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— · AI

AI Infrastructure & MLOps.

GPU clusters, model serving, vector databases, evaluation pipelines, and LLM observability — the platform layer that makes AI systems reliable in production.

AI Infrastructure & MLOps

The platform layer that makes AI systems reliable, observable, and affordable in production.

If your team is shipping LLM features but inference costs are unpredictable, latency is creeping up, and nobody can answer “did the last prompt change make things worse?” — this is the service.

What we deliver

GPU & model serving

  • GPU node pool design on EKS, GKE, or self-managed
  • vLLM, TGI, NVIDIA Triton deployments
  • Spot, on-demand, and reserved capacity strategy
  • Model registry and versioning
  • Multi-model routing and A/B serving

Hosted AI platforms

  • AWS Bedrock production patterns (provisioned throughput, guardrails, KB)
  • Vertex AI deployments and pipeline tooling
  • Anthropic on AWS rollouts
  • Azure OpenAI for regulated workloads
  • Cost allocation and chargeback across teams

Vector databases & retrieval

  • pgvector (Postgres) for early-stage and mid-scale
  • Pinecone, Weaviate, Qdrant for high-scale
  • Hybrid retrieval (BM25 + dense + reranking)
  • Index design, sharding, and tiering
  • Embedding model selection and migration

LLM observability

  • Tracing with LangSmith, Langfuse, Helicone, or OpenTelemetry GenAI
  • Token, cost, and latency dashboards
  • Prompt and model version tracking
  • Regression detection on quality metrics
  • Per-tenant cost attribution

Evaluation pipelines

  • Deterministic eval harnesses in CI
  • LLM-as-judge pipelines with human spot-checks
  • Ragas and custom retrieval evals
  • Regression gates on PR merges
  • Continuous eval against shadow traffic

Guardrails & safety

  • PII detection and redaction
  • Prompt-injection defenses
  • Output validation and schema enforcement
  • Content moderation pipelines
  • Audit logging for regulated industries

Outcomes we measure

  • p50 and p95 latency, by route
  • Cost per 1M tokens, by team and tenant
  • Eval pass rate per release
  • Incident MTTR for AI-specific failures
  • Model version coverage in CI

Built on the cloud platforms you already run

We don’t replace your AWS or GCP — we extend them. Bedrock and Vertex are first-class. So is bringing your own GPU cluster on EKS or GKE if your usage justifies it.

Contact us to scope an AI platform engagement.

— Outcomes

What this engagement delivers.

01
GPU infrastructure that doesn't melt budgets
Right-size GPU pools, autoscale model servers, cache aggressively, and route traffic across providers. We've cut inference bills by 60–80% on real workloads.
02
Model serving you can debug
vLLM, TGI, or SageMaker — picked for your traffic shape, instrumented end-to-end, with traces that explain every token.
03
Vector databases that scale
From pgvector at 10M chunks to Pinecone/Weaviate at billions — index design, sharding, hybrid retrieval, cost-per-query optimization.
04
Evals as a first-class citizen
Deterministic eval harnesses in CI, LLM-as-judge pipelines, regression detection on prompt and model changes — so AI quality is a metric, not a vibe.

Ready to put this in motion?
A 30-minute call sets the direction.

Book free consultation See where we've shipped