Thesis

The idea emerged from an insight I gained while working at the Ministry of Finance, where I saw the massive technological gap in the logistics and customs brokerage sectors. Global shipping companies were investing heavily in human capital for high-volume, repetitive data-entry tasks, despite the availability of modern AI solutions. The industry’s conservative nature (driven by strict regulations, risk aversion, and entrenched procedures) kept innovation slow.

I became passionate about addressing two major pain points in the brokerage world. The first was complexity of classification: a highly specialized challenge (e.g., differentiating between 14 types of “tomatoes” in the customs book) that affects fewer people and is handled by expert brokers. The second, significantly larger pain point was excessive manual labor: tens or hundreds of employees performing manual data entry, generating the bulk of operational costs.

Our vision was to use AI to tackle this broader problem first by creating a software layer that automates form entry and eliminates manual work, offering large firms a clear and measurable cost-saving solution.

Validation

We adopted an aggressive, sales-first validation strategy.

I led the discovery process and conviction-building efforts, and within two weeks, conducted 30 discovery calls, primarily through LinkedIn outreach to industry leaders such as the former CEO of DHL Israel and the Head of Customs Brokerage at DSV Israel (the world’s largest shipping company). Surprisingly, the cold outreach strategy worked extremely well.

The enthusiasm from Israeli industry leaders was immediate. Many shared deep insights into processes and workflows and made personal introductions that helped expand our understanding.

We targeted the U.S. market early, seeing it as large and more uniform. Through network connections, we reached the head of Innovation at DHL, and head of customs brokerage US. as well as the DHL JFK former branch manager, generating strong commercial interest and initial PoC discussions.

We then built a demo using AI tools: users uploaded shipping documents (invoice, bill of lading), and the system automatically populated the corresponding fields in logistics systems like CargoWise. A key learning emerged: this pain point was meaningful primarily for large shipping companies (DHL, DSV, MERSK). Smaller players lacked the volume and budgets to justify the investment.

We received many positive signals and clear willingness to pay.

Why it didn’t progress

1. Mismatched Business Model: We realized that the TAM for the core, painful problem (manual data entry in customs brokerage) was too small for a VC-backed business. An investor’s detailed analysis showed that even capturing the top five firms at high con

2.  Founder Vision vs. Market Reality: The founder wanted to build a fast-scaling, high-growth startup. The customs brokerage domain, while viable for a profitable bootstrapped or slower-growth business, fundamentally did not align with VC-scale ambitions.

3. Commercial Traction Was Misleading: Strong early interest from major firms (e.g., a DHL manager offering to “push me in the organization”) created a false sense of momentum, distracting us from the deeper question of overall market size.

4. Avoiding Hard Questions: Concerns about market size remained in the “back of my mind.” In retrospect, I should have confronted the core question immediately: Is this a VC-scale business, or should it be a bootstrapped company?

Insights

1. VC Fit vs. Bootstrapped Potential: Not every profitable idea is a VC business. Founders must decide early whether they’re building a high-growth startup or a stable bootstrapped operation, market size requirements differ dramatically.

2. Validate Market Size Immediately: Early commercial excitement (“Where do I sign?”) can be deceptive. If doubts about market size arise, they must be addressed immediately, letting them linger drains motivation and clarity.

3. Leverage Networks for Access: Direct LinkedIn outreach, especially to high-ranking executives and domain experts, proved highly effective in this industry.

4. External Investor Feedback is Essential: Engaging with potential investors, even informally, provides necessary external perspective on business model viability and market scale.

5. Clarify the Real Buyer: Targeting logistics forwarders was far more effective than targeting importers; closing one forwarder unlocks many importers, creating leverage.

6. Geography Focus: While local (Israeli) validation went very well, in hindsight we should have moved to U.S. validation much earlier. The U.S. market dynamics would have surfaced the true conclusions faster.

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