There & Back Again: The Complete Your-Fit-Tailored Journey

A living roadmap from concept through pilot to scale

1. The Metaphor Explained

“There & back again” captures the essence of Your-Fit-Tailored at every level of operation.

The Surface Meaning: Garments go out to users and come back. A simple round trip that repeats weekly.

The Deeper Meaning: The business model itself is circular—not just in logistics, but in learning and investment. Each cycle that completes feeds the next. Returns generate fit data. Fit data improves allocation. Better allocation increases satisfaction. Satisfaction drives retention. Retention justifies investment. Investment expands inventory. Inventory enables more users. More users generate more returns.

The Strategic Meaning: This project follows the same pattern. We start with specification (there), validate through pilot (the journey), return with operational truth (back again), then venture further into scale. Each epoch builds on what the previous learned.

The metaphor reminds us that in a circular business, endings are beginnings. The goal is not to reach a destination but to establish a sustainable rhythm where “there & back” becomes invisible to users—always fresh, always fits, no thought required.

2. Constitutional Foundation

All design decisions flow from six invariants established in memory/constitution.md. These are non-negotiable truths that govern the system regardless of implementation choices.

Invariant 1: Weekly Cadence

7-day cycles are non-negotiable; exceptions close with recovery, never skip.

Every active user maps to exactly one $WEEK_ID at any time
A delayed box is not “late shipping” but a broken cycle requiring explicit compensation
Exceptions are allowed only as explicit state changes, not silent delays

Invariant 2: Circular Inventory

Garments are stateful assets with lifecycle bounds; inventory is closed-loop.

A garment cannot be in two boxes at once
A garment cannot re-enter Available without passing through inspection
Every garment exit path (resale, donation, recycling) must be defined at intake

Invariant 3: State Truth Discipline

All entities auditable; single subsystem owns each state class.

Exactly one subsystem is authoritative for each class of state
External signals (carriers, facilities) are inputs that must be validated
State transitions are event-driven and monotonic where possible

Invariant 4: Explicit Failure Handling

Failures are normal; recovery paths are first-class, not exceptions.

Each failure resolves into: cost absorption, user compensation, or inventory adjustment
No failure remains unclassified (unclassified failures accumulate as hidden debt)
Recovery paths are designed upfront, not bolted on

Invariant 5: Probabilistic Fit

Fit is a belief distribution that evolves, not static measurement.

Allocations must incorporate uncertainty and remain correct when predictions are wrong
Each cycle should increase fit certainty or reduce uncertainty
Both explicit and implicit signals inform fit beliefs

Invariant 6: Cognitive Load Minimization

System defaults for all routine decisions; user input optional.

User cognitive load must asymptotically approach zero
Users must never be required to understand the internal state machine
Explicit user input is optional and rare

Verification Question: Does this choice reduce uncertainty, friction, or variance across cycles without increasing cognitive load on the user? If not, it violates the constitution.

3. The Complete Journey (5 Epochs)

    EPOCH 0        EPOCH 1         EPOCH 2          EPOCH 3           EPOCH 4          EPOCH 5
   Conception    Foundation      Validation      Maturation        Scaling         Sustained
   ──────────    ──────────      ──────────      ──────────        ───────         ─────────

   ╭─────────╮   ╭─────────╮    ╭─────────╮     ╭─────────╮      ╭─────────╮     ╭─────────╮
   │  Specs  │──▶│  Build  │───▶│  Pilot  │────▶│  Learn  │─────▶│  Scale  │────▶│ Operate │
   │ & Plans │   │  MVP    │    │ 25 Users│     │ Automate│      │ Expand  │     │ Sustain │
   ╰─────────╯   ╰─────────╯    ╰─────────╯     ╰─────────╯      ╰─────────╯     ╰─────────╯
       │              │              │               │                │               │
       │              │              │               │                │               │
       ▼              ▼              ▼               ▼                ▼               ▼
   Complete       Ready to       Decision:      Decision:        Decision:       Ongoing
   Jan 2026       Execute        Graduate/      Automate/        National/       Ops
                  3 weeks        Pivot/Shut     Manual Scale     Regional
                                 8-12 weeks     12-24 weeks      24-52 weeks

Epoch	Name	Duration	Primary Question	Status
0	Conception	Complete	What are we building?	✅ Specs done
1	Foundation Build	3 weeks	Can we build the MVP?	Ready to execute
2	Pilot Validation	8-12 weeks	Does this work?	Next phase
3	Intelligence Maturation	12-24 weeks	Can we learn fast enough?	Post-pilot
4	Controlled Scaling	24-52 weeks	Can we grow profitably?	Future
5	Sustained Operation	Ongoing	Can we compound value?	Future

4. Phase Definitions with Gate Criteria

Epoch 0: Conception (Complete)

Objective: Define what the system must be true at every level of abstraction.

