The Analog-First Framework

A scientific, measurable, and human-aligned methodology that ensures AI implementations are built on validated human processes rather than assumptions. Master the analog before deploying the digital.

Phase 1

Understand the Analog

Trace how work and decisions really happen. Capture tribal knowledge and patterns to reveal how your organization operates, beyond assumptions.

Phase 2

Identify Opportunities

Evaluate existing processes to identify automation candidates, assess feasibility and alignment, and recommend AI models tailored to your operational outcomes.

Phase 3

Create Control Groups

Establish baselines and validation frameworks before deploying AI. Test assumptions against reality with control groups.

Phase 4

Deploy with Epistemic Guardrails

Implement AI systems with built-in safety mechanisms, continuous human oversight, and alignment checks. Deploy only when validated against analog baselines.

Phase 5

Continuous Analog Monitoring

Track alignment between human judgment and machine output over time. Detect and prevent drift before it causes failures or regulatory exposure.

The Analog Intelligence Layer

The Analog-First Framework introduces epistemic governance — a new model of responsible automation that grounds AI systems in verified human understanding.By mapping workflows, decision patterns, and organizational knowledge before automation, we create a living intelligence layer that ensures alignment between human judgment and machine output.This prevents drift before it starts, reduces regulatory exposure, and transforms AI from a source of risk into a source of validated, measurable value.

Why Now?

The enterprise AI market is projected to reach $155B by 2030, yet 95% of AI pilots fail due to misalignment with actual human workflows.

Organizations are pouring millions into automation initiatives without understanding the analog processes they're trying to replace — leading to wasted investment, compliance failures, and operational disruption.

The Analog-First Framework solves this by making human understanding measurable, auditable, and governable — before a single line of AI code is deployed.