Atlas Invest CEO Tal Shahar shares his journey from markets and deep tech to rebuilding real estate lending with AI-driven institutional-grade underwriting.
Tal, could you share your professional journey and what led you to co-found Atlas Invest?
Answer: My career has consistently revolved around understanding risk, building systems, and allocating capital. I began investing in the stock market at a young age, which sparked a long-term interest in how markets behave, how information is processed, and how disciplined decision-making compounds over time.
In parallel, I built a strong foundation in software and data-driven systems. Over the years, I also spent significant time speaking with and advising hundreds of people about investing, from early individual investors to more sophisticated capital allocators. Those conversations helped me understand how different investors think about risk, transparency, and decision-making, and where friction and confusion repeatedly appear.
I later joined founding Deep Insight, a venture capital firm focused on deep technology across multiple stages. Working closely with founders building highly technical platforms sharpened my understanding of how long-term infrastructure is created, how defensibility is built, and how breakthrough technology moves from research into real-world deployment.
Atlas Invest emerged from connecting these experiences. Real estate lending is one of the largest financial markets globally, yet its core infrastructure has barely evolved. Underwriting, execution, and capital allocation remain largely manual and fragmented. Atlas was founded with the conviction that this is fundamentally an infrastructure opportunity, and that AI can be used to rebuild the operating system behind real estate financing, enabling institutional-grade decisions at software speed.
How did your early experiences in the stock market and software development shape your approach to fintech innovation?
Answer: Investing early taught me how markets behave in real time, how risk is priced, and how emotions and information gaps drive outcomes. Software development taught me the opposite side of the equation, how systems behave, how automation scales, and how good infrastructure quietly outperforms human-heavy processes over time.
At Atlas, we combine both perspectives. We do not treat AI as a black box or a marketing layer. We treat it as infrastructure. The goal is not to replace judgment, but to systematically surface better information, faster, and at a scale that humans alone cannot reach.
What motivated you to transition from venture capital at Deep Insight to founding a fintech startup focused on real estate lending?
Answer: The transition was driven by a growing desire to move from observing and supporting innovation to building it directly. Working in venture capital at Deep Insight meant being close to deep, foundational technologies, often at very early stages, and seeing how long-term platforms are constructed with patience and rigor.
At the same time, I became increasingly drawn to real estate lending as a problem space. It is one of the largest and most capital-intensive markets in the world, yet the core decision-making processes have changed very little. Underwriting, risk assessment, and execution are still heavily dependent on manual work, fragmented data, and individual experience.
What ultimately motivated the shift was the realization that this was not a question of incremental improvement, but of rebuilding infrastructure. I felt that applying deep technology and AI to the heart of credit decision-making could unlock a fundamentally better system for both borrowers and investors. Founding Atlas Invest was about taking responsibility for that vision and committing fully to building it from the ground up.
How did your time in the IDF’s Counter Terror Unit influence your leadership style and approach to teamwork at Atlas Invest?
Answer: My time in the IDF’s Counter Terror Unit shaped how I think about responsibility, trust, and execution in complex environments. You learn very quickly that outcomes depend on preparation, clarity of roles, and the ability to make sound decisions under pressure, often with incomplete information.
That experience influenced my leadership style at Atlas Invest in a practical way. We focus on small, highly capable teams, clear ownership, and strong alignment around objectives. People are empowered to make decisions, but they are also accountable for outcomes.
It also reinforced the importance of teamwork and shared context. In high-stakes environments, success is never individual. At Atlas, we invest heavily in processes, communication, and tooling so that teams operate cohesively, decisions are repeatable, and execution remains consistent as the company scales.
Atlas Invest uses AI to enable underwriting that is reportedly 700x faster. Can you explain how your platform achieves this and what sets it apart from traditional methods?
Answer: Traditional underwriting is built around human bottlenecks. Analysts manually collect documents, request third-party reports, run comparable analyses, and piece together borrower and asset risk across disconnected systems. That approach is inherently slow and difficult to scale.
