What is Concept-as-a-Service?

Concept-as-a-Service is a pay-on-acceptance model where Model T proactively generates hardware product concepts for engineering companies. Clients provide 5-7 target accounts, Model T selects the top 3 and delivers 2-3 validated product concepts per client — each with system architecture, BOM, and go-to-market angle — in 2 weeks. Payment only if at least one concept is accepted.

How does Model T work?

Model T uses a 6-stage AI-enhanced pipeline: Qualify (select top 3 from 5-7 accounts), Problem Map (OSINT research), Signal Research (market intelligence), Concept Engineering (architecture + BOM), Validate (expert feasibility review), and Deliver (client-ready package). Total ~50 hours per client, 1-2 weeks elapsed.

What is the cost of Model T?

From €15,000 per client engagement. Pay-on-acceptance: if zero concepts are accepted by the client, cost is zero. Approximately 60% of concepts have led to signed engineering projects.

What is Model T's conversion rate?

Model T achieved a 75% positive response rate during its validation roadshow across Munich and Switzerland — 7 meetings with zero negative reactions. Two meetings converted directly into signed projects.

Model T serves two audiences: semiconductor distributors and vendors who want proactive design-ins instead of reactive RFQ responses, and engineering or product companies who want validated concept exploration before committing R&D budget. Official vendor partners include Renesas, Lattice, NXP, STMicro, Infineon, and Qualcomm.

REAL PILOT OUTPUT — ANONYMIZED

What You Receive

Every Model T engagement produces three key documents: a product concept with architecture and BOM, meeting preparation notes, and a mini-offer deck.

Below are real documents from a pilot engagement with a professional imaging equipment manufacturer. Client identity, stakeholder name, concept names, and all identifying details have been anonymized.

Product Concept Document

Code: MT-PC-007 · Industry: Industrial Monitoring & Control · Target FPGA: CertusPro-NX (Lattice)

Executive Summary

Edge-computing predictive maintenance module for industrial rotating equipment. Combines vibration analysis, thermal profiling, and power-quality monitoring in a single ruggedized unit — replacing 3 separate sensor systems with one integrated solution. FPGA-based signal processing enables real-time anomaly detection at the edge without cloud dependency.

Context & Market Gap

Industrial facilities spend $50B+ annually on unplanned downtime. Current monitoring solutions require cloud connectivity, multiple sensor vendors, and specialized integration. The market lacks an all-in-one edge device that combines sensing, processing, and actionable output.

No existing product combines vibration + thermal + power-quality monitoring with on-device ML inference in a DIN-rail form factor under €800 BOM cost.

— Unplanned downtime costs industrial plants $20K–$250K per hour depending on sector

— Existing solutions require 3–5 separate sensor systems + cloud gateway + subscription

— Plant operators want actionable alerts, not raw data dashboards

— Regulatory push (EU Machinery Regulation 2027) demands predictive safety capabilities

— Target customer: mid-size manufacturing plants, 50–500 monitored assets

Solution — Before vs After

Integrated edge-AI monitoring module with three sensing modalities, FPGA-based real-time processing, and local decision engine. Outputs actionable maintenance recommendations directly to plant SCADA/MES without cloud dependency. Optional cloud sync for fleet-level analytics.

Dimension

Before

After

Sensor systems

3–5 separate devices, different vendors

1 integrated module, single vendor

Data processing

Cloud-dependent, 5–30 min latency

Edge processing, <500ms to alert

Installation time

4–8 hours per monitoring point

<45 minutes per monitoring point

Annual cost per point

€2,400–€4,000 (hardware + subscriptions)

€800 one-time + €120/year optional cloud

Anomaly detection

Threshold-based, high false-positive rate

ML-based pattern recognition, <2% false positives

Integration effort

Custom per-site, weeks of setup

Plug-and-learn, auto-baseline in 72 hours

High-Level Architecture

Four-layer architecture: Sense → Process → Decide → Act. All layers operate independently of cloud connectivity. FPGA handles real-time signal conditioning and feature extraction; MCU runs ML inference and business logic; communication module handles SCADA/MQTT/OPC-UA output.

Sensing Layer

3-axis MEMS accelerometer (±16g, 6.4kHz), IR thermal array (8×8, 60Hz), 3-phase power analyzer (0.5% accuracy)

Processing Layer

Lattice CertusPro-NX FPGA — FFT, envelope analysis, thermal gradient computation, power quality metrics at wire speed

Intelligence Layer

ARM Cortex-M7 MCU — TinyML inference engine, anomaly classification, remaining useful life estimation, adaptive baseline

Communication Layer

Industrial Ethernet (PROFINET/EtherNet-IP), MQTT, OPC-UA, Modbus TCP. Local web dashboard. Optional 4G/LTE uplink

Bill of Materials (Budget Estimate)

Budget-grade BOM for 1K-unit production run. Costs decrease 15–25% at 10K+ volumes.