Outputs Delivered:

Constitution with 6 invariants (memory/constitution.md)
Business model analysis (specs/economics/business-model.md)
Pilot operational playbook (specs/pilot-ops/playbook.md)
Pilot MVP specification (specs/features/pilot-mvp/spec.md)
Implementation artifacts for Airtable/Retool (implementation/)

Exit Criteria: All specifications reviewed and approved. Implementation artifacts ready to execute.

Epoch 1: Foundation Build (Ready to Execute)

Objective: Build the operational infrastructure for 25-user pilot.

Duration: 3 weeks

Work Breakdown:

Week	Focus	Artifacts
1	Airtable foundation	8 core tables, seed data, automations
2	Retool interfaces	20 pages across AdminConsole and WarehouseOps
3	Validation & training	End-to-end testing, operator training

Key Deliverables:

17 Airtable tables configured
6 automations running
20 Retool pages deployed
Operators trained on SOPs
Launch checklist complete

Exit Gate:

All Airtable tables passing validation queries
All automations tested with synthetic data
All Retool pages functional
One complete test cycle executed end-to-end
Operator sign-off on readiness

Epoch 2: Pilot Validation (8-12 weeks)

Objective: Prove (or disprove) that weekly cadence circular apparel works.

User Scale: 25 users (hard cap enforced by system)

Inventory Scale: ~100-150 garments

Team: 2-3 FTE equivalent (multi-functional roles)

Success Criteria from Spec (specs/features/pilot-mvp/spec.md):

Code	Criteria	Target
SC-201	Onboard 25 pilot users	Within 1 week
SC-202	Register 100+ garments	Correct barcoding
SC-203	Complete 25 first-week cycles	>90% success
SC-204	Fit satisfaction on first cycle	>80%
SC-205	Process returns	Within 10 days of window
SC-206	Inventory shrinkage	<5% over pilot
SC-207	Generate data export	Complete analysis
SC-208	Document all exceptions	For process improvement

Decision Gate (from specs/pilot-ops/playbook.md):

Decision	Criteria
Graduate	Cadence on-time >90%, shrink <5%, contribution margin non-negative, NPS >30
Pivot	Cadence feasible but economics/product shape needs change (price, SKUs, categories, packaging)
Shutdown	Cannot maintain cadence, shrink exceeds bounds, value proposition failure

Weekly Review Metrics:

Cadence: $ON_TIME_DISPATCH_PCT, $RECOVERY_TIME_P90
Inventory: $SHRINK_PCT_PER_CYCLE, $STATE_MISMATCH_COUNT
Operations: $CLEANING_TAT_HOURS, $QC_FAIL_RATE
Exceptions: $EXCEPTIONS_PER_100_CYCLES, $OPEN_EXCEPTIONS_AGE_P90
User: $SUPPORT_CONTACT_RATE, $CHURN_RATE, $NPS_LIKE_SCORE
Economics: $CONTRIB_MARGIN_PER_USER_WEEK, $COST_PER_BOX

Epoch 3: Intelligence Maturation (12-24 weeks)

Objective: Automate learning loops and scale with maintained quality.

User Scale: 25 → 250 users

Prerequisites:

Graduate decision from pilot
Capital secured for inventory expansion
Technical capacity for automation

Focus Areas:

Fit Intelligence Implementation
- Deploy probabilistic fit matching
- Implement automatic profile updates
- Reduce manual allocation to exceptions only
Logistics Optimization
- Negotiated carrier rates
- Optimized routing algorithms
- Hub-and-spoke consideration
Exception Automation
- Rules-based exception handling
- Graduated human escalation
- Labeled data from pilot informs automation
Retention Mechanics
- Churn prediction models
- Proactive intervention workflows
- Loyalty/engagement features

Exit Gate:

Fit algorithm accuracy >80% (no manual override needed)
Exception automation handles >50% of cases
Unit economics positive at 250 users
Operational load scales sublinearly with users

Epoch 4: Controlled Scaling (24-52 weeks)

Objective: Grow profitably while compounding system intelligence.

