Type something to search...
to navigateto selectESC to close

Services

We deploy AI workloads on AWS with production-ready, cost-visible, and maintainable infrastructure.

AWS Infrastructure for AI Workloads

You’re building with AI — we make sure the foundation is solid.

We design and deploy AWS environments purpose-built for AI workloads — with the right compute strategy, networking, IAM posture, and automation from day one.

  • Architecture review and greenfield design for AI workloads
  • AWS Bedrock and SageMaker deployment and configuration
  • VPC, networking, and security posture aligned to production requirements
  • Cost-aware compute selection — EC2, ECS, EKS, Lambda — matched to your workload
  • Infrastructure as Code (Terraform/CDK) so nothing lives only in a console

AI FinOps

What is your AI actually costing you per user, per feature, per workflow?

Most companies deploying AI have no idea what it costs at a unit level. Aggregate AWS bills tell you nothing useful. We build the visibility layer and the optimization strategy on top of your AI deployment.

  • Token cost attribution by feature, user segment, or workflow
  • AWS CUR analysis and Bedrock cost data instrumentation
  • Model right-sizing — matching model capability to task cost
  • Spend anomaly detection and budget guardrails
  • FinOps reporting that ties AI spend to product outcomes

Implementation & Integration

We write code for IaC (terraform), devops automations, and backend

Our engagements go from architecture through deployment — working alongside your team until the system is live, monitored, and understood by the people who will maintain it. Or with the ongoing maintainance option with us.

  • Hands-on build alongside your engineering team
  • Integration with existing AWS accounts, CI/CD, and observability stacks
  • Deployment to production with runbooks and operational handover
  • Production-ready from day one with proper security posture for AI use cases

Knowledge Graphs for AI

Knowledge graphs make LLMs reliable, repeatable, and deterministic.

We design, build, and deploy knowledge graph infrastructure on AWS, integrating with your existing stack and data sources.

  • Knowledge graph schema design and domain ontology — entity types, relationship modeling
  • Graph database deployment on AWS — Amazon Neptune or Neo4j on EC2/ECS
  • Entity extraction pipelines — Bedrock-powered ingestion from unstructured documents into the graph
  • GraphRAG implementation — combining graph traversal with LLM generation for richer, relationship-aware answers
  • Integration with existing Bedrock agents, knowledge bases, and vector stores
  • Query layer design — patterns your application can call in production, not just ad-hoc exploration

At the end of an engagement you’ll have a production-deployed graph database populated with your data, an extraction pipeline that keeps it current, and a retrieval layer wired into your workflow.

Not sure which service fits? Book a 30-minute call — we’ll tell you honestly what would help and what wouldn’t.