reduction in root cause identification time (21 days to under 4 hours)
F1-score improvement (58.55% to 88.55%) via DTS synthetic data augmentation
time-to-deploy: 4 weeks to 1 day on AI model production cycle
15+ enterprise clients across finance, telecom, healthcare, defense, and global sectors.
Gartner
Naver Cloud
SK Telecom
Kyobo
ROK Army
ROK Air Force
EUMC
Deutsche Telekom
Claroty
Korea Heritage Service
Ministry of Data and StatisticsGartnerNaver CloudSK TelecomKyoboROK ArmyROK Air ForceEUMCDeutsche TelekomClarotyKorea Heritage ServiceMinistry of Data and StatisticsEach record follows the same format: Before, After, What Changed, Reproduce.
Before: Schema change in upstream data caused silent model degradation. Root cause took 21 days to identify. After: Release State detected the schema diff at ingestion — issue flagged in <1 run, root cause from 21 days to under 4 hours.
Before: Preprocessing update produced inconsistent scores. No way to trace which version caused drift. After: Run Binding linked every score to its exact Release State. Rollback completed in <2 hours with 100% score distribution match.
Before: 3 rare defect classes underrepresented — model missed edge cases in production. After: DTS generated DP-safe synthetic samples for all 3 classes. Coverage gap closed, defect detection recall improved.
Before: Validation pipeline stalled — real patient records inaccessible due to HIPAA constraints. After: DTS generated differential-privacy synthetic records matching real distributions. Validation unblocked, compliance passed.
Before: Claims documents with PII passed directly to external LLM API — compliance blocked the workflow. After: LLM Capsule intercepted and anonymized all PII. Zero PII fields reached the API, output usability preserved.
Before: Scores degraded after infrastructure upgrade — runtime parameters unrecorded, reproduction impossible. After: Run Binding captured every runtime parameter. Pre-upgrade state re-run in <3 hours, exact environment reproduced.
Every SynTitan run produces structured artifacts that make execution conditions inspectable, comparable, and reproducible. These are the operational records teams use for incident response and regression verification.
When output behavior changes between runs, SynTitan diffs the two Release States to surface exactly which execution condition changed.
These are the business outcomes the operational evidence demonstrates — not theoretical capabilities, but changes documented in production deployments.
Root cause identification time. Schema change detected at ingestion via Release State. Incident resolved before next training run.
Model accuracy improvement after DTS fixed class imbalance. Rare defect class augmented with privacy-safe synthetic data. Deploy time cut from 4 weeks to 1 day.
PII detection accuracy in enterprise prompts. Compliance-blocked LLM projects unblocked without sacrificing data usability or audit requirements.
Run Binding enabled stable-state re-execution after preprocessing drift caused inference inconsistency. Prior Release State re-run confirmed. Score distribution matched baseline.
DP-safe synthetic records generated to replace retention-deleted data. F1 churn model reached 0.92. Zero real customer data accessed or exported. Full regulatory compliance.
Each case reduced deployment risk, incident investigation time, or AI adoption blockers — without replacing existing data infrastructure.
Reproducible AI execution means that any past AI run can be re-executed under the exact same data, environment, and pipeline conditions — returning the same result. SynTitan achieves this through Release State and Run Binding, which lock execution conditions at every run. When something breaks in production, you don’t debug blind — you diff the states and reproduce the last known-good run.
Every production AI failure has a root cause. These cases show how to find it fast, fix it correctly, and prevent recurrence. The same infrastructure is available now.
30-min architecture review. Engineers-first. No sales pitch.