15 Best M&A Advisors for AI Companies (2026)

How we verified this list

Every firm below was checked against its own website on August 21, 2026. Not a directory, not a press release aggregator, not a memory of who was famous in 2021. Where a fact could not be found on a page we loaded, the profile says "not published" instead of guessing. Where a firm's site was unreachable, we say that too.

What we excluded, and why

• Arma Partners. One of the strongest independent digital economy banks in Europe, with 366 completed deals and $224.3B of aggregate value published on its own site. We fetched the home page, team page, deals page, about page, sector index and two segment pages. The strings "AI", "artificial intelligence" and "machine learning" appear on none of them. A guide about AI advisors cannot rank a firm that does not claim the sector.
• Shea & Company. Software-only, Boston and San Francisco, more than $60Bn of advised transaction value. Its published sector list runs to 13 entries and "Data & Analytics" is the closest thing to AI. Same reasoning as Arma.
• LionTree. Founded 2012, media and technology focused. No AI practice page and no AI-labelled transactions on its advisory feed.
• Canaccord Genuity. A real technology banking franchise with named sector heads, but no AI or machine learning practice on the two sector pages we fetched.
• DAI Magister. Strong emerging-markets climate and deep tech shop. The only AI-labelled deal we found was a 2024 SPAC listing, not a sale.
• Raymond James. Every attempt to load their technology and services pages timed out on August 21, 2026. We are not willing to profile a firm from search snippets, so it is off the list rather than described from memory.

Quick comparison table

Deal size bands are what the firm publishes on its own site. Where a firm publishes nothing, the column says "not published" rather than an estimate, and the profile explains what we inferred from its tombstones instead.

Which advisor fits my sub-vertical?

AI is not a sector. It is a capability that shows up inside twelve or thirteen different buyer universes, and the buyer universe is what determines who should run your process. Find your row.

Tier 1: AI-dedicated specialists, roughly $30M to $1B enterprise value

These five firms name AI as a practice or a coverage area on their own websites and can point to closed transactions where the target was an AI company. They sit in the band where most fundable AI businesses realistically trade. If your company has revenue, customers and a defensible model, start here.

Tier 2: founder-scale sell-side, roughly $5M to $150M enterprise value

This is where most people reading this page really sit. These four firms take mandates the tier above will decline, and three of the four are sell-side focused, which removes the conflict question entirely. Ranked below tier one on scale and buyer reach, not on quality of representation.

Tier 3: large-cap and full-service, $250M and up

These six firms are ranked last because of filter four, not because of quality. Every one of them is a stronger institution than most of the firms above. They are also, for the overwhelming majority of AI sellers in 2026, the wrong call. Of the 172 AI acquisitions we tracked this year, 7 disclosed a value above $1B. If you are not in that seven, a firm that only works there will either decline you or staff you thinly.

What 2026 AI deals show

Everything in this section comes from the ProCloser deal index, specifically the vertical AI cut: 172 acquisitions of AI companies announced between January 5 and August 20, 2026, collected from public announcements only. Nothing is estimated and every row links to its source. This is 2026 year to date, technology and software only, and it is a sample of what was announced publicly, not of every AI deal that happened.

Who is buying AI companies

Strategic buyers dominate, and it is not close. 111 of the 172 deals had a strategic acquirer and 54 had a public company buyer. Private equity appeared in 7, split between 2 direct private equity purchases and 5 PE-backed platform acquisitions. Compare that to the full index, where private equity and PE-backed platforms together account for 190 of 1,478 deals, and the AI picture looks even more strategic-led than technology M&A generally.

That has a direct consequence for advisor selection. A firm whose relationships are mostly with sponsors is fishing in a pond holding 4% of the buyers. Ask any advisor you meet how many of their last ten closes went to a strategic acquirer rather than a fund.

The most acquisitive buyers in the AI cut were SpaceX with 3 deals, then Cognition, Sierra, 9Yards X, Global Clean Energy and Meta with 2 each. That is a long tail: 172 deals spread across a buyer set where nobody made more than three purchases. There is no small group of serial AI acquirers you can simply introduce yourself to. Your buyer list has to be built.