Atlas was designed differently. Our platform uses proprietary AI engines that ingest deal data, market data, borrower history, and third-party sources in parallel. Instead of starting with a blank page, each deal enters the system with a pre-built credit and risk profile. The AI performs market normalization, comparable analysis, scenario testing, and consistency checks before an analyst reviews the deal.
This shifts the analyst’s role from data gathering to decision-making. Humans focus on validation, judgment, and edge cases, while the system handles scale, speed, and consistency. The result is underwriting that is orders of magnitude faster, significantly deeper in analysis, and more repeatable than traditional methods, without compromising credit discipline.
What are the latest trends in commercial real estate lending that professionals should be aware of?
Answer: Commercial real estate lending is in the middle of a structural transition, driven primarily by changes in capital availability, risk appetite, and regulatory pressure.
One of the most significant developments is the shift by traditional banks away from small and mid-size commercial bridge loans. Many banks are reallocating capital toward consumer products and longer-duration lending, and away from short-term, transitional commercial loans that require intensive underwriting and ongoing management. This has created a growing financing gap in the market.
At the same time, risk awareness has increased across the industry. Lenders and investors are placing greater emphasis on fraud prevention, borrower verification, and consistency of underwriting, particularly as deal velocity increases and markets remain volatile. Strong controls and verification processes are becoming essential rather than optional.
Looking forward, there is also a clear directional shift toward more data-driven decision-making and the gradual adoption of AI. While still early, the industry is recognizing the need for better data, deeper analysis, and more scalable processes to manage risk and operate efficiently. This transition is uneven, but the trajectory is clear.
Taken together, these dynamics are reshaping the market, with private capital playing a larger role and operational discipline becoming a key differentiator across lenders.
Can you share best practices for CRE professionals navigating borrowing in today’s market?
Answer: In today’s market, the most important decision for borrowers is choosing the right capital partner, not just the cheapest one. Pricing matters, but certainty of execution matters more. Working with a lender who has clear decision-making authority and a repeatable process can significantly reduce execution risk.
Borrowers should look for partners who understand that real estate projects rarely go exactly according to plan. The ability to navigate extensions, refinances, or unexpected challenges is often more valuable than aggressive initial terms. A lender’s track record in managing loans through different scenarios is a strong indicator of how they will behave when conditions change.
Alignment is also critical. Understanding how a lender holds risk, whether they rely on syndication, and how decisions are made post-closing can help avoid friction later. In a more selective market, long-term reliability and partnership are often the difference between a smooth outcome and a disrupted one.
In what ways does combining human expertise with proprietary AI create value for both borrowers and investors on your platform?
Answer: The value comes from using each side where it is strongest. AI excels at speed, consistency, and scale. It can process large volumes of data, run comparable analyses, test scenarios, and surface patterns that would be difficult or time-consuming for humans to identify. Human expertise brings judgment, context, and accountability.
For borrowers, this combination translates into faster decisions, clearer feedback, and fewer surprises late in the process. The system creates consistency and transparency, while experienced professionals handle nuances and edge cases.
For investors, it means deeper and more repeatable risk analysis, better portfolio construction, and ongoing monitoring that does not degrade as volume grows. Human oversight ensures discipline and accountability, while proprietary AI enables scale and continuous improvement. Together, they create a process that is both efficient and institutionally robust.
What advice would you give to aspiring entrepreneurs and professionals looking to innovate in the fintech and real estate sectors?
Answer: Focus on solving real structural problems, not just building features. Fintech and real estate are complex, regulated markets, and meaningful innovation usually comes from rebuilding underlying infrastructure rather than layering new interfaces on top of existing processes.
Spend the time to deeply understand the workflows, incentives, and constraints of all stakeholders involved. The best solutions respect those constraints and improve outcomes within them, rather than trying to bypass them.
Finally, build with scale and durability in mind from the start. These markets reward long-term thinking, operational discipline, and trust. Companies and professionals who prioritize consistency, transparency, and resilience tend to create the most lasting impact.
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