A few years back, building a comp table for a healthcare acquisition was one of the most tedious things on my plate. I was Director of M&A at a NASDAQ-listed oncology platform - we were buying physician practices, doing add-ons, building out a specialty network. None of those deals were public. No press releases with disclosed multiples, no public filings, nothing you could pull from a Bloomberg terminal.
Every comp table started from scratch. I'd dig through our internal deal history, pull broker CIMs from prior processes we'd looked at, chase down transaction notes from intermediaries I'd worked with. Then came the actual work: scrubbing the data into a consistent format, calculating implied multiples (because half the time the broker's stated multiple didn't match what you got when you divided EV by EBITDA), normalizing for deal structure differences, formatting for the IC presentation. A solid comp table - one I'd actually put in front of our investment committee - could take me the better part of two days.
The mechanical work was what killed the time. Not the judgment - the judgment I could do in my head. It was the formatting, the organizing, the cross-referencing, the Excel work. The part a computer should be doing.
AI handles that part now.
I want to be careful here about what "AI builds comp tables" actually means, because the marketing on these tools outruns reality by a wide margin. Based on what I've seen, AI is genuinely useful for a specific slice of the comp table workflow: organizing raw data, calculating multiples, flagging outliers, and formatting output for presentations. What AI cannot do - and what still requires someone who's been in the room on these deals - is select the right comps, adjust for deal-specific structures, and apply the healthcare-specific nuances that actually determine whether a multiple is relevant to your transaction.
But that mechanical layer? That slice that was eating two days of my time? That's now a morning.
What the Workflow Actually Looks Like
The way I think about this now: AI is your analyst for the data work, and you're the senior doing the judgment work.
The tools that have earned a place in my workflow, based on my experience:
Claude is the workhorse. The reason is the context window - Claude's enterprise tier handles 1 million tokens, which means you can load multiple CIMs, broker notes, and your internal comp history into a single session and ask it to pull consistent fields across all of them. For private healthcare deals where your comps are all sitting in PDFs and deal notes, this matters a lot. It also has a direct Excel integration in beta that's worth knowing about.
ChatGPT with Advanced Data Analysis is the calculator. Once you have structured data, GPT-4o's code interpreter will compute your multiples, run descriptive statistics across your comp set, flag outliers by standard deviation, and produce clean output you can drop into Excel. It's also better than most people for writing the comp set commentary for your IC memo.
Perplexity handles the public research layer. If your comp set includes larger disclosed transactions - a platform deal that made the trade press, a health system acquisition that was announced - Perplexity will compile them into a table with citations faster than you can run a manual search. The limitation is that it can only surface what was publicly reported, which for private practice deals is usually just "deal announced" with no financial terms.
MEMO: These tools are advancing so fast that having to utilize various models for each task (as listed above) will quickly be a thing of the past. As a subscriber to my newsletter, you’ll be first to know!
For teams with institutional budgets, AlphaSense (which acquired Tegus for $930M and now covers 1.4 million private companies with M&A detail) and Hebbia (which KKR and Permira are using for institutional-grade VDR processing) are in a different tier. Hebbia's "Matrix" product runs the same query across hundreds of documents simultaneously - if you're processing a full data room, that capability is real. But for the individual practitioner or lean deal team, Claude and ChatGPT get you very far at $20-200/month.
According to McKinsey's 2026 M&A trends analysis, AI has made deal cycles 10-30% faster and M&A activities 20% cheaper. From what I've seen, the comp table workflow is one of the clearest examples of where that efficiency actually lands.
The Part AI Still Can't Do
Before I give you the prompts, I want to be honest about the ceiling, because I've seen people hand too much of this work to the model and end up with a comp table that looks right but isn't.
Comp selection is still yours. A GI practice acquired by a hospital system in 2024 and a GI practice acquired by a PE platform in the same year are different transactions, even if the headline numbers look similar. The strategic rationale, the pricing dynamics, the buyer's synergy math - none of that is in the data. AI will happily put both in the same comp set unless you tell it otherwise.
Private data gaps are real. No AI tool has access to the private practice transaction multiples you actually need for sub-$50M healthcare deals. The data that formed the basis of the comp work I did at the oncology platform doesn't exist in any database AI can reach. AlphaSense's private company coverage is the best you'll find in an off-the-shelf tool, and even they acknowledge limited coverage on sub-$25M add-on transactions. AI processes the data you bring. It doesn't source it.
Healthcare nuances require deal experience. A primary care group with a Medicare Advantage panel and VBC contracts might trade at 10-20x EBITDA. The fee-for-service primary care practice next door might trade at 3-5x. AI will not catch that distinction unless you explicitly flag it in the prompt. Same with ASC ownership (adds 1-3 turns in my experience), physician retention risk for key-person-dependent practices, and payor mix (practices above 70% commercial can command 40-60% higher multiples than government-payer-heavy groups).
Hebbia's own framing - taking analysts from zero to 90% and freeing them for the last mile - is the right mental model. The 90% is real. The last 10% is irreducibly human, and in a healthcare deal it's where the actual value lives.
One more thing: data security is not optional. Never upload NDA-protected CIM data to public AI tiers - ChatGPT Plus, Claude Pro, anything that isn't an enterprise or API-tier deployment with documented data handling. FINRA's 2026 oversight report now formally addresses gen AI risks in financial services. And Deloitte's analysis documented GPT-4 hallucinating financial figures in M&A contexts - always verify AI's arithmetic against your source documents.
That's the framework and the honest context. The full workflow - the three specific prompts I use for extraction, normalization/outlier analysis, and healthcare-specific annotation - is below for subscribers.
The Three Prompts That Cut My Comp Table Time by 80%
These prompts are designed for Claude (preferred for document-heavy work) or ChatGPT GPT-4o. Each assumes you are bringing your own deal data - AI is the processor, not the source.