User Scale: 250 → 2,500 users

Inventory Scale: 1,500 → 15,000 garments

Focus Areas:

Geographic Expansion
- Multi-hub logistics
- Regional inventory pools
- Local carrier partnerships
Product Expansion
- Category additions (informed by pilot data)
- Size range optimization
- Seasonal rotation capability
Team Scaling
- Warehouse operations staff
- Customer support scaling
- Engineering for automation
Financial Model Validation
- Prove unit economics at scale
- Working capital efficiency
- Path to profitability demonstration

Exit Gate:

Positive contribution margin sustained 3+ months
Customer acquisition cost < 3 months revenue
Operational processes documented and repeatable
Technology platform stable under load

Epoch 5: Sustained Operation (Ongoing)

Objective: Establish the service as a continuously improving system.

Characteristics:

User growth driven by word-of-mouth and brand
Inventory turns compound efficiency
Fit intelligence becomes competitive moat
Operations are largely automated
Human oversight for exceptions and improvement

Key Metrics in Steady State:

Garment utilization: >15 uses per item
Net retention: >100% (upsells offset churn)
Contribution margin: >20%
NPS: >50
Carbon footprint per garment-use: declining

5. The Circular Nature (Triple-Loop Model)

Your-Fit-Tailored operates on three nested feedback loops, each with its own “there & back again” cadence.

                           ╭─────────────────────────────────────────╮
                           │        INVESTMENT LOOP (Quarterly)      │
                           │                                         │
          ╭────────────────┴───────────────╮                         │
          │                                │                         │
          ▼                                │                         │
    ┌──────────┐                           │                         │
    │  Invest  │                           │                         │
    │ Capital  │                           │                         │
    └────┬─────┘                           │                         │
         │                                 │                         │
         ▼         ╭─────────────────────────────────────────╮       │
    ┌──────────┐   │        LEARNING LOOP (Per Cycle)        │       │
    │ Acquire  │   │                                         │       │
    │Inventory │   │  ╭────────────────────────────────────╮ │       │
    └────┬─────┘   │  │      GARMENT LOOP (Weekly)         │ │       │
         │         │  │                                    │ │       │
         ▼         │  │    ┌─────┐    ┌──────┐   ┌──────┐  │ │       │
    ┌──────────┐   │  │    │ Out │───▶│ Wear │──▶│Return│  │ │       │
    │  Deploy  │───┼──┼───▶│     │    │      │   │      │  │ │       │
    │  to Box  │   │  │    └─────┘    └──────┘   └──┬───┘  │ │       │
    └──────────┘   │  │                            │      │ │       │
                   │  │    ┌─────────────────┐     │      │ │       │
                   │  │    │   Refurbish &   │◀────┘      │ │       │
                   │  │    │   Restock       │            │ │       │
                   │  │    └────────┬────────┘            │ │       │
                   │  │             │                     │ │       │
                   │  ╰─────────────┼─────────────────────╯ │       │
                   │                │                       │       │
                   │                ▼                       │       │
                   │  ┌──────────────────────────┐          │       │
                   │  │ Observe Fit & Condition  │          │       │
                   │  └────────────┬─────────────┘          │       │
                   │               │                        │       │
                   │               ▼                        │       │
                   │  ┌──────────────────────────┐          │       │
                   │  │ Update Fit Belief        │          │       │
                   │  └────────────┬─────────────┘          │       │
                   │               │                        │       │
                   │               ▼                        │       │
                   │  ┌──────────────────────────┐          │       │
                   │  │ Improve Next Allocation  │          │       │
                   │  └──────────────────────────┘          │       │
                   │                                        │       │
                   ╰────────────────────────────────────────╯       │
                                                                    │
    ┌──────────────────────────────┐                                │
    │  Measure Unit Economics      │◀───────────────────────────────╯
    └────────────┬─────────────────┘
                 │
                 ▼
    ┌──────────────────────────────┐
    │  Optimize Operations         │
    └────────────┬─────────────────┘
                 │
                 ▼
    ┌──────────────────────────────┐
    │  Reinvest in Inventory       │──────▶ (back to top)
    └──────────────────────────────┘

