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— · AI

GenAI on AWS & GCP.

Production GenAI on the cloud platforms you already trust — Bedrock, Vertex AI, SageMaker, Anthropic on AWS — with guardrails, cost controls, and the IAM you already have.

GenAI on AWS & GCP

Production-grade Generative AI deployed inside the cloud accounts you already trust — with the IAM, networking, and compliance posture you already have.

For teams who want to ship AI features without giving up the security and operational discipline that took years to build.

AWS Bedrock

What we build

  • Bedrock production rollouts with provisioned throughput strategy
  • Bedrock Knowledge Bases with hybrid retrieval and re-ranking
  • Bedrock Guardrails for content, PII, and prompt injection
  • Bedrock Agents with tool use and orchestration
  • VPC endpoint and PrivateLink configurations
  • Multi-account model access via AWS Organizations
  • Cost allocation by team using cost categories
  • Cross-region failover and quota management

Anthropic on AWS

Native Anthropic Claude deployments on Bedrock — including Claude Sonnet, Opus, and Haiku — with the same IAM, audit, and networking your team already runs.

Google Cloud Vertex AI

What we build

  • Vertex AI Model Garden deployments
  • Vertex AI Agent Builder for grounded enterprise agents
  • Vertex AI Search for retrieval over your corpus
  • Gemini and Anthropic on Vertex
  • Custom training and fine-tuning pipelines
  • Pipelines, Endpoints, and Model Registry setup
  • IAM and VPC-SC for regulated data

Amazon SageMaker

For teams that need to fine-tune, train, or self-host:

  • SageMaker JumpStart and Model Garden
  • Inference endpoints (real-time, async, serverless)
  • Fine-tuning jobs and pipelines
  • SageMaker Studio environments for data science teams
  • Custom training on GPU instances

Why teams pick this path

  • Data residency — no traffic to third-party SaaS APIs
  • Compliance — SOC 2, HIPAA, PCI, GDPR posture you already passed
  • Cost predictability — provisioned throughput contracts you control
  • Operational consistency — same observability, alerting, and on-call rotations as the rest of your stack

How we engage

  1. Cloud assessment — current AWS or GCP posture, blockers, IAM model
  2. Pattern selection — Bedrock vs Vertex vs SageMaker vs hybrid, with cost model
  3. Reference implementation — one route in production, fully observable
  4. Rollout — additional routes, guardrails, FinOps, evals
  5. Operate — ongoing cost optimization and model lifecycle

Contact us to scope a GenAI rollout on AWS or GCP.

— Outcomes

What this engagement delivers.

01
Stay in your cloud, your IAM, your VPC
No data leaves your AWS or GCP boundary. Use the IAM, KMS, and audit you already have. Compliance teams stop blocking AI rollouts.
02
Production patterns, not blog-post demos
Provisioned throughput where it matters, on-demand where it doesn't. Guardrails wired up. Cost dashboards by team. Real rollouts.
03
Model choice without lock-in
Anthropic, Meta, Mistral, Cohere, Amazon, Google — pick the right model per route. Swap them without rewriting your app.
04
We do the boring infra work
VPC endpoints, cross-account access, KB ingestion pipelines, Lambda glue, rate-limit handling, retry logic, eval CI. The unglamorous stuff that makes it work.

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

Book free consultation See where we've shipped