Price, and the absence of it

28 of the 172 deals carry a disclosed value, which is 16%. Broken into bands, 4 disclosed under $20M, 11 between $20M and $200M, 6 between $200M and $1B, and 7 above $1B. The largest disclosed AI transaction of the year is SpaceX's announced agreement to acquire Cursor for $60 billion, stated in SpaceX's own second quarter 2026 earnings release filed with the SEC. Across the priced subset, our valuation benchmarks put the median disclosed vertical AI deal at $167.8M with a mean of $5.49B, a gap that tells you how much a handful of megadeals distort any average you read elsewhere.

27 of the 172 deals crossed a border, roughly 16%. Across the whole index, 101 of 1,478 deals ran between the US and Europe specifically.

Three deals worth studying

The honest tier below this band

If your AI company has under roughly $2M of revenue, or is pre-revenue with a strong team, most of the fifteen firms above will pass. That is not a judgement on your business, it is arithmetic on their side: a sell-side process costs the same amount of banker time whether the outcome is $4M or $40M, and the economics only work above a threshold.

Your realistic options below that line, named plainly:

• Acquire.com and similar founder marketplaces. Listing-based, self-serve, fast. Good for a clean small SaaS with predictable revenue. Bad for anything where the value sits in a team, a model or a data asset, because a listing cannot run a competitive process against three strategic acquirers.
• Flippa and Empire Flippers. Genuinely useful for micro-SaaS and internet assets. Not built for AI companies where diligence involves model provenance, training data rights and inference cost structure.
• Regional business brokers. Cheaper, closer, and almost always the wrong choice for AI. A broker who sells HVAC companies will price your business on earnings and will not know who your strategic buyer is. Read our business broker versus M&A advisor comparison before you sign anything.
• Acquihire conversations run directly. Many 2026 AI outcomes were team purchases. If that is genuinely your path, a lawyer and a strong advisor relationship matter more than a banker, and you should read our note on finding a buyer directly.

The honest advice at this size: build for another eighteen months if you can. The gap between a $2M and a $6M revenue AI business is not three times the price. It is often the difference between a marketplace listing and a real process with four bidders.

The honest tier above this band

Above roughly $2B of expected value, the fifteen firms in this guide start being the wrong answer in the other direction. At that scale you want a bank that can underwrite, syndicate and defend a public-market narrative, and that means the bulge bracket: Goldman Sachs, Morgan Stanley, J.P. Morgan, Bank of America, Citi, plus the elite independents like Centerview, Allen & Company, PJT Partners and LionTree.

Why they are usually wrong for you, stated bluntly. Their minimum economics mean a $150M AI sale is a rounding error, and it will be staffed accordingly. They carry lending relationships and research franchises that create conflicts a sell-side-only boutique does not have. Their process templates assume a public-company counterparty and a disclosure calendar. And the partner who pitched you will be on three larger deals by the time your diligence starts.

There is one situation where you should call them anyway: an unsolicited approach from a public strategic at a number that surprised you. In that case, the credibility of the name on your side of the table has independent value, and a fairness opinion may be required. Our guide to the banks that work the $20M to $200M band covers where the real line sits.

The AI valuation ladder

These are indicative ranges, not quotes. Two AI companies with identical revenue routinely trade three multiples apart on the strength of retention, gross margin after inference cost, and whether the model is a moat or a feature. Use this to set expectations and then argue about where inside the range you sit.

One caution on the disclosed medians. Only 16% of tracked deals publish a price, and disclosure skews heavily toward larger transactions, so those medians describe the priced subset rather than the market. For owner-level pricing at the lower end, the multiples-by-industry benchmarks are the better anchor. For a first-pass number on your own business, try the business valuation tool.

What do these advisors charge in 2026?

No firm in this guide publishes its pricing, so the ranges below come from our own M&A advisory fee research across the middle market. Treat them as the opening position, because every element is negotiable and the monthly charge is frequently credited back against the amount paid at close.

