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Customer Library / Volume 04

Production AI infrastructure, written up by the engineers who shipped it.

Every case study below was authored by the deployment engineer and reviewed by the customer's technical lead. We publish the architecture, the measured delta, and the post-mortem — because the gap between a 14-month rollout and a 9-week one is rarely magic, almost always process. Across 612 enterprise customers, VLVC's platform cuts the median team's time-to-production from 14 months to under 9 weeks.

14mo
Industry median time-to-production
8.7wks
VLVC client average, 180+ deployments
73%
MLOps incident reduction in first 90 days
99.992%
Inference uptime, trailing 12 months
Read the Architecture Brief → Browse by Industry

Co-authored with the customer's platform team · Reviewed under VLVC's public customer-disclosure protocol · No NDAs required to publish.

// Filter by Vertical

Four industries. One platform. Pick the peer closest to your stack.

VLVC's deployment record spans regulated incumbents and high-velocity AI-native teams — the platform is identical, but the constraints differ. Filter by vertical to find an architecture brief from an organization facing the same regulators, latency budgets, and audit posture you do.

  • 01
    Financial Services
    3 of Fortune 10 Regulated inference p99 < 80ms

    Risk, fraud, and treasury models running on audited inference stacks with model-risk-management controls.

    Read the case study →
  • 02
    Healthcare & Life Sciences
    HIPAA · BAA-ready PHI-adjacent 22 research institutions

    Clinical NLP, imaging triage, and translational-research pipelines operating under HIPAA without throughput penalty.

    Read the case study →
  • 03
    Public Sector
    FedRAMP Moderate DOE partner Airlock deployments

    Federally funded research deployments, citizen-service copilots, and audit-ready inference for civilian agencies.

    Read the case study →
  • 04
    AI-Native
    14 unicorns Series B+ Product-grade

    Model-as-a-product teams shipping LLM and CV features where downtime, drift, and cost-per-1k-tokens are P0 metrics.

    Read the case study →
Case Study 01 Financial Services

A top-5 U.S. bank cut fraud-model drift from 18 days to 47 minutes.

The bank's previous pipeline issued quarterly model refreshes and detected drift an average of 18 days after it began degrading live approvals. VLVC replaced the brittle in-house stack with an audited inference mesh — and shrank the entire detect-and-rollback loop to under one hour.

Problem

The bank operated 47 fraud-scoring models in production. Their homegrown stack had no canonical feature store, no drift instrumentation, and no model-risk-management audit trail. Three of the models had drifted undetected for over 90 days — a regulator-citation risk under SR 11-7. Mean time to detect (MTTD) was 18 days; mean time to rollback was another 11.

Architecture

VLVC deployed the Orchestrator mesh across the bank's three U.S. colocation regions, fronted by a feature store with deterministic replay. Models were wrapped in VLVC's MRM-compliant audit envelopes, and a continuous-drift controller compared live prediction distributions against a hashed baseline every 60 seconds. All inference calls landed in a tamper-evident ledger aligned to the bank's existing model-risk repository.

Measured Delta
  • 47 min drift-detection latency, down from 18 days
  • 9.4 sec median rollback, down from 11 days of manual QA
  • 0 regulator findings in the 12 months following deployment
  • 31% reduction in false-positive declines on the high-risk segment
Read the Architecture Brief → Next: Healthcare ↓
Case Study 02 Healthcare & Life Sciences

A 22-hospital system shipped discharge-summary triage to production in 8 weeks — without losing PHI traceability.

The health system's clinical informatics team needed to triage 14,000 discharge summaries per day to flag readmission risk. The compliance review board required a fully auditable PHI boundary before any model touched the EHR — and the throughput had to hold under load.

Problem

The first internal pilot had taken 11 months to reach a single hospital. Every model update required a manual HIPAA review, every inference call lacked a tamper-evident log, and the on-call team had no way to answer "what did the model see at 03:14 last Tuesday?" — a question their auditor asked them twice a quarter.

Architecture

VLVC inserted a PHI bridge between the EHR and the inference mesh, terminating all identifiers at the boundary and replacing them with deterministic study-scoped tokens. Inference ran inside VLVC's HIPAA-scoped VPC with VLVC's BAA in force; every token-to-record mapping landed in a BAA-audited, append-only ledger that the system's compliance team could query directly. Drift and bias controllers ran on hashed cohort definitions.

Measured Delta
  • 8 weeks wall-clock from kickoff to 22-hospital rollout
  • 14,200 summaries processed per day at p95 < 1.8s
  • 0 PHI findings in the post-deployment HIPAA audit
  • 19% reduction in 30-day readmissions within the pilot cohort
Read the Architecture Brief → Next: Public Sector & AI-Native ↓
Case Studies 03 & 04

A federally funded lab and a Series B+ AI-native product — same platform, opposite ends of the stack.

We pair the remaining two briefs because they show the breadth of VLVC's deployment surface: a research environment operating under air-gapped, audit-grade constraints, and a customer-facing product team shipping LLM features where downtime is a revenue event.

Case Study 03 Public Sector

A U.S. Department of Energy national lab unified 11 research clusters under one auditable inference plane.

4,800 H100-equivalent GPUs under one Orchestrator
Problem

Eleven research clusters, four funding programs, three classification levels. Researchers waited an average of 17 days for GPU allocation, and every audit cycle meant re-proving that no model crossed a classification boundary.

Architecture

VLVC deployed a federated Orchestrator with classification-aware scheduling and an air-gapped control plane. Job manifests were cryptographically scoped to a clearance tier; no researcher could submit work above their cohort's ceiling. The lab's existing Slurm queues were left in place as workers.

Measured Delta
  • 17 days → 2.1 hours median GPU-allocation wait
  • 0 classification-boundary findings in the 2024 DOE audit
  • 2.3× throughput on the lab's flagship climate-foundation pretrain
Read the Architecture Brief → DOE · 2024 AI Infrastructure Partner of the Year
Case Study 04 AI-Native

A Series C document-AI startup held cost-per-1k-tokens flat while tripling inference volume.

−42% cost-per-1k-tokens at 3.1× volume
Problem

The customer's homegrown autoscaler spun up cold-start replicas on every traffic spike, and their unit economics broke once they crossed a million daily active users. Latency p99 was over 4 seconds on Mondays.

Architecture

VLVC's warm-pool scheduler pre-allocated GPU capacity from the customer's reserved H100 tranche, and a token-level cache collapsed redundant re-encodings of long documents. The team's existing model artifacts moved into VLVC's model registry with no retraining.

Measured Delta
  • p99 4.1s → 620ms under peak load
  • −42% cost-per-1k-tokens, sustained for two quarters
  • 3.1× daily inference volume on the same GPU budget
Read the Architecture Brief → Series C · 1.2M DAU at deployment
// By the numbers

The architecture claims, summarized so you can quote them internally.

Four metrics, drawn from VLVC's published 2024 customer disclosures and the trailing-twelve-month platform telemetry. Every number below is independently verified by the customer's platform team before publication.

99.992%
Inference uptime

Across 14 global regions, trailing twelve months — measured at the customer load balancer, not the cluster.

9.4T
Inference requests in 2024

Cumulative inference volume processed across all VLVC customer deployments during calendar year 2024.

−73%
MLOps incident rate

Average reduction in customer MLOps incident rate within the first 90 days, measured across 180+ deployments since 2019.

8.7wks
Time-to-production

Average wall-clock from kickoff to first model in production — vs. an industry median of 14 months, per VLVC's 2024 deployment census.

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