ReCircuit Research
Public research portal for circular economy intelligence.
Whitepaper v2.0, architecture companion, citation metadata, diagrams, and source files for ReCircuit's early-stage AI research direction.
Abstract
The recovery problem is an intelligence problem.
Electronic waste is no longer only a disposal problem. It is a fragmented intelligence problem across product identity, material value, reverse logistics, climate accounting, repairability, and compliance evidence.
ReCircuit proposes an AI platform for material intelligence across the circular economy. The platform vision combines structured device intake, computer vision, forecasting, pricing logic, carbon estimation, ESG evidence, and API-accessible records so that recovery decisions can be made with more context and less ambiguity.
This publication separates current status from future capability. ReCircuit has a public website, research foundation, architecture direction, and prototype work in development. It does not claim customers, revenue, funding, deployments, investors, employees, partnerships, awards, or validated performance metrics.
Version
2.0
Publication
2026
Author
Mohnish M
Status
Research + prototype
Read Online
Explore Whitepaper v2.0 by section.
Global Context
Electronic waste, critical minerals, circularity, reverse logistics, and product data are converging.
Problem Statement
Existing systems are fragmented across visibility, traceability, decision making, and compliance.
Platform Vision
ReCircuit as an AI platform for material intelligence across the circular economy.
Artificial Intelligence
AI should improve decisions while exposing uncertainty.
Enterprise Platform
Different stakeholders need different workflows over the same asset intelligence graph.
Business Model
Revenue mechanisms should follow real workflow value, not speculative projection.
Risks
A credible circular AI platform must identify its own failure modes.
About the Founder
Founder biography, factual and non-resume style.