Customer Stories
Measurable outcomes from production AI engagements.
91% accuracy validated in 2 weeks.
Two-week lab: a defect-detection dashboard prototype plus a model-accuracy spike on the top three defect types. Validated 91% accuracy with a $1.2M/yr savings projection; greenlit for build.
99.7% sync across 800K+ records.
ERP, MES, and QMS integrated for real-time production data with sub-500ms latency. Dual-run reconciliation before switchover; zero production impact during cutover.
2.4M transactions/day upgraded. Zero downtime.
Rails 6.1 to 7.1 for a fintech API. p95 1.8s → 680ms, 18 CVEs eliminated, PCI-DSS compliance maintained through the dual-boot migration.
Time-to-value: 7 days → 90 minutes.
Pod built self-serve onboarding for a SaaS platform: in-app tours, feature flags, and analytics instrumentation, shipped on a steady sprint cadence.
Cart abandonment ↓42%.
A dedicated pod owned checkout optimization and inventory sync: A/B tested flows, a real-time inventory API, and six payment gateway integrations shipped in 8 weeks.
Planning cycle: 3 days → 4 hours.
6-agent supply-chain planner: demand forecasting, inventory optimization, supplier coordination, logistics routing, risk assessment. Expedited shipping ↓38%. Executives get real-time visibility into agent decision logic.
Prior authorization: 4.2 days → 6 hours.
5-agent clinical workflow: intake, clinical review, policy check, approval, notification. PII redaction and HIPAA audit trails enforced at the orchestration layer. On-prem deployment with SSO for 1,200 clinicians.
Fraud screening: $0.18 → $0.04 per transaction.
8-agent detection pipeline for a global bank: transaction analysis, risk scoring, KYC validation, document verification, decision engine. False positives ↓41% via model routing (fast models for scoring, frontier models for complex review). Full audit trace for FINRA and FinCEN.
$42M in cargo saved. 250K shipments monitored.
Real-time IoT monitoring across 340 pharma facilities, 3.5M telemetry pings/hour. Excursion discovery went from 6-8 hours to 4.2 minutes with a 15-minute intervention window; 8,400 critical excursions caught in year one. FDA 21 CFR Part 11 compliant.
$14.7M in fraud caught. False positives ↓85%.
TensorFlow anomaly detection across 12M receipts for a Fortune 200 financial services company. Fraud caught rose $2.1M → $14.7M/yr; false positives 28% → 4.2%; 60K audit hours cut to 8.4K. Payback in 2.8 months.
$18M annual fuel savings. 450 vessels.
PyTorch GNN re-optimizes routes every 6 hours across Atlantic, Pacific, and Indian Ocean lanes (previously quarterly, by hand). ETA accuracy ±5 days → ±12 hours; port congestion delays 14% → 3.2%. Still running 6 years later.
450K LOC migrated. Zero downtime.
Rails 3 to 7 for a top-10 telehealth provider: 450K LOC, 2.5M patient records, 14 months, and 15,000 daily clinical users never noticed. Test coverage 35% → 85% caught 847 regressions; passed SOC 2 Type II during the migration.
Critical CVEs → 0 before go-live
OWASP sweep, SBOM, access logging; bulk video/audio redaction in VISTA. Redaction time per request 3.1h → 1.2h; records turnaround 10 days → 3 days; 14 WCAG 2.1 issues → 0.
Infra spend ↓29% YoY
Batched edge inference on Jetson cut cloud GPU hours 38% (4,800 → 2,976/yr); cold storage tiering cut the footprint 35% (180TB → 117TB). Detection accuracy held within 1.5 points.
p95 latency ↓46% in 6 weeks
p95 840ms → 450ms. Re-platformed hot paths, added tracing, tuned indices. No feature freeze. Infra spend down 21% ($42k → $33k/mo); 7 critical CVEs closed before go-live.