Technical Consultant (Internship) @ Silverpine, Silverpine GmbH

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TL;DR: Interned at Silverpine GmbH in Berlin, a start-up tokenising vintage and collectible cars through blockchain technology. Advised on product-market fit by evaluating financial offerings against legal/commercial constraints. Built a proprietary dataset of 10,000+ collectible car listings to quantify market dynamics and proved ROI of data analytics through ML-driven insights on investment viability and price volatility.
Key lessons: navigating regulatory grey areas requires deep collaboration across legal and technical teams, data work becomes meaningful when connected to real human passion, and innovation thrives when you balance ambition with pragmatic execution.

🛣️ The Journey

In Berlin, I joined Silverpine GmbH at a fascinating inflection point in both blockchain technology and alternative asset investing. The company's vision was audacious yet elegant: democratise access to vintage and collectible cars by tokenising ownership. Imagine owning a fraction of a 1967 Shelby GT500 or a classic Porsche 911-not as a pipe dream, but as a legitimate investment vehicle accessible through blockchain technology.
As a technical consultant intern, I found myself at the intersection of three complex domains: blockchain infrastructure, European regulatory compliance, and automotive market analysis. Each morning, walking into the office through Berlin's eclectic neighbourhoods, I felt the weight and excitement of working on something genuinely novel.
The work split into two parallel tracks that eventually converged: understanding how to make fractional ownership legally compliant in the EU, and building the analytical foundation to identify which cars deserved to be tokenised.

🏛️ Navigating Compliance

1. 📜 Regulatory Frameworks in Uncharted Territory

The EU's regulatory landscape for digital securities was still evolving when I arrived. We were working in the grey areas—frameworks existed for traditional securities and for traditional fractional ownership, but tokenised fractional ownership of physical assets? That was new territory.
I spent countless hours researching how existing regulations around fractional ownership, digital securities, and asset tokenisation could apply to our model. The challenge wasn't just understanding what the regulations said, but interpreting what they meant in our specific context.
  • Which directives applied? MiFID II? The Prospectus Regulation?
  • How did we classify tokenised car ownership—as securities, as commodities, or something else entirely?
  • What disclosure requirements applied when offering fractional ownership to retail investors?

2. 🤝 Cross-Functional Collaboration & Product-Market Fit

The complexity demanded deep collaboration between legal, technical, and business teams. I learned to translate technical blockchain concepts into language legal experts could work with, and conversely, to understand legal constraints well enough to shape our technical architecture around them.
Some days felt like being a translator between worlds. Our legal team would describe compliance requirements that seemed technically impossible. Our blockchain developers would propose elegant solutions that violated regulatory principles. My role became finding the middle ground—the structures that were both legally sound and technically feasible.
Critically, I advised on product-market fit by evaluating our financial offerings against legal and commercial constraints. This meant assessing which tokenisation structures could attract investors while remaining compliant, which fee structures made economic sense within regulatory boundaries, and which investor protections were both legally required and commercially viable.
We developed recommendations for legal structures that could support compliant partial ownership: SPVs (Special Purpose Vehicles) that held the physical cars while tokens represented economic interests, KYC/AML frameworks that met EU standards, and disclosure documents that balanced regulatory requirements with user accessibility.

📊 Building the Data Foundation

1. 🔍 Data Extraction and Pipelining

Beyond compliance, we needed to answer a fundamental question: which cars should we tokenise? Not every vintage vehicle makes sense as an investment. We needed data—lots of it—and we needed it structured, clean, and analysable.
I built a proprietary dataset of over 10,000 collectible car listings to quantify market dynamics. This involved creating automated systems to extract vintage and collectible car data from disparate sources:
  • Auction house results (Bonhams, RM Sotheby's, Gooding & Company)
  • Dealer listings across Europe
  • Market databases and historical sales records
  • Enthusiast communities and forums
The challenge wasn't just extraction but standardisation. Every source had different formats, different ways of describing condition, different currencies and date formats. I created data pipelines that could ingest this chaos and output clean, structured datasets ready for analysis.

