Turn promising AI ideas into secure, measurable applications on AWS.
We design and build generative-AI systems with Amazon Bedrock, your enterprise data, access controls, evaluation, observability, and cost limits built in from the beginning — so pilots can become production without a rewrite.
The problem
AI initiatives stall between slide decks and shadow IT. Teams paste sensitive data into public tools, run unbounded API spend, or ship a demo that cannot meet security, evaluation, or cost requirements.
Pilot projects that never reach production
Unclear data boundaries and access control
Inference costs that finance cannot forecast
No evaluation loop — nobody knows if the system is actually good
Who this is for
Product and professional-services teams ready to pilot internal assistants or document workflows
Organizations that need RAG over proprietary data with real access controls
Leaders who want a production path — security, cost, and observability — not only a demo
Regulated or security-conscious teams that cannot use public consumer AI tools with company data
Outcomes we aim for
A use case narrow enough to prove value quickly
A pilot with encryption, identity, logging, and cost caps
Evaluation criteria so “good enough” is measurable
A documented path from pilot to production operations
What we cover
Use-case selection and success metrics
Amazon Bedrock pilots and model selection
RAG and knowledge bases over S3, OpenSearch, or Kendra
Access control, encryption, and audit logging
Evaluation, observability, and cost controls
SageMaker and MLOps baselines when custom models are warranted
How we deliver
Select the use case
Pick a problem with clear users, data sources, and success metrics. Kill vague “AI strategy” before it burns budget.
Pilot
Build a constrained Bedrock or RAG system with security and cost limits from day one.
Evaluate
Measure quality, latency, cost, and risk. Decide go / iterate / stop with evidence.
Harden & operate
Productionize with monitoring, access reviews, and an operating model your team can own.
What you receive
Use-case brief and success criteria
Working pilot architecture on AWS
Security and cost guardrails documentation
Evaluation notes and recommended next steps
Production readiness checklist when you proceed
Example engagement shape
Bedrock / RAG pilot
Short fixed-scope pilot: one use case, one corpus, identity and cost caps from day one, and explicit evaluation criteria before any production push.
Use-case brief and success metrics
Working pilot architecture on AWS
Security and cost guardrails documentation
Go / iterate / stop recommendation after evaluation
Production discipline: identity, encryption, logging, and cost from the start
No hype cycle — we will recommend not building when the use case is weak
Same engineer-led model as our infrastructure work
Clear handoff so your team is not dependent on a demo environment
Engagement options
Most AI work starts as a short fixed-scope pilot. Production hardening and ongoing MLOps support follow only when the pilot earns them.
AI use-case workshop — choose what to build and how to measure it
Fixed-scope Bedrock / RAG pilot with security and cost caps
Production hardening and operational handoff
Frequently asked questions
Can you build a private ChatGPT-style tool on AWS?
Yes. We usually start with Amazon Bedrock and RAG over your documents in S3 or OpenSearch, with encryption, access policies, and cost caps leadership can approve.
How do you control generative AI costs on AWS?
Inference limits, models sized to the job, usage monitoring, and architectures finance can forecast — not open-ended API spend.
Do you only do pilots or production deployments?
Both. We prefer a short pilot to prove value, then a documented path to production with security and MLOps baselines your team can operate.
Will our data be used to train public models?
We design architectures so your data stays in your AWS environment under your access policies. Model-provider terms are reviewed as part of the design — we do not treat that as fine print.
Have an AI idea that needs a secure path to production?