Loop 1: Garment Loop (Weekly)

Cadence: 7 days

Flow: Out → Wear → Return → Refurbish → Out

Key Metrics:

Days in transit (target: <2 each way)
Wear window utilization
Return compliance rate
Refurbishment turnaround

“There”: Garment leaves warehouse to user “Back Again”: Garment returns, cleaned, restocked

Loop 2: Learning Loop (Per Cycle)

Cadence: Each completed cycle

Flow: Observe → Hypothesize → Test → Learn → Apply

Key Metrics:

Fit feedback capture rate
Fit prediction accuracy
User satisfaction trend
Allocation exception rate

“There”: Send garments based on current belief “Back Again”: Update belief based on actual outcome

Loop 3: Investment Loop (Quarterly)

Cadence: 13 weeks

Flow: Invest → Operate → Measure → Optimize → Reinvest

Key Metrics:

Contribution margin per user-week
Garment turns per quarter
Working capital efficiency
Customer lifetime value

“There”: Deploy capital into inventory “Back Again”: Harvest learnings and returns to inform next deployment

6. Post-Pilot Roadmap

Scale Trajectory

Metric	Pilot (E2)	Maturation (E3)	Scale (E4)	Sustained (E5)
Users	25	250	2,500	10,000+
Weekly Cycles	25	250	2,500	10,000+
Garments	100-150	1,500	15,000	60,000+
Garment Turns/Year	10-15	15-20	20-25	25-30
Team Size	2-3	5-8	15-25	40-60
Locations	1 hub	1-2 hubs	3-5 hubs	Regional network
Capital Required	$50K	$500K	$3M	$15M+
Revenue (Annual)	$65K	$650K	$6.5M	$26M+
Contribution Margin	Negative	Break-even	10-15%	20%+

Key Milestones Post-Pilot

Month 3-6 (Maturation Start):

Deploy fit intelligence v1
Expand to 100 users
Establish second cohort ship day
Negotiate volume carrier rates

Month 6-9 (Maturation Mid):

Achieve 200 users
Automate 50% of exceptions
Launch retention mechanics
Achieve contribution margin break-even

Month 9-12 (Maturation End):

Reach 250 users
Fit algorithm >80% accuracy
Unit economics validated
Series A readiness (if external capital path)

Year 2 (Scale):

Geographic expansion (2-3 markets)
Product category expansion
2,500 users
Positive EBITDA

7. Immediate Pre-Pilot Actions

Before launching the 25-user pilot, the following actions must be completed to de-risk the validation phase.

Action 1: Mini-Pilot Validation (Critical)

Purpose: Validate return compliance before full pilot commitment.

Scope:

5 users (“friends and family”)
2 weeks, full cycles
Measure actual return timing distribution

Success Criteria: >90% on-time return without excessive friction.

Why This Matters: Return compliance is the “single weakest assumption” per the integrated specification. If users don’t return on time, the entire cadence model breaks down. Better to learn this with 5 users than 25.

Action 2: Price Cohort Test

Purpose: Validate unit economics at higher price point.

Scope:

Split pilot: 15 users at $50/week, 10 users at $65/week
Compare conversion, satisfaction, and contribution margin

Success Criteria: $65 cohort shows acceptable conversion with improved margin.

Why This Matters: At $50/week, contribution margin is ~14% with no safety buffer. The $65 price point provides margin headroom for operational variance.

Action 3: Payment Integration Specification

Purpose: Complete payment handling specification before billing goes live.

Required Deliverable: /specs/features/payment/spec.md covering:

Stripe/Braintree integration patterns
Pre-authorization timing (at commitment)
Decline handling workflow
Refund/credit policies
Settlement computation logic

8. Risk Analysis

Comprehensive risk assessment based on evaluation report findings. See /analysis/evaluation-to-growth-report.md for full analysis.

