Audit-Ready AI and Data Reference Systems

Featured Case Study

Audit-Ready RAG System

A traceable, citation-grounded retrieval architecture tested against USCIS policy documents. The point is not immigration. The point is what trustworthy AI infrastructure looks like under pressure.

The Architecture

A RAG pipeline that ingests PDF documents, chunks them with respect to legal hierarchy, stores embeddings in pgvector, and retrieves relevant context to generate grounded answers.

What Makes It Auditable

Source citations on grounded answers, an explicit no-source fallback when retrieval evidence is insufficient, retrieval choices designed to reduce redundancy, and architecture decisions made for repeatability.

Why This Matters

The same architecture pattern can be adapted for compliance documents, internal knowledge bases, policy libraries, and other controlled document collections.

Architecture Decisions

pgvector over Pinecone: SQL-joinable, no new infrastructure dependency, lower cost, and the data stays in your environment.
MMR retrieval over plain similarity: Maximal Marginal Relevance reduces redundancy when retrieving from repetitive legal text.
Temperature 0: Lower-variance model settings for more consistent, repeatable outputs in compliance-oriented use cases.
Recursive chunking: Respects nested legal document hierarchy instead of blindly splitting on token count.
Claude Sonnet 4.6 via AWS Bedrock: Strong performance on dense legal text with an enterprise-friendly deployment path.
Explicit configuration: Model settings, credentials, and data paths are made visible rather than assumed from ambient environment state.

AWS Bedrock Claude Sonnet 4.6 Titan Embeddings v2 PostgreSQL pgvector LangChain FastAPI Docker S3 Python

View Repo → Discuss Your Use Case →

Featured Case Study

ETL Architecture for AI Governance

A reference ETL implementation for ingesting, classifying, and governing AI-processed data. It demonstrates local development, audit trails, AI-assisted document classification, data quality gates, and infrastructure-as-code patterns.

The Architecture

An Airflow-orchestrated pipeline that ingests raw documents, classifies them using Claude on Amazon Bedrock, applies data quality gates via Great Expectations, and lands validated records in Databricks Delta Lake with full lineage tracking.

What Makes It Auditable

Every transformation is versioned. Every AI classification decision is logged with model version, prompt hash, and confidence score. Quality gates block invalid records before they reach governed tables. The local development stack is reproducible from a single Makefile command.

Local-to-Cloud Parity

The stack runs locally through Docker Compose and LocalStack, while Terraform modules document an AWS deployment path. Teams can test DAGs, quality gates, and classification logic without requiring a live client environment.

Architecture Decisions

Airflow over Prefect: The target production environment is Amazon MWAA, which provides a managed Airflow runtime with IAM integration, S3 DAG storage, and CloudWatch logging. Prefect would require self-hosting and rebuilding equivalent AWS integrations.
Databricks Delta Lake over raw S3: ACID transactions, schema enforcement, time travel for audit queries, and a query layer that supports both batch analytics and ad-hoc investigation.
Claude on Bedrock for classification: Enterprise deployment path designed for AWS-based regulated environments, with controlled access, logging, and deployment boundaries.
Great Expectations for quality gates: Declarative validation checks enforce required identifiers and timestamps, allowed document types, agencies and priority tiers, minimum page counts, and minimum document-text length before records continue downstream.
LocalStack for local AWS emulation: S3 runs locally in Docker for development and integration testing, reducing cloud spend and credential exposure while preserving the AWS S3 API surface.
Terraform with remote state: Versioned S3 state backend with DynamoDB locking. Every infrastructure change is tracked, reviewable, and reproducible across environments.
Makefile-driven workflows: One command to stand up the full stack, run tests, lint, or tear down. Reduces onboarding friction and eliminates undocumented tribal knowledge.
Strict code quality tooling: Ruff for linting, mypy for type checking, pytest for tests. Enforced in CI. No shortcuts on code that governs data lineage.

Apache Airflow 3.2 Amazon Bedrock Claude Sonnet Databricks Delta Lake PostgreSQL Great Expectations Terraform Docker Compose LocalStack Python

View Repo → Discuss Your Use Case →

Your data. The same rigor.

Compliance pipelines, document classification workflows, warehouse migrations, and AI integration with audit trails. Archetype Core can adapt these patterns to a defined client environment and requirement.

Discuss Your Use Case

More Work

Related systems.

Reference build

Automated Data Quality Monitoring

Workflow-driven data quality monitoring with checks for schema drift, null thresholds, freshness windows, and row-count anomalies. Quality gates without the overhead of a full observability platform.

n8n PostgreSQL Python SQL

Real systems, open code, documented decisions.

Audit-Ready RAG System

The Architecture

What Makes It Auditable

Why This Matters

Architecture Decisions

ETL Architecture for AI Governance

The Architecture

What Makes It Auditable

Local-to-Cloud Parity

Architecture Decisions

Your data. The same rigor.

Related systems.

Automated Data Quality Monitoring

Need auditable data infrastructure?