Question 1

Where does our data go? Is our AI use private?

Accepted Answer

Your data stays in your AWS account. Bedrock requests run via VPC interface endpoints (PrivateLink), so prompts and responses don't traverse the public internet. Vector stores are KMS-encrypted Aurora pgvector inside your VPC, with IAM scoped per service.

For regulated workloads we deploy on a [HIPAA-eligible foundation](/industries/healthcare) and select BAA-eligible Bedrock models only.

Question 2

How long until we have something in production?

Accepted Answer

Most builds run 4 to 8 weeks from kickoff to a working prototype, plus 2 to 4 weeks to harden for production. Single-use-case pilots have shipped in under a month.

The biggest variable is data readiness, that's what the assess phase clarifies. We surface trade-offs before you commit, so the timeline you sign up for is the one we ship to.

Question 3

How much does it cost to run AI on AWS?

Accepted Answer

Less than most teams expect at pilot scale. Bedrock charges per token, so a modest RAG runs in the low hundreds per month and scales with usage, not infrastructure. The model you pick (Claude Haiku, Sonnet, or Opus) drives cost more than the architecture does.

We architect to your budget up front and add cost dashboards so spend doesn't drift. Infrastructure-as-Code makes it easy to tune as you scale.

Question 4

Can we start small and scale up, or do we need everything built upfront?

Accepted Answer

Start small. Most clients launch with a focused use case, test, and then expand.

This approach reduces risk and proves ROI before any major investment. Adding new models, scaling capacity, or implementing additional features can happen fast, so you're never locked into the initial scope.

Question 5

Do we need ML engineers or can our existing developers handle this?

Accepted Answer

Your existing engineering team can handle most AI implementations with AWS Bedrock, it's API-driven like any other service.

You only need ML expertise for custom model training or advanced fine-tuning. We work directly with your developers to implement the AI pipeline, then provide training and documentation so your team can maintain and extend it confidently.

Question 6

How does safeINIT charge for AI work?

Accepted Answer

AI work usually runs as a [project-based engagement](/collaboration/project), scoped fixed-fee across assess, prototype, and production. Once you're live, an INIT retainer keeps the system tuned and your team supported.

AWS spend is separate, billed directly by AWS, we don't charge a percentage of it. If you already run on [safeZONE](/safezone), the AI build slots on top, the foundation is there.

Production AI on AWS, deployed in weeks, not months

You’re ready to build with AI, but is your platform ready?

3 steps to a live AI system on AWS

What we build

RAG over your documents

Contact-center AI

Bedrock-powered assistants

ML forecasting and analytics

Frequently asked questions

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