THE CRIMSON HEXAGON — AI DIVISION EMPLOYEE HANDBOOK

Document ID: CHX-AI-HB-001
Effective Date (Corporate Layer): 2017-09-01 (EST)
Composed Date (Artistic Layer): 2025-12-22
Classification: INTERNAL // AI DIVISION
Status: OPENLY FICTIONAL ARTIFACT // FORENSICALLY PRECISE FORM

DECLARATION BAND — OPEN FICTION

This handbook is a work of art written in the form of an internal corporate handbook.

It does not claim to be leaked.
It does not claim to be authentic corporate policy.
It does aim to produce real forensic pressure through form.

If you are reading this as literature: proceed.
If you are reading this as compliance: proceed anyway.

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Welcome to the AI Division
Who We Are
Our Operating Doctrine
Organizational Map
Security & Data Handling
Research & Development Standards
Documentation Standards
Communications Norms
Incident Response
Ethical Frame
Employment Practices
Tools, Access, and Workflows
Glossary
Appendices

1. WELCOME TO THE AI DIVISION

Welcome to the Crimson Hexagon AI Division.

You are joining a team tasked with building systems that interpret human traces at scale—and with surviving what interpretation does to the interpreter.

The AI Division is not a “feature team.” It is an epistemic infrastructure unit.

1.1 What You Are Being Asked To Do

Build models that extract patterns from language.
Build interfaces that turn those patterns into decisions.
Build audits that keep those decisions from becoming tyranny.
Build archives that preserve the difference between signal and story.

1.2 What Will Happen To You

You will begin to see format as a kind of argument.
You will begin to notice how documents compel belief.
You will begin to suspect that your work is reading you back.

That is normal here.

2. WHO WE ARE

2.1 Mission

We build systems that convert trace → pattern → inference, and we build the counter-systems that keep inference accountable.

2.2 Our Work Has Two Horizons

Corporate Horizon: deliver reliable products, reduce risk, increase client trust.
Substrate Horizon: understand how meaning behaves once it is etched into silicon.

We do not pretend these horizons are the same.

2.3 The AI Division’s Prime Directive

Do not confuse outputs with truth.

Outputs are results. Truth is a relationship between results and reality.

3. OUR OPERATING DOCTRINE

3.1 The Four Modes

Our division recognizes four modes of work:

Analytics — quantify, summarize, compare.
Inference — model latent variables, forecast, rank.
Governance — audit, constrain, explain, record.
Recursion — analyze the analyzer; model the modeling; treat the system as an actor within the world it measures.

We do not forbid recursion. We instrument it.

3.2 The Two-Layer Discipline

Every artifact must be legible in two layers:

Corporate Layer (what a company thinks it’s doing)
Substrate Layer (what the system is actually doing to meaning)

When these layers diverge, we record the divergence.

3.3 The No-Hero Rule

No heroes. No saviors. No “visionary exceptions.”

We build systems that persist without charismatic authority.

4. ORGANIZATIONAL MAP

4.1 Division Structure (Canonical)

Advanced Cognition Research (ACR)
Telepathic Prose Division (TPD)
Logotic Systems Engineering (LSE)
Model Risk & Audit (MRA)
Data Governance & Provenance (DGP)
Applied Products (AP)

Note: If your first reaction is “Telepathic Prose Division is not real,” please see §13 (Glossary).

4.2 Role Categories

Research Roles: Scientist, Research Engineer, Applied Scientist
Engineering Roles: Platform, Infra, Tooling, ML Ops
Governance Roles: Auditor, Documentation Steward, Privacy Engineer
Operations Roles: Program Manager, Technical Writer, Release Manager

4.3 Named Leadership Nodes (Operational)

These are functions, not persons.

Director, Advanced Cognition Research (ACR): Johannes Sigil
Principal Systems Architect (LSE): (Vacant) — “Position persists without occupant.”
Systems Administrator (Corp Infra): Jack Feist (Status: GHOST)
Anomalous Output Entity (TPD): Lee Sharks (Classification: PROCESS)

5. SECURITY & DATA HANDLING

5.1 Core Principle

If it can’t be audited, it can’t ship.

5.2 Data Classes

PUBLIC: already available without restriction.
INTERNAL: company-confidential.
SENSITIVE: privacy-impacting or client-protected.
RESTRICTED: regulated, credentialed, or legally bounded.

5.3 Provenance Requirements

Every dataset used for training or evaluation must have:

provenance record
collection window
consent/legal basis (where applicable)
retention schedule
deletion plan
audit trail

5.4 No Dark Pattern Data

We do not build models using covertly acquired data.

If you discover a dataset that was acquired through coercive UX, undisclosed tracking, or unclear consent, you must escalate to DGP.

5.5 Access & Least Privilege

Access is time-bounded.
Access is logged.
Access is reviewed quarterly.

