Pre-LOI AI scoring of buyer bids, how sellers read between the lines
Sellers are starting to use AI to score buyer bid letters before signing the LOI. The dynamics on the negotiating table change when the seller has read the buyer's previous bids too.
A quiet shift is underway on the sell side of mid market M&A processes. Sell side bankers and corporate counsel have started using AI tools to score buyer bid letters before the seller picks an LOI. The output is a structured comparison of the bids on dimensions that go beyond price.
The use case is straightforward. A competitive process produces five to ten bid letters that look similar at a glance but vary in meaningful ways: rep and warranty insurance assumptions, financing commitment specificity, escrow structure, transition services arrangements, employee retention expectations, regulatory representations. Reading them carefully takes time. An AI tool can produce a structured comparison in minutes.
The downstream effect on the negotiation is more interesting than the time saved.
What the AI is actually doing
The tools in use are mostly enterprise instances of generic LLMs (Claude, GPT, custom deployments) with a structured comparison prompt. The prompt takes the bid letter, extracts the key parameters, and emits them in a normalized format. The seller's banker and counsel get a table that lines up the bids side by side on twenty or thirty dimensions.
That is not novel work. A senior associate could produce the same table. What is novel is the cost. The cost of running ten bids through a structured comparison was an hour per bid. It is now a few minutes per bid. That means the seller is more likely to do it on every bid rather than only on the apparent top contenders.
The second order effect is that the seller now compares bid letters across deals, not just within a deal. A seller's banker who has run thirty deals over the last two years can run the AI against the entire bid letter corpus, pull pattern data, and tell the client "this buyer's bid is unusually weak on financing certainty compared to its prior bid letters in similar processes." That kind of cross-deal pattern recognition was not feasible without AI tooling. It is now routine.
How buyer side practitioners should think about this
A buyer's bid letter is now being read against the buyer's prior bid letters in similar processes. That means consistency matters. A buyer who has structured prior deals one way and offers a meaningfully different structure on a current deal should expect to be asked why. The AI is not making the inference. It is surfacing the comparison so a human can ask.
Two practical implications.
First, the buyer's deal team should know its own bid letter library before submitting. The same coordinator who tracks competitive pricing should track competitive structure. If the current bid departs from the buyer's standard, the deal team should be prepared to explain it.
Second, the buyer's bid letter should be drafted with the assumption that the seller's counsel will run it through a comparison tool. That changes what is worth being specific about. Vague language that the seller's counsel might have glossed over a few years ago will now be flagged in a structured comparison, with a note that other bidders were more specific on the same point. The buyer either provides the specificity at bid stage or gives the seller a reason to push back.
The sell side benefit and its limit
The sell side benefit is real. A better-informed seller can negotiate more effectively, particularly on non-price terms where the variation between bids is genuinely meaningful and easy to miss without a structured comparison.
The limit is that the AI is not running the deal. The judgment about which bid to select, which bid is actually executable, and how to play one bidder against another remains human. The AI shifts what the human is working from, not who is making the decision.
What this looks like in a small tool
I built a stripped down version of this for the public side of the workflow. /tools/buyer-bid-scorer/ takes two or more bid letter snippets and returns a structured comparison across the standard dimensions. It is meant for educational and orientation use, not as a substitute for the real banker-and-counsel comparison work. The tool saves nothing, sends nothing back to me or my firm, and is not legal or financial advice.
The broader point is that AI is changing the cost structure of comparison and pattern recognition work on both sides of M&A processes. The sell side is moving faster than the buy side because the value at stake per dollar of cost reduction is currently higher. The buy side will catch up, and the negotiation dynamics will settle into a new equilibrium where both sides have more information faster. That equilibrium will be different from the one practitioners are used to.
Walter Allison is a corporate attorney in Denver. He writes here about M&A, private equity, and venture capital structure.
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