Hello,
A note on what I have been up to. In late 2025 I formally started Pravah Capital, a search fund focused on acquiring an Indian B2B services or technology business. Many of you played some role in getting me here - through advice, a sharp question, or simply by being someone whose perspective mattered when I was deciding whether to do this. This is the first of what I hope will be a quarterly note from the field - not a formal update, just notes on what I am learning while running a real search in real time.
The headline from quarter one: the deal flow is real, the succession-gap thesis is real, and the valuation gap is real. We have evaluated 64 live situations, mostly Indian B2B services and technology platforms in the ₹100-300cr (~$10-35M) enterprise value band. We are actively pursuing nine of them, including one where we have submitted a non-binding offer.
The more interesting story is what 90 days inside this market actually feels like.
The succession gap thesis is real, and the path is naturally a patient one. A generation of founders in their 50s and 60s with no obvious next chapter is as deep as the data suggested. The journey from a first conversation to a transaction takes time, which makes sense. Sellers reference a range of valuation benchmarks - PE/VC deals on much larger companies, public-market multiples in different growth regimes, IPO chatter on peers - and a real conversation only begins once those references reset against the reality of their own business. The bigger thing being built, though, is trust. A founder handing over a company they have spent thirty years on does so on much more than price.
My working assumption is that the right buyer relationship typically gets built over six to twelve months, and that the moment a founder is genuinely ready to transact usually comes after they have spent some time in the market. I want to be the first call when that moment arrives. This is a search that will reward patience and the relationships we build over the next year, and I am optimistic about the shape of what is in front of us.
The Indian advisor ecosystem is far more mature than I had expected, and is the reason a two-person team has been able to evaluate sixty-four situations in ninety days. Most of our deal flow so far has come through investment banks, M&A boutiques, and small specialist shops scattered across the country. Typically four or five-person teams, with deep local roots in their cities and the trust of the founders in their catchment areas. Contrary to what India's M&A landscape looked like even five or ten years ago, the SME intermediary base has genuinely ramped up in recent years. Some weeks it feels like there are more brokers than there are buyers and sellers. Q2 will see us build out a more proprietary sourcing engine, but the depth of this ecosystem, and our goal of building a close-to-the-ground network in the cities and sectors we care most about, is what allows us to credibly run the playbook we are running.
AI is reshaping both what we look for and how we operate. On the investing side, every target now has to clear an AI-resilience test before we deepen: businesses where AI augments rather than substitutes, where domain expertise is hard to encode, where pricing is on outcomes rather than headcount, where embeddedness in customer workflow is genuine. This filter has killed several otherwise interesting situations. On the operations side, my associate Manav and I are running the search through a custom Claude Code stack that lets two people cover the ground a five-person team would have covered a year ago. Sector primers, deal screens, advisor management, contact enrichment - most of what used to be analyst-week work compresses into hours. The composition of the team I will eventually need looks different from what I planned twelve months ago: less routine research, more judgment and relationship work.
What follows is more on what we are seeing and what I am wrestling with. If you have ten minutes, the second section is the most useful. If you have twenty, scroll all the way down - I have two real questions I would love your read on.
Bhavik
Sixty-four deals across roughly seven sector clusters. Below: six anonymised situations from the quarter that capture the kind of decisions we are navigating, then the filters we are applying across sectors.
Anonymised, but real - each is a live or recently-evaluated deal.
A business at ~$14M revenue running mission-critical infrastructure for several hundred Indian stockbrokers. SEBI-mandated upgrade cycle is creating a forced refresh. Strong stickiness, high switching costs - but the seller is anchored to peak-cycle multiples and one year of decelerating growth has not yet moved that anchor.
A dealer-management platform at ~$8M revenue embedded in the workflows of large OEMs across auto, FMCG, and pharma, with a 25-year track record and 85%+ recurring revenue. They are voluntarily winding down legacy one-off revenue to reinvest in AI products, which compresses growth and margin in the near term. The underwriting question becomes: do you pay for the bounce-back, or wait for proof?
A pan-India equipment-handling operator at ~$4M revenue with 49% EBITDA from a non-obvious source: disciplined sourcing of secondhand machinery. The business is more capital-intensive than we typically pursue. The question is whether the sourcing edge is a real moat or just a moment.
A chip-design house at ~$7M revenue operating at the most advanced process nodes. AI augments rather than substitutes the work. Globally scarce skill set, deeply embedded in foundry workflows. We have submitted a non-binding offer here. The valuation conversation is open.
A back-office services business at ~$5M revenue serving the US mortgage-default market. Strong growth, 80%+ repeat revenue, structural cost arbitrage. But 65-80% of the underlying work falls into among the most automatable categories in the AI mapping we use. The deal lives or dies on whether the service has a credible pivot path.
A regulatory-driven testing lab at ~$3M revenue with 50%+ EBITDA - well above what our asset-light orthodoxy says should be sustainable. Accreditation moat, several thousand active clients, compliance-driven demand. The intellectual question is how much of the margin is structural versus cyclical, and whether scale can be replicated.
Across these, the principles we filter for are consistent: deep vertical domain expertise where the work itself is hard to commoditise, ownership of customer outcomes rather than just staffing, embeddedness in customer workflows and data flows, and the small-cost-but-mission-critical shape - a service essential to the customer's operation but a small line item on their P&L, where modest pricing power compounds into outsized economics. The spaces that throw up the most examples of these principles are contract testing and lab services, B2B services with regulatory or domain moats, vertical SaaS, and the parts of IT services and engineering R&D that survive an AI-startup-substitution test. We mostly pass on industrials and manufacturing (a quarter of all flow, outside our asset-light mandate), and on a long tail of ed-tech, CPaaS, AI consulting, and staffing-style businesses below our size or margin floor.
Manav Chaudhry, who joined as our first full-time associate this quarter, is the other half of the team. Everything runs through a custom AI-augmented stack we have built to integrate sourcing, screening, sector research and outreach. The compounding effect of every sector primer, every advisor interaction, and every deal evaluation feeding the next one is the part that I think will look like overhead in quarter one and like a real edge by quarter four.
Two questions I would love your read on. Both are live; both have shaped real decisions in the last 90 days.
My working heuristic is to favour businesses where AI augments rather than replaces, where domain expertise is hard to encode, where pricing is on outcomes rather than headcount, and where the service is genuinely embedded in customer workflow or data. This filter is doing real work - it has killed deals - but it feels under-tested.
The honest version of the question: which of these signals would still hold up if the next two years of AI capability moved much faster than expected? And which businesses look protected today only because we have not yet seen the right disruption vector? If you have a strong intuition here from your portfolio or your own experience, I would value that lens enormously.
Our framework says: asset-light, recurring or repeat revenue, deeply embedded in customer workflows, sector limited to B2B services and tech. It is a useful filter - it kills most of what we see. But quarter one has put real exceptions in front of us: a capital-intensive industrial services business with 49% EBITDA from a non-obvious sourcing edge, and a regulated business with margins structurally higher than our framework expects.
The question I would value your read on: when a real exception lands on your desk, how do you stress-test whether it is genuinely exceptional - or just a story you are telling yourself to break a discipline you should have kept? This applies to backing a fund as much as to underwriting a deal. Sharp pattern-recognition here, from any angle, would be useful.
I'll send these every quarter or so to keep you updated. If you would rather not be on the list, just reply and I'll take you off, no offence taken. Equally, if anyone in your world would find this useful, feel free to forward.
If something here sparks a thought, I would genuinely value the email. And if you are ever in Mumbai and want to grab a coffee and dig into any of this, the answer is yes.
Thank you for the early support.
Bhavik