Discovery Phase in Product Engineering: Laying the Right Foundation

By Srihari M, Director – Product Development, EurthTech

Published: March 18, 2025

In hardware and embedded systems development, the Discovery Phase isn't a checkbox — it's the blueprint. Without a thorough understanding of the problem space, user pain points, and technical feasibility, even the most elegant design can fail in the real world of smart infrastructure solutions and IoT product engineering.

At EurthTech, we've seen that success downstream always begins with clarity upstream.In this blog — the first in our multi-part Discovery Series — we walk through our structured approach to defining the right problem and building a working Proof of Concept (PoC) for smart city solutions and digital transformation for infrastructure. Each section reflects EurthTech’s co-creation approach to de-risk embedded product development and deliver long-term value to industries adopting AI-powered embedded systems and IoT & embedded services in India.

Part 1: Understanding the Problem Space

Too many teams jump into prototyping with assumptions. At EurthTech, we pause to ask the right questions first — not just “can we build it?” but “should we build it?” That’s the difference between a project that scales in the smart city ecosystem and one that doesn’t.

Why Start with the Problem?

Skipping deep problem analysis often leads to:

• Misaligned features

• Wasted engineering effort

• Poor user adoption

We start with structured validation that ensures alignment across urban infrastructure digitalization, industrial IoT and automation, and AI for smart infrastructure.

Problem Discovery Checklist

• What is the core problem? (Specific, not vague)

• Who experiences it, and how?

• What are users currently doing to work around it?

• What are the measurable pain points?

Example:Instead of saying: “Factories need better maintenance.”We reframe to:“Factory managers struggle to prevent unexpected equipment failure due to the absence of real-time data, leading to production downtime and increased costs.”

This mindset shapes our AI-based predictive maintenance IoT systems and smart pole IoT integrations for connected urban environments.

Part 2: Audience & Market Segmentation

Every IoT and embedded system solution must be built for someone. We segment user personas to match real-world industrial and urban contexts in smart infrastructure engineering.

Persona ExampleName: RajRole: Factory Operations ManagerIndustry: AutomotivePain Points:

• 10% monthly production downtime from unpredictable failures

• No IT support for complex solutions

• Needs a plug-and-play predictive maintenance system using AI and IoT

By building such detailed personas, we align AI-driven product engineering with market realities — not assumptions.

Part 3: Competitive & Industry Landscape

Even a strong idea must find its space in the ecosystem. We analyze competitors across AI for utilities, AI GIS analytics, and industrial IoT domains.

Competitor	Key Features	Gaps / Weaknesses
Company A	Wireless, AI-powered insights	Expensive, complex integration
Company B	Low-cost, simple to deploy	Weak analytics, not scalable
Company C	Great industry backing, strong HW	High maintenance burden

Our differentiation becomes clearer when we position EurthTech as an AI product engineering company in India delivering custom embedded software development and smart city technology solutions.

Part 4: User and Market Research

Once the problem is scoped, we validate assumptions through direct user engagement — especially in IoT and embedded engineering services for smart cities.

1. User Interviews

We ask open-ended, story-driven questions such as:

• “Tell us about the last time a machine failed unexpectedly.”

• “What were the consequences? How did you respond?”

• “If you could change one thing about your current setup, what would it be?”

From 10 factory manager interviews, 7 revealed real-time visibility as their top unmet need. This shaped our AI-powered embedded IoT devices for smart lighting systems and industrial monitoring.

2. Surveys for Scale

Good survey = concise, balanced, targeted.

Sample Questions:

• Rate (1–5): How often does unplanned downtime occur?

• Annual maintenance cost range?

• Key features in predictive IoT devices?

• Primary concern in adopting new tech? (e.g., Cost, Integration, Reliability)

When 80% of responses flagged cost as a barrier, we refocused on affordable, modular IoT products for smart infrastructure and utilities.

3. Early Prototype Feedback

Before building a full device, we create:

• Paper prototypes for UI

• 3D-printed enclosures for fit testing

• Firmware-on-eval boards for proof of function

A prototype smart pole IoT module revealed signal failures in concrete-rich environments. We added an offline fallback feature — a key lesson for edge AI embedded systems in field deployments.

4. Market Trend Scanning

We tap into:

• Analyst reports (Gartner, McKinsey)

• Industry forums

• Tech expos and webinars

• Patent filings and public roadmaps

In 2024, predictive maintenance using AI and IoT ranked as a top industrial investment area — helping us shape our AI-enabled geospatial analytics and GeoAI-based smart city solutions.

Part 5: Defining the Product Vision & Strategy

With research in hand, we anchor the product to a strong vision and turn it into a development-ready strategy that aligns AI, IoT, and embedded systems.

🌟 Sample Vision Statement

“To empower factory operators with real-time predictive insights via an affordable, self-installable IoT sensor network — cutting unplanned downtimes and improving operational efficiency through AI-powered smart infrastructure.”

💡 Value Proposition Table

Feature	User Benefit	Our Advantage
Plug-and-play sensor	Easy setup, no IT needed	Faster deployment than rivals
AI-based analytics	Downtime prediction	More accurate than rule-based tools
Battery-operated wireless	Flexible placement	No wiring or external power needed

Part 6: Setting Success Metrics

Clear metrics mean clearer decisions — essential for digital twin smart city systems and AI in GIS analytics.

Market KPIs

• 30% of target users onboarded in 6 months

• NPS > 50 from pilot users

Technical KPIs

• 98% device uptime

• Predictive model accuracy >85%

Business KPIs

• ROI for customer within 9 months

• 25% lower maintenance costs post-deployment

Part 7: Product Roadmap (Sample)

Phase	Goal	Timeline
Discovery	Finalize problem and personas	Month 1
Prototype Development	Build working MVP	Months 2–3
Validation & Testing	User trials, feedback	Months 4–5
Pilot Deployment	Small-scale release	Month 6
Mass Production	Full-scale rollout	Months 8–12

Part 8: Proof of Concept (PoC) Development

This is where strategy meets execution. A PoC is not a "lite product" — it’s a confidence-building checkpoint.

Goals of PoC

• Can the tech work under field conditions?

• Will users interact as we expected?

• What will it really cost to build and scale?

PoC Types

PoC Type	Best For
Hardware Prototype	Sensors, embedded systems
Software Simulation	Algorithms, cloud integration
Mechanical Models	Form-fit ergonomics, enclosures
UX Wireframes	UI feedback, interaction design

For our smart sensor system, we built a hardware PoC using ESP32 with LoRa, and a software PoC for real-time data streaming — demonstrating how AI-powered embedded systems can enhance urban infrastructure.

PoC Evaluation Criteria

Metric	What It Evaluates
Technical Feasibility	Can it run reliably under constraints?
User Validation	Do users find it helpful and usable?
Cost Viability	BOM and assembly cost realism
Scalability Potential	Ready for thousands of units?

Decision Flow After PoC:

• Proceed to Engineering

• Refine & Retest

• Pivot or Drop

Final Thoughts

The Discovery Phase is where great smart infrastructure solutions are born — or flawed ones are caught early.At EurthTech, we treat Discovery not as pre-sales fluff, but as the strategic foundation of AI-driven engineering success. It aligns teams, saves cost, de-risks execution, and sets the tone for everything that follows — from embedded AI systems to AI-powered smart poles and geospatial analytics for smart cities.

If you're building a product that must succeed in the field — not just the lab — we’d love to talk.

Need help turning your product idea into a validated, working prototype?

👉 Talk to EurthTech — your smart city solutions provider and AI engineering partner in India.