Services

Production Infrastructure & Architecture

Built a prototype on Lovable, v0, or a notebook? We put real infrastructure under it.

You proved the idea. Now it has to survive production. We design and build the infrastructure your AI product runs on — secure, observable, automated, and ready for real traffic.

• Architecture for AI workloads — reliability, security, and scale from day one
• AWS-native: Bedrock, SageMaker, ECS/EKS — with VPC, IAM, and security posture done right
• Infrastructure as Code (Terraform/CDK) and automated deployments
• Cost-aware hosting for the app, data, and vector stores around your models
• Build-vs-buy guidance, and architectures that keep you portable

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Unit Economics & Cost Attribution

Know what your AI costs — down to the feature and the user.

We instrument your AI spend across every feature, user, and workflow, then re-architect until the economics work. We start from billing data you already produce, so the first results come without new instrumentation.

• Cost per user, per feature, per workflow — tied to product and revenue
• Token attribution from real billing data — AWS CUR, Bedrock logs, provider usage APIs
• Model right-sizing — matching capability and price to each task
• Inference-path optimization — prompt caching, routing, batching, and leaner context
• Budget guardrails and spend anomaly alerts

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AI Observability

See what your AI is actually doing.

We build observability for the AI path itself — every prompt, retrieval, tool call, and response — so you can spot quality drift, latency, and cost as they happen.

• Tracing across the full AI path — prompts, retrievals, tool calls, responses
• Quality and drift monitoring — catch regressions early
• Cost and latency per request and per feature
• Abuse and anomaly detection — runaway sessions, prompt injection, denial-of-wallet
• Built on OpenTelemetry, added alongside your stack

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Retrieval & Grounding

AI answers grounded in your own data.

We build the retrieval layer — vector and knowledge-graph RAG, plus the data pipelines behind them — that makes your AI output consistent, explainable, and production-ready.

• Retrieval architecture — vector RAG, GraphRAG, or both, matched to your data
• Data and ingestion pipelines that keep your knowledge fresh
• Knowledge graph and ontology design for explainable answers
• Production graph and vector databases — Amazon Neptune, Neo4j, or your stack
• Wired into your application alongside what you already run

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AI Control Plane

One place to route, govern, and control your AI traffic.

We deploy a control plane between your application and the model providers, so routing, caching, spend control, and logging live in one place you own.

• Self-hosted in your AWS — your keys, your data, your infrastructure
• Routing and automatic fallback across Bedrock, Anthropic, OpenAI, and more
• Prompt and response caching, with per-user rate limits and budgets
• Guardrails — PII redaction, input limits, content filtering
• OpenAI-compatible — integrate by pointing to one endpoint