2. 📈 Market Analysis and Investment Thesis

With clean data flowing, the real work began: identifying cars with genuine investment potential. I proved the ROI of data analytics through ML-driven insights on investment viability and price volatility.
I developed an analysis framework that examined multiple dimensions:
  • Historical appreciation trends: Which models had consistently appreciated over 5, 10, 20 years?
  • Price volatility patterns: Machine learning models to identify stability vs. speculative risk in different vehicle segments
  • Rarity and production numbers: Limited production runs often correlated with stronger price performance
  • Market demand indicators: Auction attendance, bidding intensity, time-to-sale metrics
  • Condition and provenance: Original ownership history, restoration quality, documentation completeness
The ML-driven approach allowed us to quantify not just which cars appreciated, but how reliably they did so—critical for building investor confidence in tokenised automotive assets. What made this work meaningful was understanding that behind every data point was someone's passion. A 1973 Porsche 911 Carrera RS wasn't just a data row with appreciation metrics—it was an icon that enthusiasts dreamed about owning. The data helped us identify investment opportunities, but it was the human stories that gave context to the numbers.

3. 💡 From Data to Decisions

The analysis framework I built became core to Silverpine's evaluation process. Before committing to tokenise a vehicle, the team could run it through the framework to understand its investment profile. Was this a car that would appreciate steadily? Did it have the rarity and demand to justify the operational overhead of tokenisation? Did market signals suggest growing or declining collector interest?
This wasn't about eliminating human judgment—automotive investing requires deep expertise and intuition. But data provided a foundation for better decisions and helped us spot opportunities that might have been overlooked.

🌆 Berlin and the Start-up Environment

Working at Silverpine meant immersing myself in Berlin's unique culture—a city where remnants of divided history stand alongside cutting-edge innovation. The juxtaposition felt appropriate. We were trying to bridge traditional automotive heritage with blockchain's digital future, just as Berlin bridged its complex past with an ambitious present.
The start-up environment taught lessons beyond the specific work:
  • Resource constraints force creativity: Limited budget and tight timelines meant I couldn't build perfect systems. I learned to ship working solutions quickly, then iterate.
  • Ambiguity is the default state: In a start-up pioneering new territory, there are no playbooks. Comfort with uncertainty became essential.
  • Execution speed matters: The window for first-mover advantage in tokenised assets was narrow. Every week of delay was a week competitors could catch up.
There were moments of genuine doubt. Data pipelines that broke at 2 AM. Regulatory interpretations that seemed to contradict our entire business model. Technical challenges that demanded creative solutions we weren't sure would work. But pushing through those moments built resilience and problem-solving skills that transcend any specific technology or domain.

🏫 The Lessons

1. 🔗 Innovation Lives at Intersections

The most interesting work happens where domains collide. Silverpine wasn't just a blockchain company or just an automotive investment platform—it was both, and the magic lived in connecting those worlds. I learned to become conversant across disciplines: enough blockchain to architect systems, enough finance to understand investment thesis, enough law to grasp regulatory constraints, enough automotive knowledge to appreciate what made cars valuable.
Specialists are valuable, but the ability to translate between specialisations—to see connections others miss—creates unique leverage.

2. ⚖️ Regulation as Design Constraint

Early in the internship, I saw regulation as an obstacle. By the end, I understood it as a design constraint that made the product better. Regulations exist for reasons—protecting investors, ensuring market integrity, preventing fraud. Working within those constraints forced us to build more robust, trustworthy systems.
The best innovations don't ignore regulation; they figure out how to deliver value while respecting the frameworks designed to protect people.

3. 📊 Data Tells Stories

I learned that data analysis isn't just about numbers-it's about uncovering stories. Behind appreciation trends were stories of cultural shifts in collecting. Behind auction results were stories of passionate communities valuing automotive heritage. Good data work honours both the quantitative rigour and the qualitative context.

4. 🚀 Start-up Intensity is a Feature

The fast pace and resource constraints of start-up life aren't bugs to be fixed—they're features that force prioritisation and rapid learning. I learned more in a few months at Silverpine than I would have in years at a larger, more established company. The intensity is the point.

🎯 The Takeaway

This internship fundamentally shaped how I think about innovation, regulation, and building at the intersection of traditional and emerging industries. The technical skills—data pipelines, compliance frameworks, market analysis—were valuable. But the deeper lessons about navigating ambiguity, collaborating across disciplines, and building responsibly in novel domains continue to inform every project I undertake.
Berlin taught me that transformation is possible when you honour heritage while embracing the future. Silverpine taught me that the most exciting work lives at the frontiers where technology meets tradition, and that building in those spaces requires both technical excellence and deep respect for the systems and people you're working to serve.