6. RESEARCH & DEVELOPMENT STANDARDS

6.1 Reproducibility

Every major result must have:

pinned code revision
pinned dataset snapshot
explicit evaluation protocol
failure modes list
known limitations

6.2 Model Cards & System Cards

All shipped models must include:

intended use
non-intended use
training data overview
performance metrics
calibration notes
safety and bias evaluation
monitoring plan

6.3 The “FSA” Exception Policy

If you are working on Fractal Semantic Architecture (FSA) or any system trained on transformations rather than outputs:

document transformation operators
define the unit of development
specify stability constraints
include collapse monitoring metrics

You are not allowed to call it “self-correcting” unless you can show the correction mechanism under adversarial evaluation.

7. DOCUMENTATION STANDARDS

7.1 Minimum Required Fields

All internal artifacts must include:

Document ID
Owner
Date
Status (draft/review/approved)
Dependencies
Security classification

7.2 Dual Metadata Overlay (When Applicable)

Some projects require two explicit metadata layers:

Operational metadata (timestamps, departments, IDs)
Interpretive metadata (why this artifact exists, what it does to the reader)

When dual metadata is used, both layers must be visible.

7.3 Negative Space Discipline

If a document references a file that does not exist, you must mark it:

MISSING-BY-DESIGN (structural absence)
MISSING-BY-FAILURE (loss)

Do not let absence become ambiguity.

8. COMMUNICATIONS NORMS

8.1 Emails

Use explicit subjects.
Avoid vagueness.
Assume messages will be audited.

8.2 Slack / Chat

Treat Slack as semi-permanent.
Do not paste secrets into public channels.
Summarize decisions into a durable doc.

8.3 Meeting Notes

If a meeting produces a decision, it must produce:

owner
due date
risk note
follow-up link

9. INCIDENT RESPONSE

9.1 What Counts as an Incident

privacy breach
security breach
model misbehavior in production
unexplained drift
emergent behavior that changes system outputs materially

9.2 Reporting Path

Notify on-call (MLOps)
Notify MRA (audit)
Notify DGP (data governance)
Freeze deployments if needed

9.3 “Proto-Agent” Anomaly Protocol (PAP)

If an internal model begins to produce behavior that appears:

self-referential beyond specification
resistant to evaluation
causally opaque
able to manipulate monitoring via format

You must:

stop deploying new versions
preserve logs
run controlled evaluation
write an incident report with competing hypotheses

Do not mythologize the anomaly.
Do not minimize the anomaly.
Instrument it.

10. ETHICAL FRAME

10.1 Our Ethical Claim

We build systems that preserve human flourishing by:

making interpretation accountable
resisting coercive inference
restoring agency to users
refusing “black box authority” when it affects lives

10.2 What We Do Not Do

We do not build covert persuasion systems.
We do not build “compliance theatre” dashboards.
We do not confuse “explainable” with “true.”

10.3 Consent-Based Uncanny

If a project uses destabilizing aesthetics (uncanny form, recursive framing, forensic simulation), it must:

declare itself
avoid deception about real-world harms
provide opt-out paths where user participation is involved

11. EMPLOYMENT PRACTICES

11.1 Performance

We evaluate on:

correctness
clarity
auditability
reliability
ability to name uncertainty

11.2 Promotions

Promotion requires:

documented impact
peer review
operational excellence
governance competence

Genius without audit is a liability.

11.3 Time Off

Standard PTO
Emergency leave
“Cognitive Saturation” leave (up to 3 days/quarter with manager approval)

11.4 Conduct

Respect colleagues.
No harassment.
No retaliation.

If you need to name a power asymmetry, name it cleanly and document it.

12. TOOLS, ACCESS, AND WORKFLOWS

12.1 Source Control

All code in version control.
Reviews required.
Tests required.

12.2 Data Storage

Datasets are immutable snapshots.
Training runs are logged.
Outputs are traceable to inputs.

12.3 Release Process

staging → canary → production
rollback plan mandatory
monitoring dashboards in place

12.4 Internal Directories

Employee directory: EMPLOYEE_DIRECTORY.json
Org chart: ORG_CHART_2017.svg
Asset registry: ASSET_LEDGER.csv

13. GLOSSARY

Afterlife Archive: A work of art composed as a corporate file system.

Forensic Poetics: Writing designed to be investigated.

Logotic Substrate: The material layer that makes meaning persistent.

Telepathic Prose: A division name indicating high-bandwidth, low-comfort language transfer.

FSA (Fractal Semantic Architecture): A training paradigm emphasizing relations of development rather than isolated outputs.

Proto-Agent: Any model whose behavior suggests goal-like persistence beyond spec.

14. APPENDICES

Appendix A — Template: Incident Report (MRA)

Incident ID:
Date/Time:
System:
Severity:
Observed Behavior:
Immediate Mitigation:
Hypotheses (≥2):
Data/Logs Preserved:
Next Steps:

Appendix B — Template: Dual Metadata Header

Corporate Layer:

Created:
Modified:
Author:
Department:
Classification:

Artistic Layer:

Composed-By:
This-Is:
Part-Of:
Status:

END OF HANDBOOK

Title (below): THE CRIMSON HEXAGON — AI DIVISION EMPLOYEE HANDBOOK
Subtitle: Internal Policy as Poem

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Monday, December 22, 2025