Audit-Ready AI Infrastructure
Traceable pipelines. Cited answers. Reproducible decisions. For regulated environments where “the AI said so” is not an acceptable answer.
The Problem
Organizations are deploying AI faster than they can govern it. When something goes wrong, the same questions appear, and most systems cannot answer them.
Which documents, records, or prompts produced this answer?
Which model version ran, and can the exact decision be reconstructed later?
Can this system hold up under compliance review, legal scrutiny, or internal audit?
Start Here
Start with an assessment when you need to understand risk. Move into a pilot when the use case is clear and your team is ready to build.
A focused review of your current data and AI architecture to identify governance gaps, traceability risks, and the fastest path to an audit-ready system.
Teams adopting AI in regulated or high-trust environments.
Architecture review, risk map, decision-traceability recommendations, and next-step roadmap.
Clarity on what to fix now before an AI workflow becomes expensive to unwind later.
Use this when you need to find traceability gaps, audit-readiness risks, and the highest-priority fixes before a larger AI effort.
Use this when the use case, document set, or workflow is already clear and your team needs a working, auditable AI system.
What We Do
We specialize in regulated environments where compliance, audit trails, and traceability are baseline requirements.
Cloud-native platforms, pipeline design, warehouse strategy, and the data foundations everything else depends on.
Retrieval pipelines, LLM integration, source citation, and production AI that can explain itself.
Audit trail design, data lineage, and controls that let you tell a regulator exactly what happened and why.
Who We Serve
Most AI systems fail not because they are inaccurate, but because they cannot be trusted.
If your organization handles sensitive data, answers to regulators, or depends on AI systems that must be trusted, the architecture behind those systems matters.
Building AI systems that can pass compliance reviews, not just demos.
Adopting AI without putting patient data, governance, or regulatory posture at risk.
AI systems that can withstand audits, investigations, and control reviews, not only perform in testing.
Answers that can be cited, defended, and trusted in real document-heavy workflows.
See how this works in real systems.
Explore the case studies →Public Sector
Public agencies handle policy documents, program guidance, and public information that must be answered consistently, cited accurately, and reviewed under scrutiny. This is the use case the architecture was built for.
Federal
Archetype Core is a SAM.gov-registered company with UEI and CAGE assigned, built for federal and regulated teams that need audit-ready AI and data infrastructure.
Active federal vendor registration with UEI and CAGE assigned. Full entity details available for federal, prime, and partner inquiries.
Led by a practitioner with Active Public Trust background and eight-plus years building data infrastructure in federal environments.
Core competencies, NAICS codes, differentiators, and project examples available for review.
About
If your team is deciding how to assess, pilot, or modernize an AI system that must be traceable and defensible, start the conversation here.
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