Our concept note describes 8 capabilities across identity, IoT, supply chain, authentication, integration, and AI. This analysis decomposes the platform into buildable layers, estimates effort honestly, and recommends a phased approach that's fundable before it's finished.
Layer Decomposition
The concept consists of 5 distinct systems that must interlock. Each has its own complexity profile, dependencies, and risk surface. Mapping concept's 8 capabilities → 6 engineering layers:
Normalizes barcode, QR, secure QR, NFC, RFID/EPC into a single resolvable master identity. Handles ID generation, format detection, carrier linking, and GS1 Digital Link compliance. This is the foundation everything else depends on.
Real-time capture from RFID readers, QR scanners, NFC taps, and PUF cameras. Handles high-throughput bulk reads (1000+ items/sec from shelf antennas), deduplication, sequencing, and durable event storage. Factory-floor conditions: intermittent connectivity, dirty data.
Models the directed acyclic graph: textile roll → cut parts → garment → carton → logistics → retail → consumer. Parent-child relationships, batch splitting/merging, location tracking at each transformation step. Core data model for DPP compliance.
Verifies product authenticity via Yometel's cipher-embedded secure QR + PUF matching. Tracks scan history globally, detects anomalies (grey market, counterfeits, suspicious patterns). Feeds intelligence back to brands as dashboards and alerts.
REST + GraphQL APIs for ERP/SAP/PLM systems, DPP apps (including Yometel's own), brand apps, and third-party analytics. OAuth2 multi-tenant auth, rate limiting, webhooks, and a developer portal. The monetization surface.
The "later" layer. Demand forecasting from scan patterns, inventory distortion detection, ESG/waste analytics, and operational optimization. Requires 6-12 months of data accumulation before it delivers real value. Don't build this first.
Component Effort
T-shirt sizes with complexity rationale. These estimates assume a competent team familiar with the domain.
| Component | Size | Weeks | Complexity | Why |
|---|---|---|---|---|
| ID Resolution Service | M | 5-6 | Multi-format parsing (EPC, GTIN, SGTIN, QR payloads) + GS1 Digital Link resolver + mapping table. Well-defined problem, moderate edge cases. | |
| Event Ingestion Pipeline | L | 8-10 | Bulk RFID reads at factory scale, intermittent connectivity, dedup, ordering guarantees. Needs edge agent for offline-first. Hardware integration unknowns. | |
| Genealogy Engine | L | 8-10 | Graph data model for multi-level BOM, batch split/merge, location transitions. EPCIS 2.0 event mapping. Most complex domain modeling in the platform. | |
| Auth & Intelligence | L | 8-10 | Depends on Yometel's cipher spec (black box?). PUF matching is ML-grade. Anomaly detection needs training data that doesn't exist yet. High uncertainty. | |
| API Platform | M | 6-8 | Standard REST/GraphQL, multi-tenant OAuth2, rate limiting, webhooks. Well-understood patterns. Complexity comes from breadth of integrations (SAP, PLM, DPP). | |
| AI & Analytics | XL | 12-16 | Demand forecasting, inventory distortion, ESG scoring: each is a standalone ML project. Data dependency: needs ingestion data first. Defer. | |
| Admin Dashboard | M | 6-8 | Multi-tenant brand portal, traceability explorer, scan analytics, config management. Standard SaaS UI but domain-heavy. | |
| Edge Agent (Factory) | M | 4-6 | Offline-first data collector for factory floor. Local buffer, sync on connectivity, RFID reader protocol adapters. Deploy to low-spec hardware. |
Recommended Phasing
The critical insight: The full scope requires funded-scale investment. Solution: build a fundable MVP in Phases 1-2 that proves the thesis with earmarked budget, then use traction to unlock Phase 3-4 funding.
Critical Design Decision
A "Common Master Code" (CMC) is a proprietary 17-digit ID that unifies all carrier formats. This is the single most important architectural decision in the platform. Get it wrong, and adoption stalls.
Create Yometel's own 17-digit master code as the canonical ID.
Use GS1 DL as the canonical external ID. Maintain an internal resolution layer that maps any carrier to the GS1 DL.
The moat isn't the ID format - it's the resolution layer. The ability to accept ANY input (barcode, QR, NFC, RFID, legacy codes from Bangladesh factories) and resolve it to a standardized, item-level digital identity - THAT is what no one else does well. Build the intelligence in the mapping, not the format. Fragmented IDs across sectors are the problem. The solution is a universal resolver, not a universal replacement.
Risk Register
Ordered by likelihood × impact. These aren't hypothetical — they're drawn from the 5 meetings of conversation history between Kshitij and Koji.
The concept note describes 8 capabilities. Building all 8 simultaneously would require 15+ engineers and 18+ months. Pre-funding, this isn't viable.
RFID reader protocols, edge conditions in factories (heat, dust, intermittent power), Yometel's proprietary cipher spec - all are black boxes until we're on-site.
Legacy systems, manual processes, inconsistent barcode usage. The "ingest" layer must handle extremely dirty data from developing-market factories.
If Phase 2 completes but funding doesn't materialize, development stalls.
EU DPP mandates start Jan 2027, but detailed technical requirements (data schemas, verification protocols) are still being finalized.
Physically Unclonable Function via micro-lens smartphone photography is cutting-edge. No proven at-scale deployment exists for textile authentication.
Team Composition
Phase 1-2 team (MVP funding required). This is the core team that builds the MVP. Roles scale up in Phase 3-4.
Phase 1-2 total: 7 people · 7 months ·
~49 person-months
Phase 3 adds: +1 ML engineer, +1 backend, +1 security engineer = 10
people
Phase 4 adds: +2 ML engineers, +1 data engineer = 13 people at peak
The platform is real and the timing is right - DPP creates the regulatory forcing function. But the concept note is 3x bigger than what should be built first. The approach: build the identity resolver + event pipeline + genealogy engine in 7 months, prove DPP compliance at one factory, and use that to unlock the funding for intelligence and AI layers.