Meeting Preparation Notes

Present two product concepts and discuss pilot engagement. Industry: Professional Imaging Equipment. Stakeholder: VP of R&D.

1. Company Context

Premium professional imaging equipment manufacturer. Industry leader with strong brand heritage and engineering excellence.

— Market leader in professional imaging — premium positioning, strong brand loyalty

— Mid-size enterprise, ~1,500 employees globally

— Core strengths: image quality, engineering precision, product reliability

— Current challenge: revenue stagnation, margin pressure from market shifts

— Key gap: limited AI/ML capabilities, no cloud-native solutions, no recurring revenue model

— Risk: losing market share in fast-growing automation segments

2. Stakeholder Profile — How to approach

VP of R&D

Type

Process-oriented leader, academic background in management

Values

Structured frameworks, measurable metrics, managed risk through staged approach

Style

Methodical — processes over technologies. Prefers governance models with clear milestones

Selling approach

— Lead with processes and metrics, not technology features

— Present ready collaboration frameworks with governance

— Show understanding of organizational challenges (R&D coordination)

— Avoid "technology jazz" without business context

Concept A — Primary

The Hook

"We studied your situation and noticed a gap: [Client] makes the best hardware in the world, but end users lose efficiency through manual operations. No premium vendor combines your quality with autonomous AI control."

Problem & Vision

"Today operators depend on manual processes — fatigue, errors, cost. [Client] risks losing share in growing automation segments. But imagine a system where [Client] products operate autonomously, coordinate with each other, and generate monthly recurring revenue for software and analytics."

Solution

"An edge-AI and robotics system integrated with your products. Autonomous operation, multi-unit coordination, cloud analytics, human override. Not just automation — a complete intelligent layer for your entire product line."

Proof

"We've built: autonomous robotics with real-time control (<100ms latency), automotive vision systems with safety certification (ISO 26262). Structured processes for AI integration with premium equipment."

Proposal

"6-month pilot. Hardware + AI + integration. Target: >95% accuracy, MVP readiness, confirmed references. Staged approach with managed risk through clear milestones."

Concept B — Secondary

Context

"In the era of synthetic media, 'seeing is believing' has disappeared. Professional users cannot trust digital files without cryptographic proof of authenticity."

Solution

"A hardware verification module integrated into your products. Cryptographically signs every output from source. Any post-processing modification breaks the digital signature — instant automatic verification."

Value

"Brand protection as the industry trust standard. Entry into verification-sensitive markets — legal, archival, news. New monetization through verification certificates. First-mover advantage — no premium vendor has made this a standard yet."

Objection Handling

"This is too risky — we're not ready for such changes"

→ Staged approach: PoC → Pilot → Scale. Managed risk with clear milestones. Start small — 6-month pilot, limited scope.

"We don't have internal resources for integration"

→ Engineering-as-a-Service: we handle integration, you get a ready solution without diverting core R&D resources.

"How will this affect our existing processes?"

→ Ready collaboration frameworks. Incremental integration into existing workflows. No disruptive changes — structured approach with traceability.

Closing question

"How does this align with your plans for R&D acceleration and transition to recurring revenue?"

Mini-Offer Deck

7-slide presentation prepared for the first meeting. Actual deck includes client-specific visuals and architecture diagrams.

01Title & Agenda

— Tailored title: "[Client]: [Solution Domain] & [Value Platform]"

— Subtitle: "Prepared specifically for [Client]"

— Agenda: Context → Concept A → Concept B → Next Steps

02Context — The "Why"

Professional operations are still tied to manual labor

— Operators depend on manual processes — fatigue and errors

— [Client] risks losing share in growing automation segments

— No premium vendor combines [Client]-grade quality with AI

— Vision: autonomous products + recurring revenue model

03Concept A — Solution

AI Automation System

— Edge AI core: >95% accuracy, <100ms latency

— Native integration with [Client] product ecosystem

— Multi-unit orchestration with intelligent handoff

— Cloud control & analytics: SaaS model

One-time sales → subscription. Margin growth + ecosystem protection.

04Concept A — Proof

— Architecture: Edge AI → Control → [Client] API → Cloud

— Reference: autonomous robotics, real-time navigation

— Reference: automotive vision, ISO 26262 certified

— Proven integration with premium equipment

05Concept B — Expansion

Verification Platform

— Trust crisis in the era of synthetic media

— Hardware cryptographic module in [Client] products

— Signs every output — any modification breaks the chain

Brand protection, new markets, certificate monetization.

06Concept B — Details

— Architecture: Sensor → Processing → Verification → Signed Output

— Synergy: both concepts strengthen [Client] ecosystem

— Concept B protects content created with Concept A

07Next Steps

Start small, scale with confidence

— 6-month Concept A pilot on limited scope

— Discuss Concept B expansion after successful pilot

— Technical workshop with structured collaboration framework