Critical Risks

Risk 1: Return Compliance Cascade (CRITICAL)

Description: If late return rate exceeds 15%, a cascade of negative effects compounds:

p_late_return > 15%
       │
       ▼
Buffer $B must increase → More inventory per user
       │
       ▼
Capital tied up in buffer → Cash flow stress
       │
       ▼
Next cycle delayed → User trust erodes
       │
       ▼
Churn increases → Subscriber loss
       │
       ▼
Fixed costs spread over fewer users → Margin collapse

Mitigation:

Pre-pilot validation with small cohort (Action 1 above)
Aggressive deposit/hold policy
Proactive return facilitation communications
Clear escalation path defined in SOPs

Status: Unmitigated until mini-pilot completes.

Risk 2: Unit Economics Sensitivity (HIGH)

Description: The $7/week contribution margin at $50/week has no safety buffer.

Sensitivity analysis shows:

If shipping increases from $34 to $50: Margin collapses
If garment lifespan drops from 20 to 10 uses: Margin halves
If cleaning cost doubles: Margin eliminated

Mitigation:

Test higher price point ($65-75/week) in pilot
Negotiate carrier rates before scale
Monitor cost drivers weekly during pilot

Status: Partially mitigated (honest analysis exists, higher price cohort planned).

Medium Risks

Risk 3: Airtable Scaling Limits (MEDIUM)

Description: Airtable constraints will become limiting factors:

50,000 record limit per base
Rate limits on automations
No real-time event streaming
Limited concurrent user capacity

At 250 users × 52 weeks × 3 items = 39,000 garment-cycle records/year.

Mitigation:

Document migration path to scalable platform (Postgres + custom app)
Trigger migration planning before reaching 150 users
Monitor record growth weekly

Status: Documented, not mitigated.

Risk 4: Manual Allocation Bottleneck (MEDIUM)

Description: Weekly allocation at pilot (25 cycles) requires 1-2 hours of operator time. At 250 users, this becomes 10-20 hours/week—essentially a full-time role.

Mitigation:

Prioritize Fit Intelligence MVP before scaling past 75 users
Begin specification work during pilot weeks 4-8
Track allocation time weekly to model scaling curve

Status: Planned for Epoch 3.

Risk 5: Key Person Dependencies (MEDIUM)

Description: Pilot operations depend on specific individuals with no redundancy specified.

Mitigation:

SOPs documented (complete)
Cross-train operators during build phase
Establish on-call rotation

Status: Partially mitigated.

Risk Matrix Summary

Risk	Likelihood	Impact	Priority	Status
Return non-compliance	Medium	Critical	P1	Unmitigated
Unit economics failure	High	Critical	P1	Partial
Airtable scaling limits	Medium	High	P2	Documented
Manual allocation bottleneck	High	Medium	P2	Planned
Key person dependency	Medium	Medium	P3	Partial

9. Risk Framework

Graduate Criteria (All Must Be True)

Criteria	Threshold	Measurement
Cadence On-Time	>90%	`$ON_TIME_DISPATCH_PCT` over stability period
Shrinkage	<5% per cycle	`$SHRINK_PCT_PER_CYCLE`
Contribution Margin	Non-negative	`$CONTRIB_MARGIN_PER_USER_WEEK`
Net Promoter Score	>30	Post-cycle survey
Recovery Time	P90 <7 days	`$RECOVERY_TIME_P90_DAYS`

Pivot Indicators (Any One Triggers Review)

Indicator	Signal	Possible Pivot
Price sensitivity	Churn spikes at renewal	Price increase or tier restructure
Category failure	Specific garment types high damage/dissatisfaction	Reduce SKU breadth
Fit ceiling	Cannot improve fit satisfaction past 70%	Simplify to standard sizes only
Cadence stress	Weekly is operationally unsustainable	Move to bi-weekly
Geographic limitation	Shipping costs destroy economics	Regional-only model

Shutdown Criteria (Any One Triggers Evaluation)

Criteria	Threshold	Implication
Cadence Failure	On-time <70% for 4+ weeks	Operational model broken
Shrinkage Spiral	>15% cumulative	Inventory economics unworkable
Value Rejection	NPS <0 or churn >50%/month	Users don’t want this
Cash Exhaustion	<30 days runway	No path to continue

Risk Mitigations

Risk	Mitigation	Owner
High shipping costs	Negotiate volume rates; staged replenishment	Ops Lead
Cleaning bottleneck	Multiple vendor contracts; overflow capacity	Inventory Controller
Fit intelligence failure	Maintain manual allocation capability	Data Steward
User acquisition cost	Referral focus; low-CAC channels	Marketing
Inventory capital lock-up	Resale channel for retired items	Finance

