Measured outcomes. Live systems. Real revenue impact.
Every engagement below is in production — running pipelines, automated decisions, deployed models. Each result is documented with a before-and-after number, not a success summary.
From pipeline to production
Credit risk model cut false-positive rate by 38%
A Series C fintech was manually reviewing 60% of flagged accounts. We replaced their rule-based system with a gradient-boosted production model, reducing analyst review volume and lifting approval throughput in 11 weeks.
Pipeline rebuild dropped data latency from 14 hours to 22 minutes
A B2B SaaS platform was making pricing decisions on day-old data. We re-architected their ingestion and transformation layer on a modern lakehouse stack, delivering near-real-time feature tables to their downstream models.
Multi-touch attribution model raised paid ROAS by 2.4×
An e-commerce brand was allocating media budget on last-click data. We built a probabilistic attribution model in production, giving their growth team channel-level revenue signals that updated every six hours.
Agentic AI cut support triage time by 71%
A Series B SaaS company's support team spent four hours daily routing and classifying tickets. We deployed an agentic NLP workflow that classifies, enriches, and routes in under 90 seconds — fully automated, no human in the loop.
Numbers from systems in production
14 → 22 min
2.4× avg ROAS lift
38% fewer false positives
71% less manual triage
Paid media return on ad spend improvement via production attribution models.
Median pipeline latency reduction across data engineering engagements.
Risk model precision gain in financial services ML deployment.
Operational hours reclaimed via agentic AI automation across SaaS clients.
Your next engagement starts with a scoped conversation
Tell us your problem domain and current stack. We will scope the work, define the production target, and tell you exactly what we will build.