“AI has allowed us to build something we couldn’t before”

“AI has allowed us to build something we couldn’t before: The Cube — our innovation hub connecting global financial institutions with ideating founders. Leveraging no-code AI infrastructure tools like Base44, we were able to finally launch a secure, structured, and highly dynamic platform that would have been too complex or costly to build just a year ago,” said Eyal Redler, Managing Partner at The Garage. “AI hasn’t replaced human judgment, but it has significantly amplified our operational capacity, speed, and strategic ambition.”

Redler joined CTech for its VC AI Survey to share how the technology has impacted the sector. “At The Garage, we view AI not as a sector — but as a horizontal capability, and we expect to see these capabilities penetrate and influence almost every segment of Fintech and Enterprise software,” he added.

Eyal Redler, The Garage

You can read more below:

Fund ID
Name and Title: Eyal Redler, Managing Partner
Fund Name: The Garage
Founding Team: Omer Nagar, Eyal Redler, Shay Dan
Founding Year: 2022
Investment Stage: Pre-seed and Seed
Investment Sectors: AI, FinTech, Enterprise-Tech,

On a scale of 1 to 10, how has AI impacted your fund’s operations over the past year - specifically in terms of the day-to-day work of the fund's partners and team members?

8 - More importantly, AI has allowed us to build something we couldn’t before: The Cube — our innovation hub connecting global financial institutions with ideating founders. Leveraging no-code AI infrastructure tools like Base44, we were able to finally launch a secure, structured, and highly dynamic platform that would have been too complex or costly to build just a year ago. AI hasn’t replaced human judgment, but it has significantly amplified our operational capacity, speed, and strategic ambition.

Have you already had any significant exits from AI companies? If so, what were the key characteristics of those companies?

As we only started three years ago and invest mostly in the earliest stages of enterprise software — which typically demands longer sales cycles — we’ve had no full exits yet. However, several of our AI portfolio companies — including Sedric, Insait, Atlas Invest, AironWorks, Velon, Vayu, Varolio, Pelles, and Tweezr — are already leading startups in their respective verticals.

What they share is not just strong core technology, but also a clear understanding of how to integrate into enterprise workflows and deliver tangible ROI. These teams know how to meet the compliance, security, and accuracy demands of enterprise environments — and that’s what sets them apart.

Is identifying promising AI startups different from evaluating companies in your more traditional investment domains? If so, how does that difference manifest?

Yes. Traditional enterprise-tech startups are often evaluated based on traction, CAC/LTV, or enterprise sales velocity. In AI, we place heavier weight early on the caliber of the technical founding team, access to proprietary data, defensibility of the IP, and their ability to build bridges between novel tech and real business use cases. The bar is higher — both technically and commercially. We also consider how the AI space is evolving and whether the startup’s solution will remain relevant and differentiated in both the near and long term.

What specific financial performance indicators (KPIs) do you examine when assessing a potential AI company? Are there any AI-specific metrics you consider particularly important?

We evaluate both traditional SaaS metrics and AI-specific dynamics. Key KPIs include:

1. Business model quality — e.g., ARR vs. volume-based pricing or hybrid structures
2. Model training cost vs. recurring revenue
3. Data acquisition cost and exclusivity
4. Time-to-value for the customer
5. AI-driven efficiency gains — such as hours saved, decision accuracy, or automation depth.
6. In enterprise settings, we also look at how well the product meets security, compliance, and integration standards, as these are often gating factors for adoption. Another crucial element in these cases is the models’ accuracy along with the potential consequences of incorrect outputs, especially in mission-critical applications.

How do you approach the valuation of early-stage AI startups, which often lack significant revenues but possess strong technological potential?

We focus on a combination of team, TAM, technical defensibility, and customer urgency. A startup with a top-tier technical team, unique “founder-market fit” and strong MOAT, access to proprietary data, and early validation from potential customers can command a premium. However, we actively resist “hype multiples” and prefer founders who are as rigorous about business fundamentals as they are about model performance.

What financial risks do you associate with investing in AI companies, beyond the usual technological risks?

Key risks include:

1. High cloud/infrastructure costs that may make unit economics unattractive
2. Data dependencies (especially if relying on third-party APIs or non-exclusive data sources)
3. Legal/regulatory uncertainty around data privacy, IP, and AI usage
4. Rising competition and commoditization in foundational models
5. Misalignment between model performance and actual business impact

Do you focus on particular subdomains within AI?

Yes, we’re focused on enterprise-oriented subdomains where AI has an immediate business impact. These include:

1. LLMs and natural language enterprise business workflows (e.g., document analysis, structured data extraction)
2. Applied AI in financial services (e.g., fraud detection, reconciliation, underwriting automation)
3. Agentic systems that automate enterprise tasks end-to-end
4. Proprietary vertical models, where performance exceeds general-purpose LLMs by leveraging domain-specific advantages.

How do you view AI’s impact on traditional industries? Are there specific AI technologies you believe will be especially transformative in certain sectors?

AI is already reshaping compliance-heavy sectors like banking, insurance, and healthcare. In particular, agentic AI and autonomous workflows are redefining “back office” functions — reducing manual effort and turnaround time. Technologies we expect to be especially transformative include retrieval-augmented generation (RAG), AI copilots embedded into existing tools, and continuous learning systems for structured environments.

What specific AI trends in Israel do you see as having strong exit potential in the next five years? Are there niches where you believe Israeli startups particularly excel?

Israel excels in applied AI for cybersecurity, Fintech, DevOps, and defensetech. In the next five years, we believe the biggest exits will come from:

1. Enterprise copilots (sales, legal, finance)
2. AI-enhanced security platforms
3. AI infrastructure layers (e.g., model observability, cost optimization)
4. Vertical AI for regulated industries (banking, govtech, healthcare)

Israeli founders often blend deep tech with real-world urgency — which gives them an edge in building “must-have” products.

Are there gaps or missing segments in the Israeli AI landscape that you’ve identified? What types of AI founders are you especially looking to back right now in Israel?

We still see a shortage of founders focused on AI-native products for SMBs, consumer finance, and ops-heavy industries (logistics, agriculture). We’re especially looking to back founders who:

1. Build with real data advantages from Day 1
2. Have GTM instincts as sharp as their model architecture skills
3. Understand how to navigate compliance and regulation in their target market

We're also actively looking for teams that are pushing the boundary of autonomous agents in real-world enterprise environments.