLENGE
A major investment firm's risk models were producing alerts that analysts could not explain or act on — leading to ignored warnings and compounding exposure. Over 12 months, 74% of high-priority alerts were dismissed without investigation.
OUTCOME
Risk analysts now act on alerts with full context. False positive rate dropped by 68% in the first 90 days. Zero undocumented alert dismissals in the 6 months following deployment.
APPROACH
Audited the full alert generation pipeline from market data ingestion to model output
Identified that 3 upstream data sources had undocumented schema drift causing systematic noise
Rebuilt the decision engine with explainability-first architecture — every alert includes a reasoning chain
Implemented confidence scoring and tiered escalation to route alerts by analyst workload and signal strength
CHALLENGE
A hospital network's patient routing system was making incorrect triage recommendations due to upstream data quality failures that went undetected for months. Three separate EHR systems were feeding the model with inconsistent patient identifiers and missing vitals.
OUTCOME
Zero critical patient routing incidents in the 8 months since deployment. The clinical IT team now has real-time visibility into data pipeline health. Regulators cleared the system for expanded use across two additional hospital sites.
APPROACH
Mapped the full patient data journey across 3 EHR systems and 14 data transformation steps
Deployed real-time schema validation and null-check monitoring at every ingestion boundary
Built a data quality dashboard giving clinical IT teams visibility into pipeline health for the first time
Implemented automated circuit-breaker logic that halts model inference when data quality drops below threshold
CHALLENGE
A global logistics operator needed demand forecasting across 50M+ SKUs, updated hourly, without system degradation. Their existing batch system took 11 hours to complete a single forecast run — making hourly updates impossible.
OUTCOME
Hourly forecast runs now complete in under 12 minutes across 50M+ SKUs. Forecast accuracy improved from 81% to 91% at production scale. The operator has since expanded the system to cover 3 additional regional markets.
APPROACH
Decomposed the monolithic forecast pipeline into distributed inference workers with horizontal scaling
Implemented a hierarchical forecasting architecture — product family models inform SKU-level models
Rebuilt the data ingestion layer to process streaming updates rather than batch snapshots
Deployed automated performance monitoring with real-time accuracy tracking across SKU tiers
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