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Your career spans India, Europe, and North America. Could you walk us through your journey and explain how you first discovered the Search Fund model?
My career has sat at the intersection of operating, investing, and growth, shaped by a simple question I kept encountering in real operating environments: how do capital, operators, and systems actually work together to produce durable results over long periods of time?
I began my career in India, moved to Europe for graduate studies in the UK, and later relocated to North America. Along the way, I worked across sales, customer success, and operating roles in institutional settings. Each transition sharpened my view of the same issue: decisions often look sound at the strategic level, yet break down once they reach the operating floor.
During graduate school in the UK, I had the opportunity to work with a search fund. That experience introduced me to the model in a practical way. What stood out was the structure of accountability. Capital was paired with a single operator who was responsible for outcomes over years, not quarters. The focus was on acquiring established businesses, improving them methodically, and compounding value through operational discipline.
Watching that model applied with rigor—clear underwriting, long-term thinking, and real governance—changed how I viewed entrepreneurship. Acquiring a profitable, cash-generating business and improving it systematically struck me as a more rational way to build lasting value than starting from zero while absorbing avoidable risk and dilution.
Over time, it became clear that this was one of the most straightforward and honest frameworks for building and stewarding lower middle market businesses.
Five years later, you launched your own search fund in Canada. What motivated that decision, and how would you describe your experience?
Canada was a deliberate choice.
The fundamentals were strong: a large population of founder-owned businesses, increasing succession pressure, reliable financing markets, and, at the time, less competition than major U.S. markets. From a structural standpoint, the environment made sense.
The search itself was demanding. I sourced actively, built relationships with brokers and advisors, and evaluated opportunities across multiple sectors. Canada has many high-quality businesses and a predictable regulatory landscape.
What I underestimated was how institutionalized the ecosystem is. While proprietary search is encouraged in theory, access is often intermediated. Credibility develops slowly through repeated interaction. Advisors, capital providers, and established networks play a significant role.
Sourcing was not the constraint. The real challenge emerged after signing letters of intent. That is where timelines tighten, stakeholders multiply, and execution risk increases. Managing that transition ultimately defined the experience.
Looking back at your search, what would you do differently – and what advice would you give someone starting today?
Three core lessons, all centered on execution discipline.
First, build credibility before you build scale.
Early in a search, it’s tempting to involve many stakeholders to keep options open. In practice, that creates friction when decisions need to move quickly. If I were starting again, I would anchor the process around one or two highly credible partners early—people who can evaluate decisions in real time and move decisively.
Their value isn’t just capital. It’s pattern recognition under pressure and credibility with sellers and advisors. A tight inner circle that can act quickly reduces perceived execution risk far more than a broad group that requires consensus.
Second, professionalize your process infrastructure from day one.
During my search, I relied on a duct-taped stack of tools – data rooms, spreadsheets, and long email threads. That approach works when activity is slow. It breaks the moment velocity increases.
Having a single, structured source of truth for all deal-related information is strategic, and not merely an administrative overhead. When stakeholders have continuous visibility into risks, assumptions, and open items, decisions move faster and confidence holds. Build this infrastructure while your search is still manageable. Once you sign an LOI, velocity increases dramatically. Infrastructure that scales beats improvised processes that fracture under pressure.
Third, understand that LOI-to-close is where deals actually fail.
Sometimes diligence reveals issues you can’t underwrite – financial risks, customer concentration, key-person dependency. Sometimes external factors intervene – seller hesitation, financing shifts, market conditions. But many deals fail for a simpler reason: process breakdown.
Timelines slip. Communication fragments. Stakeholder confidence erodes. You can’t control everything diligence uncovers, but you can control how quickly information moves and whether coordination holds under pressure. Post-LOI communication should be treated as a core operating function, not an afterthought. When updates lag, confidence declines – usually because clarity is lost.
Finally, compete where you have a structural advantage.
If you’re up against buyers who can close in 30 days with certainty, speed alone is rarely your edge. You need a differentiated advantage – proprietary access, deep sector expertise, or situations where operating credibility outweighs rapid deployment. Don’t fight battles you’re not positioned to win. Choose your competitive ground deliberately.
Are investor expectations of searchers changing?
Yes, and in specific ways.
Five years ago, investors evaluated searchers primarily on deal selection and financial modeling capability. Today, they’re equally focused on process execution and systems thinking.