10. Success Metrics by Phase

Epoch 1: Foundation Build

Metric	Target	Method
Tables created	17	Count in Airtable
Automations working	6/6	Test with synthetic data
Pages deployed	20	Count in Retool
Test cycles complete	1+	End-to-end validation
Operators certified	100%	Training sign-off

Epoch 2: Pilot Validation

Metric	Target	Method
User onboarding	25 in week 1	`$ACTIVE_USERS_END`
Cycle completion	>90%	`$CYCLES_COMPLETED / $CYCLES_SCHEDULED`
On-time delivery	>90%	`$ON_TIME_DELIVERY_PCT`
Return compliance	>90%	Returns received / Returns expected
Fit satisfaction	>80%	Survey response
Shrinkage	<5%	`$SHRINK_PCT_PER_CYCLE`
NPS	>30	Survey calculation

Epoch 3: Maturation

Metric	Target	Method
User scale	250	Active user count
Fit automation	>80%	Manual overrides / Total allocations
Exception automation	>50%	Auto-resolved / Total exceptions
Contribution margin	Non-negative	`$CONTRIB_MARGIN_PER_USER_WEEK`
Churn	<10%/month	Churned / Active start of month

Epoch 4: Scale

Metric	Target	Method
User scale	2,500	Active user count
Markets	3-5	Active hub locations
Contribution margin	>10%	Monthly financial review
CAC payback	<3 months	CAC / ARPU
Garment turns	>20/year	Total rentals / Total inventory

Epoch 5: Sustained

Metric	Target	Method
Net revenue retention	>100%	Annual cohort analysis
NPS	>50	Quarterly survey
Garment turns	>25/year	Inventory utilization report
Contribution margin	>20%	Monthly P&L
Carbon/garment-use	Declining	Annual sustainability audit

11. Known Gaps and Future Work

Updated 2026-01-29 based on Evaluation-to-Growth Report findings.

Critical Specification Gaps (Pre-Pilot)

Gap	Current Status	Required For	Priority
User Acquisition Strategy	Absent	Pilot recruitment	Critical
Payment Integration Specification	Absent	Pilot billing	Critical
Regulatory Compliance Checklist	Absent	Legal protection	High

User Acquisition Gap: Despite extensive operational specification, there is no plan for:

User acquisition channels beyond “direct marketing” and “referral”
Customer acquisition cost (CAC) modeling
Brand positioning and messaging strategy
Timeline for recruiting 25 pilot users

Payment Integration Gap: Payment appears in state machines (HoldPayment state) but implementation is missing:

No payment processor specified
No billing cycle definition (pre-pay vs. post-pay)
No deposit/liability policy specification
Settlement computation logic undefined

Regulatory Compliance Gap: No specification addresses:

Consumer protection regulations for subscription services
State-by-state compliance requirements
Sustainability claims verification (greenwashing risk)
Data privacy (fit profiles contain body measurements)
Health/hygiene certification requirements

Specification Gaps (Post-Pilot)

Gap	Current Status	Required For	Priority
Fit Intelligence Subsystem	Theoretical (Constitution §5)	Epoch 3	High
Logistics Optimization	Partial (playbook SOPs)	Epoch 3	Medium
Retention Mechanics	Not specified	Epoch 3	Medium
Churn Prediction	Not specified	Epoch 3	Medium
Scale Architecture	Theoretical only	Epoch 4	Low (for now)
Multi-tenant Support	Not addressed	Epoch 4 (if B2B)	Low

Fit Intelligence MVP: The core differentiator (“always fits”) relies on operator judgment during pilot. Specification needed for:

Minimum viable recommendation algorithm
Feedback capture UX
Profile update triggers
Confidence scoring methodology

Financial Model Gaps

Gap	Current Status	Required For	Priority
Working capital model	Preliminary estimates	Epoch 2 investment decision	High
CAC/LTV analysis	Framework only	Epoch 3 go-to-market	Medium
Multi-hub economics	Not modeled	Epoch 4 expansion	Low
Exit scenarios	Not addressed	Investor discussions	Low

