AI InfrastructureFintech
512-GPU Training Cluster, Designed and Deployed in 16 Weeks
The challenge. A fast-growing fintech needed dedicated training capacity for fraud-detection and LLM workloads. Their cloud bill was unsustainable, but the team had never built GPU infrastructure at this scale.
What we did. Designed a rail-optimized cluster with a RoCEv2 Ethernet fabric, parallel storage sized against measured checkpoint throughput, and staged deployment with burn-in and NCCL stress tests at every gate.
The result. Acceptance benchmarks confirmed the design before final payment — and the fabric design avoided an InfiniBand premium the workload didn't need.
94%scaling efficiency at 512 GPUs
31%under the original budget estimate
16 wksfrom design sign-off to production
BenchmarkingSaaS
62% Inference Cost Reduction, Same Quality Bar
The challenge. A SaaS company's LLM features were succeeding — and their inference spend was growing 20% month over month, faster than the revenue those features earned.
What we did. Benchmarked their workload across serving stacks (vLLM, TensorRT-LLM), quantization levels and GPU types; built an eval suite to prove quality parity; then implemented model routing that sends easy requests to smaller models.
The result. The eval suite showed no measurable quality regression — and it now gates every model change they ship.
62%lower monthly inference cost
3.4×throughput per GPU after tuning
0quality regressions on the eval suite
Generative AIInsurance
Claims Copilot That Cut Triage Time by 70%
The challenge. Claims handlers at an insurance group spent hours locating clauses across thousands of policy documents. A previous chatbot pilot had failed — it hallucinated coverage terms.
What we did. Built a RAG system with hybrid retrieval and reranking over the policy corpus, added mandatory citations with clause-level links, and — critically — an evaluation harness scoring groundedness on a curated test set before any release.
The result. Handlers verify answers via citations in seconds. The evaluation harness caught two regressions before users ever saw them.
70%faster claims triage
<2%ungrounded answers on the eval set
1,200+daily queries within 3 months
AI StrategyManufacturing
From 40 AI Ideas to a Board-Approved Roadmap in 8 Weeks
The challenge. A manufacturer's business units had proposed dozens of AI initiatives. Leadership had budget for three — and no defensible way to choose.
What we did. Ran use-case discovery workshops across five plants, scored 40+ candidates on data readiness, feasibility and ROI, and built token-level cost models for the finalists, including a build-vs-buy analysis for each.
The result. Seven initiatives made the roadmap with staged budget gates. The first — predictive quality inspection — paid back its build cost within two quarters.
40+use cases assessed and scored
7initiatives on the funded roadmap
8 wksto board approval
MLOpsHealthcare
Fragile AI Platform to 99.9% Uptime in One Quarter
The challenge. A healthcare analytics provider had models in production but every deployment was manual, incidents took days to diagnose, and GPUs sat idle two-thirds of the time.
What we did. An operations-rescue engagement: Kubernetes-based serving with vLLM, CI/CD with eval gates for every model and prompt change, full tracing with per-request cost attribution, and GPU sharing across teams.
The result. Deployments went from a nervous afternoon to a routine pipeline run. The utilization gain deferred a planned hardware purchase entirely.
99.9%platform uptime post-engagement
34→71%GPU utilization
hours→mindeployment time
GovernanceBanking
Sovereign LLM Platform That Cleared Regulatory Review
The challenge. A bank wanted GenAI for customer service and internal knowledge — but regulators and its own risk team blocked any architecture where customer data left its jurisdiction.
What we did. Deployed open-weight models on the bank's own GPU infrastructure, built consent-aware data pipelines with full processing records, red-teamed the platform before launch, and mapped every control to the applicable data-protection clauses.
The result. The platform passed internal audit and regulator review on the first submission. Red teaming surfaced 17 findings — all fixed and retested before go-live.
1stsubmission cleared regulatory review
17red-team findings fixed pre-launch
100%data kept in-jurisdiction