Public Sector
Archetype Core helps federal agencies and government contractors modernize data platforms, implement Databricks and Unity Catalog, engineer reliable pipelines, and build AI systems whose outputs remain traceable to authoritative sources.
The Standard
Archetype Core works across data readiness, system readiness, and evidence readiness so consequential systems are reliable, governed, and reconstructable later.
Is the data authoritative, understood, and traceable to its source?
Can it be used securely and reliably, with the right controls, testing, and monitoring?
Could someone later reconstruct what the system saw, did, produced, and what a human ultimately decided?
Source → data / retrieval → AI / agent → output → human action → decision
The evidence has to survive each handoff.
Services
Engagements are shaped around the mission need, existing environment, security boundary, and evidence the team must retain. The Readiness Review is one way to begin, not the full extent of the work.
Databricks architecture, lakehouse modernization, legacy migration, PySpark pipelines, data quality controls, performance, and production reliability.
Unity Catalog architecture, access design, lineage, provenance, ownership, and audit controls that make data movement and system behavior reconstructable.
RAG and retrieval architecture, source citations, controlled access, evaluation, monitoring, confidence signals, and human decision handoffs.
A Clear Starting Engagement
For one defined AI or data use case, the review traces the evidence chain from authoritative source to human decision and shows where it stops being complete.
From kickoff
Confirmed at intake
Fixed fee
Standard engagement covers one bounded use case confirmed during a short intake. Delivery starts at kickoff once required access and materials are available. Work outside the confirmed boundary is scoped separately.
Proof
Archetype Core maintains working reference systems to demonstrate how traceability is designed into retrieval and data pipelines, not added after the fact.
The RAG system demonstrates 9,116 indexed chunks, 44 automated tests, citation-backed retrieval, and auditable interaction records. The ETL system demonstrates Databricks Delta Lake pipelines, data quality gates, record-level audit trails, and SHA-256 hashing of input records and prompts.
Grounded answers with source citations and recorded retrieval context.
Model and retrieval metadata retained so system behavior can be examined later.
AWS Bedrock, PostgreSQL, containerized services, and infrastructure-as-code.
Delivery Approach
Archetype Core supports agencies, prime contractors, and subcontract teams with architecture, modernization, implementation, and focused advisory work. Senior Databricks and data architecture support can be embedded within an established delivery team or scoped as a bounded workstream.
Identify the mission outcome, authoritative sources, users, integrations, constraints, and the decision the work must support.
Design lineage, access, testing, monitoring, and audit records into the architecture rather than adding them after delivery.
Document the architecture, decisions, operating controls, and remaining risks so the team can run and defend the system.
Bring the mission need, the current environment, and the question the system must be able to answer later.
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