Anthropic Claude & AWS Bedrock
Engineering.
We architect enterprise deployments on Claude via AWS Bedrock, giving you frontier model quality with the data residency, VPC isolation, and audit logging your compliance team actually requires.
Frontier Models Behind a Compliance Boundary
The friction enterprises hit with frontier models is almost never model quality. It's getting a security or compliance team to sign off on data leaving their perimeter. Routing Claude through AWS Bedrock inside your existing VPC resolves most of that friction structurally: traffic never crosses the public internet, IAM governs access the same way it governs every other AWS resource, and CloudTrail gives you the audit log your compliance team already expects from every other system.
PrivateLink or VPC endpoint routing so prompts and completions never traverse the public internet, satisfying data-residency requirements without a custom network path.
Bedrock Guardrails configured per use case: content filtering, PII detection, and denied-topic lists tuned to your domain rather than left on generic defaults.
Per-team, per-feature IAM scoping and usage tagging, so token spend and access are attributable and auditable the same way any other AWS cost center is.
A provider-abstraction layer at the application boundary, so a future model swap (Claude version bump, or adding a second provider) doesn't mean rewriting every call site.
What we build
VPC-Isolated Deployment
Routing all model traffic through Bedrock PrivateLink inside your VPC so prompts and completions never traverse the public internet.
Prompt & Guardrail Engineering
Structured prompting, tool-use design, and Bedrock Guardrails (content filtering, PII detection, denied topics) tuned to your domain and risk tolerance.
Cost & Usage Governance
Per-team, per-feature token budgets and IAM-scoped usage dashboards so model spend is predictable, attributable, and alertable like any other cloud cost center.
Prompt Regression Testing
Version-controlled prompt libraries with automated regression tests, so a prompt change or model version bump can't silently degrade production output quality.
What we fix
Compliance teams blocking AI features over data residency and audit-trail concerns
No visibility into which team or feature is driving model spend across the organization
Prompt sprawl with no version control or regression testing across model or prompt changes
Vendor lock-in risk from hardcoding a single provider's SDK throughout the application codebase
No structured process for evaluating a new model version before rolling it out to production
Guardrails left on generic defaults, missing domain-specific risks the business actually cares about
Stop guessing.
Start shipping.
Thirty minutes with a senior engineer. No sales deck, no junior SDRs. Just a technical conversation about what's actually breaking or slowing you down.