YC’s call for startups for the summer ‘25 batch includes a section on Fullstack AI, I’ve written about AI Rollups a few times on this blog, but it looks like the model might now accelerate.

Coincidentally the same day OffDeal (a YC company) has published their blueprint for a rollup that takes on investment bank M&A. Somewhat unusually, there’s tonnes of detail in this strategy doc so I’ve pulled our a few interesting bits below.

First up, note how they’ve moved down market to deliberately unprofitable business that traditional M&A would not consider:

Our first battleground is lower-middle-market M&A—deals in the $5-30 m range that big banks ignore because $100-300k fees can’t support a bloated legacy deal team. A two-person pod plus AI agents does make money at that ticket size, and the high deal velocity gives us dozens of live reps each quarter.

The full strategy is then laid out:

Short-term. Remain in SMB M&A until our processes and software reach maturity

Mid-term. Move up-market, competing for $100m+ deals.

Long-term. Move even more up-market ($1bn+ deals) and add adjacent advisory services—capital raises, debt advisory - on the same platform, aiming for a full-service franchise.

They also make a strong case on the level of scrappiness they can ship with:

Because every application is internal, we can release features at “good-enough” and harden them through live use rather than long test cycles. This creates a rapid product feedback loop.

This resonated - I think this particularly suits software with probabilistic outcomes where getting consistent quality is hard. Your own team will be far more tolerant of occasional failure/the nuance of the tool, so heavy use of LLMs in this setting as opposed to say selling software into this niche makes sense.

It’s noticeable as well that the tooling needed in this niche neatly fits the jagged edge of what models are good at today, e.g. heavy use of deep research modes.

Towards the end there’s a nice call out of the problems with the model:

Launching any full-stack startup, let alone an investment bank is hard:

• Many competencies, zero excuses. We must stand up data infrastructure, AI tooling, deal execution playbooks, and a brand that convinces owners to entrust their life’s work - all at once. There is no single “core feature” we can hide behind.

• Long lead, lumpy revenue. A SaaS demo converts in weeks; a sell-side mandate must be sourced, launched, and closed before a dollar lands. We carry months of payroll while a single busted deal can wipe a quarter’s pipeline—so the capital curve is steeper than pure software.

• Skepticism by default. Industry insiders often view a VC-backed investment bank run by “tech outsiders” as Silicon-Valley hubris. Until we post results, we’re assumed wrong.

The cashflow volatility called out here feels like the big one - M&A activity is pro-cyclical and lumpy in the way it pays out. A venture model where you’re trying to build a high growth juggernaut at pace while running a loss with limited runway might not be a great fit, particularly as you scale out and opex grows (will that headcount advantage from use of agents and AI hold as the deal complexity ramps up?). Perhaps this is the other, unwritten, driver of starting with smaller deal sizes - the volume will be higher which means your pipeline is less concentrated on a few big, slow moving, bets. Also interesting how this strategy is clearly written to persuade with the two weaker, more fixable, issues either side of the filling in the s*** sandwich.

As you try to move upmarket through the niches, you suspect it might be hard to keep winning business rapidly in a market where trust is everything and buyers will be heavily influenced by brand, staying power and a lengthy track record.

This is very interesting though, it feels feasible. One to watch - I’ve seen lots of discussion of the model but this is a pretty strong blueprint for a startup in the space. Worth reading.