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AI infrastructure &
agentic systems.

The systems behind real AI products: RAG pipelines, agent crews, the eval harness, the cloud underneath, the local-first stack on the desk. Engineered to ship, not to demo.

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What we build

From the data layer to the eval harness.

An AI product isn't one thing. It's a stack: auth and multi-tenancy at the bottom, a vector store wired into your relational data, retrieval that finds the right context, prompts that survive a model upgrade, agents that know what they're allowed to do, and an eval harness that tells you when any of it broke.

Privian builds the whole thing. The shape is consistent (AWS-native by default, local-first where it makes sense, agentic where the work warrants it), but every engagement is right-sized for the actual problem in front of you.

The capabilities

RAG pipelines. Embeddings, chunking strategy, pgvector indexes, hybrid retrieval, the prompt template that pulls the right context. The surface that earns the bill.

Agentic architectures. MCP-routed tool access, multi-tenant agent crews, governance via OPA, the autonomy ladder (suggest → execute-with-confirm → bounded execution).

Cloud-native AI stacks. The AWS shape we actually start with: API Gateway, Lambda, Cognito, RDS+pgvector, Bedrock, S3, EventBridge, SQS, CloudFront, CloudWatch.

Local-first AI. Apple Silicon + Mac Studio for training, batch, and eval. mflux, Ollama, mlx-lm, Whisper, Piper. The desk-side stack that pays for itself in months.

Prompts as code. Prompts in git, versioned, A/B-tested via feature flags, rolled back when regressions hit. The single most-edited surface with the discipline it deserves.

The eval harness. Test sets, golden examples, regression detection. How you find out it broke before a customer does, and why it has to be a permanent part of the stack.

Typical deliverables

  • End-to-end AI architecture, documented in diagrams + ADRs
  • Working code in your repos, not a slide deck
  • Terraform/CDK for everything we provisioned
  • An eval harness running in CI
  • Prompt versioning + rollback wired into your flag system
  • Operational runbook + handoff session with your team

Ideal fit

  • You're shipping an AI feature and need it to survive production
  • You have a working prompt-in-a-notebook and need the system around it
  • You have customer data and need multi-tenancy done right from day one
  • You're picking between AWS Bedrock, Azure OpenAI, and self-hosted, and want a clear answer

Engagement models that fit

  • Fractional SE: hands-on build alongside your team
  • Scoped Project: fixed deliverable, fixed timeline
  • AI Readiness Assessment: start here if you're not sure

Related writing

The thinking behind the practice.

AI
Retrieval is the secret-sauce surface: getting RAG right

Embeddings, chunking, hybrid search, the prompt template that pulls the right context. The surface that earns the bill.

 AI
The eval harness: how you know it's working

Test sets, golden examples, regression detection. Find out the AI broke before a customer does.

 AI
Prompts as code: versioning, A/B, rollback

Git, CI, feature flags. The discipline the most-edited surface in an AI product needs.

Got an AI system to build or rescue?

Tell us what you're working on. We'll figure out the shape together.

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