The shift reflects a broader maturation in the ecosystem. Operational improvements that once differentiated strong searchers – implementing CRM systems, improving pricing discipline, professionalizing sales – are now table stakes. Investors assume you’ll do these things. The question is whether you can identify, prioritize, and execute improvements systematically, or whether your value creation thesis depends on one-time fixes.
What distinguishes searchers now is how they operate under information constraints and time pressure. Investors watch closely during the LOI-to-close phase because it reveals leadership capability in compressed form: How is diligence structured? How are risks surfaced and communicated? How is stakeholder alignment maintained when complexity increases?
The searchers who stand out treat their search process itself as an operating business. They build systems early – for deal tracking, investor communication, diligence management – not because they enjoy process, but because they understand that execution credibility compounds. An investor who sees structured thinking during the search trusts that same discipline will carry into operating the business.
In practical terms, this means investors increasingly ask: “Show me how you’re managing this process,” not just “Show me the deal.” The barrier to capital isn’t deal quality alone—it’s demonstrated ability to execute with institutional rigor while maintaining the speed and conviction of an entrepreneur.
You’ve since launched DealDecoder. What does it do, and how does it change the LOI-to-close process?
DealDecoder is an operating system built specifically for searchers and lower middle market investors.
The LOI-to-close phase is where complexity spikes: multiple stakeholders, compressed timelines, and high-stakes decisions made with incomplete information. Despite that, most transactions are still managed through email threads, shared drives, and disconnected spreadsheets. That fragmentation introduces risk precisely when clarity and speed matter most.
Practically, DealDecoder sits at the center of the deal process. Searchers upload diligence materials as they move through a transaction; the system structures and analyzes that information across deals; and LPs review everything in a shared environment where context is preserved over time. Financials, diligence findings, open risks, and key decisions live in one place, rather than being scattered across tools and inboxes.
The objective is not to replace judgment. It is to support it. When investors have continuous visibility into assumptions, risks, and unresolved items, decisions move faster and with greater confidence.
After closing, that same infrastructure carries forward. Diligence context, value-creation plans, and operating priorities do not reset or disappear. They remain accessible and usable, creating continuity across the portfolio rather than forcing each deal to start from zero.
In short, DealDecoder reduces execution risk by giving both searchers and investors a shared source of truth throughout the full lifecycle of a deal.
Were there moments during your search that shaped the way you built DealDecoder?
Yes.
During one LOI process, an investor asked about a revenue concentration risk we had already analyzed. The conclusion was sound, but reconstructing the full context required digging through email threads, models, and diligence notes. It took close to twenty minutes to surface something that should have been immediately accessible.
That delay did not derail the deal, but it introduced hesitation. The concern was not the underlying business. It was whether the process itself was fully under control.
That moment made the real issue clear. This was not a personal productivity problem. It was a structural one. There are no tools purpose-built for how searchers and investors actually work during ETA transactions. Instead, deals are managed by duct-taping together generic tools—data rooms, spreadsheets, shared drives, and inboxes—that were never designed to preserve decision context under pressure.
Searchers and investors are forced to recreate institutional infrastructure manually, deal by deal, while timelines are compressing and stakes are highest. When context fragments, confidence slows. When confidence slows, execution risk compounds.
As capital becomes more experienced and selective, informal and improvised deal processes simply will not scale. The ecosystem is already moving in that direction, and DealDecoder was built to address that gap directly.
What is your long-term vision for DealDecoder, and how has the ecosystem responded?
The long-term vision is for DealDecoder to become the default infrastructure layer for lower middle market dealmaking.
Today, each transaction is treated as a standalone event. Context is rebuilt from scratch, lessons are relearned, and institutional knowledge lives in people’s heads or scattered files. That doesn’t scale as capital becomes more experienced and selective.
DealDecoder changes that. Over time, the platform becomes a portfolio intelligence layer where past diligence, decision rationales, risk patterns, and outcomes compound rather than disappear. Searchers operate with institutional-grade credibility from the first LOI, not after the first close.
We’re working with searchers and investors globally as design partners. We recently signed one of the largest institutional search fund investors in Canada – a firm managing a significant portfolio across multiple active searches. Their participation signals that this isn’t just workflow optimization for individual searchers. Institutional capital recognizes that deal infrastructure directly impacts portfolio performance.
The response has been consistent: fewer deals lost to avoidable process breakdowns, faster decision-making under pressure, and coordination that holds when complexity spikes. The search fund ecosystem is professionalizing rapidly, and infrastructure needs to evolve accordingly. DealDecoder is built for where this market is heading.