Operational Gaps

Gap	Current Status	Required For	Priority
Carrier contract templates	Not created	Epoch 2 launch	High
Cleaning vendor SOPs	Not formalized	Epoch 2 launch	High
User legal agreements	Not drafted	Epoch 2 launch	High
Safety/hygiene protocols	Mentioned in playbook	Epoch 2 launch	High
Insurance requirements	Not researched	Epoch 2 launch	Medium

Technology Gaps

Gap	Current Status	Required For	Priority
Fit algorithm design	Conceptual	Epoch 3	Medium
Barcode/RFID selection	Not specified	Epoch 1	High
User-facing app/portal	Not designed	Epoch 2 or 3	Medium
Analytics platform	Ad-hoc exports	Epoch 3	Medium

12. Appendices

A. Document Cross-References

Document	Location	Purpose
Constitution	`memory/constitution.md`	Invariants governing all design
Business Model	`specs/economics/business-model.md`	Unit economics and pricing analysis
Pilot Playbook	`specs/pilot-ops/playbook.md`	Operational procedures and SOPs
Pilot MVP Spec	`specs/features/pilot-mvp/spec.md`	Feature requirements and acceptance criteria
Implementation Status	`implementation/IMPLEMENTATION-STATUS.md`	Current build progress
Airtable Setup	`implementation/airtable/SETUP-GUIDE.md`	Database configuration
Retool Setup	`implementation/retool/SETUP-GUIDE.md`	UI configuration
Evaluation Report	`analysis/evaluation-to-growth-report.md`	Comprehensive project analysis (2026-01-29)

A.1 Evaluation Report Summary

The Evaluation-to-Growth Report (analysis/evaluation-to-growth-report.md) provides comprehensive analysis across 9 dimensions:

Key Findings:

Constitution-driven design creates strong architectural coherence
State transition contracts are production-ready
Unit economics at $50/week are marginal (~14% contribution margin)
Return compliance remains the single weakest assumption
User acquisition strategy is absent

Top Recommendations:

Validate return compliance before full pilot launch with 5-user mini-pilot
Increase pilot price to $65-75/week or reduce cadence to improve margin safety
Prioritize user acquisition specification before scaling past pilot

See full report for detailed analysis, risk matrix, and strategic recommendations.

B. Key Variable Reference

From pilot playbook (specs/pilot-ops/playbook.md):

Variable	Definition	Typical Value
`$PILOT_USER_RANGE`	Target user count	25-250
`$CADENCE_ON_TIME_TARGET_PCT`	On-time delivery target	90%
`$LOSS_DMG_MAX_PCT_PER_CYCLE`	Maximum acceptable shrinkage	5%
`$LATE_GRACE_DAYS`	Days before late return triggers action	2-3
`$LOST_FINALIZE_DAYS`	Days before declaring item lost	14
`$RECOVERY_MAX_DAYS`	Maximum days to restore cadence	7

C. Epoch Timeline (Nominal)

2026
────────────────────────────────────────────────────────────────────────
Jan    Feb    Mar    Apr    May    Jun    Jul    Aug    Sep    Oct    Nov
│      │      │      │      │      │      │      │      │      │      │
├──────┤
│  E0  │  Specs complete
│      │
      ├────────┤
      │   E1   │  Foundation build (3 weeks)
      │        │
              ├────────────────────────────┤
              │           E2               │  Pilot validation (12 wks)
              │                            │
                                          ├────────────────────────────┤
                                          │           E3               │
                                          │  Maturation (begins)       │

2027
────────────────────────────────────────────────────────────────────────
              │           E3 continues     │           E4 begins
              ├────────────────────────────┼───────────────────────────▶

D. Decision Log Template

For tracking graduate/pivot/shutdown decisions:

Decision ID: ____
Date: ____
Week of Pilot: ____

Metrics Snapshot:
- On-time dispatch: ____%
- Shrinkage: ____%
- NPS: ____
- Contribution margin: $____

Decision: [ ] GRADUATE  [ ] PIVOT  [ ] SHUTDOWN  [ ] CONTINUE

Justification (cite 3 metrics + 1 qualitative observation):
1. ____
2. ____
3. ____
4. ____

If PIVOT, what changes:
____

If SHUTDOWN, reason:
____

Next actions:
1. ____
2. ____
3. ____

Signed: ____

Last updated: 2026-01-29 Status: Living document—update as epochs progress