Recreategoods extracts
management decision from design, creation, ideation.
40%
unsold clothing
60
billion unsold garments
50%
Lead Time
[THE PROBLEM]
Upcycling doesn't scale.
Until now.
Up to 40% of annually manufactured clothing goes unsold,roughly 60 billion garments.
Upcycling offers a path out, but today it depends on individual design talent,
resists standardization, and is impossible to cost in advance.
It does not scale because the decisions it requires, what to make, from what,
at what cost, for whom, have no systematic answer.
recreategoods provides that answer.
[DECISION LOGIC]
Multiple AI agents.
One autonomous decision chain.
AI agents own each stage, reasoning, critiquing, and refining outputs before advancing them. A dedicated critic agent reviews every stage output before it proceeds — this is what makes autonomy reliable rather than aspirational.
[PLANNER] → [GENERATOR] → [VALIDATOR] → [SCORER] → [SELECTOR]
Each stage is modular and independently auditable. No black box.
Every decision is traceable.
[THE PROBLEM]
Upcycling doesn't scale.
Until now.
[OUTPUTS]
What you receive from
every run.
Work description
Structured production instructions per transformation. Each step mapped to known operation types, with dependencies and time estimates.
Cost calculation
Cost calculation
Output as a range: optimistic, expected, conservative. Reflects real uncertainty without false precision.
CO2 savings
Conservative upper-bound estimates, traceable to individual production steps. Ready for sustainability reporting.
Commercial signal
Comparable historical product performance, price band success rates, and category demand, with explicit confidence intervals.
[BRAND IDENTITY]
The system works fully on-brand.
Brand Profile
-
extracted from lookbooks, campaigns, references
-
encodes aesthetic, price, margin, sustainability
-
persistent across all decisions
Taste Alignment
-
learns from a single domain expert
-
generalizes across your full assortment
-
no large datasets required
System Behavior
-
no prompts, no manual tagging
-
operates within defined brand constraints
-
produces consistent, scalable outputs
[AUTONOMY GROWS]
More capability appears as the system learns.
More capability appears as the system learns.
Capabilities are not toggled manually.
They appear when the system has the data to support them reliably.Selection rationales appear when the system starts selecting autonomously
Taste indicators appear when the system has learned enough to have preferences
Commercial signals appear when sufficient sales data exists
Campaign material generation appears at full adaptive autonomy
[FEATURES]
Inputs. Decisions. Outputs.
Built as a system.
Inputs
Inventory and data from your existing systems.
CSV, JSON or ERP integration.
Lookbooks and campaign images for brand setup.
Decision chain
End-to-end evaluation before any result appears.
Transformation planning
Feasibility validation
Cost calculation
CO₂ estimation
Brand alignment
Commercial scoring
Outputs
Production-ready results.
Tech packs
Transformation specifications
Cost and margin breakdown
CO₂ reduction
Multiple options with clear trade-offs.
Brand Identity
A persistent understanding of your brand.
Built from existing assets
Adaptive over time
Controlled exploration
Continuous learning
Access
Built for teams.
Role-based permissions
Multi-brand support
Export and API access
[FEATURES]
Inputs. Decisions. Outputs. Built as a system.
Inputs & inventory
Inventory and data from your existing systems.
CSV, JSON or ERP integration.
Lookbooks and campaign images for brand setup.
Decision Chain
End-to-end evaluation before any result appears.
Transformation planning
Feasibility validation
Cost calculation
CO₂ estimation
Brand alignment
Commercial scoring
Outputs
Production-ready results.
Tech packs
Transformation specifications
Cost and margin breakdown
CO₂ reduction
Multiple options with clear trade-offs.
Brand Identity
A persistent understanding of your brand.
Built from existing assets
Adaptive over time
Controlled exploration
Continuous learning
Access & Team
Built for teams.
Role-based permissions
Multi-brand support
Export and API access
The system works fully on-brand.
[TECHNOLOGY]
Proprietary where it matters.
No dependency on a single AI provider. Foundation models are interchangeable backends. The differentiating core, decision logic, feasibility validation, taste learning, scoring, is fully proprietary and stays with recreategoods.
[CONTACT]