Two AI-specific things to negotiate. First, ask whether the percentage paid at close applies to earnout and retention consideration or only to cash at closing, because AI deals carry unusually heavy earnout structures and the difference can be six figures. Second, ask what happens if the outcome is an acquihire rather than an asset sale, since the work is similar and the headline number is not. Our fee calculator models the total for your deal size.

The frames we use in this guide

Five lenses we apply to every AI mandate question that reaches us. Each one has a rule, a number behind it, and something you can do with it this week.

1. The Credited-Advisor Rate

Rule: in AI, you cannot select an advisor from public deal credits, because public deal credits barely exist.

Number: 8 of 172 AI acquisitions in 2026 named an advisor. That is 4.7%, against 134 of 1,478 across the whole index, which is 9.1%. AI deals disclose their advisors at roughly half the rate of technology M&A generally.

Act on it: stop asking for a tombstone sheet as your primary filter and start asking for three references from founders who closed with that firm in the last eighteen months. Call all three. In a market this opaque, references carry more information than league tables.

2. The Disclosure Gap

Rule: your comparables are mostly invisible, so you are paying an advisor for private pricing knowledge, not for public research.

Number: 144 of 172 AI deals were announced without terms. 84% of the market priced itself in private.

Act on it: in your first meeting, ask the advisor to name three private AI transactions in your sub-vertical and describe roughly where they priced. If they can only cite deals you could have found on TechCrunch, they do not have the private data you are hiring them for.

3. The $1B Cliff

Rule: pick the firm that matches your band, not the firm that matches your ambition.

Number: 7 of 172 AI deals disclosed a value above $1B. 15 disclosed between $20M and $200M. Only 4 disclosed under $20M, and 144 disclosed nothing at all, which strongly implies most of the unpriced deals sat at the smaller end.

Act on it: before you take a meeting, check whether the firm publishes a deal-size band. Woodside publishes $30M to $500M. AGC publishes $50M to $1B and above. Windsor Drake publishes $5M to $300M. L40 publishes up to $100M ARR. Those four numbers save you four wasted calls.

4. The Border Test

Rule: if your buyer list has one country on it, your process is incomplete.

Number: 27 of 172 AI deals crossed a border in 2026, and 101 of the 1,478 deals in the full index ran specifically between the US and Europe.

Act on it: ask every advisor you meet how many of their last ten closes had a buyer headquartered outside the seller's country. If the answer is zero, either add a second firm or accept a narrower auction. Our US to Europe deal cut shows what the flow looks like in practice.

5. The Strategic Majority

Rule: AI is a strategic buyer market, and a sponsor-heavy advisor is fishing in the wrong pond.

Number: 111 of 172 AI deals had a strategic acquirer and 54 had a public company buyer. Private equity in any form appeared in 7.

Act on it: ask what proportion of the firm's last twenty closes went to a strategic rather than a fund. In AI you want that number above two thirds. In traditional lower-middle-market software you would accept the opposite. This is one of the few places where AI genuinely behaves differently from the rest of technology M&A.

How to verify an advisor is legit and unconflicted

Twenty minutes of checking before you sign, in order.

1. Search FINRA BrokerCheck. Go to brokercheck.finra.org and search the firm name. You are looking for the registered entity, the CRD number, how long it has been registered, and any disclosure events. Note that the legal entity often differs from the brand: AGC Partners operates as America's Growth Capital, LLC, for example.
2. If they are not on BrokerCheck, ask why in writing. There is a legitimate answer. Since 2023, US federal law provides an M&A broker exemption in Section 15(b)(13) of the Securities Exchange Act, which lets qualifying firms advise on private company sales without full broker-dealer registration. L40 states that it operates under it. That is a real position, not a loophole. What you should never accept is silence.
3. Check the individual, not just the firm. Search the banker who will run your process by name. Windsor Drake publishes CRD 2288224 for Bruce Goldstein, which is exactly the transparency you want.
4. For non-US firms, check the regulator. Arma Partners is authorised by the FCA in the UK with a separate FINRA member entity in the US. GP Bullhound and Drake Star publish no regulatory status we could find, so ask.
5. Ask three conflict questions in writing. Do you take buy-side mandates in my sector? Do you have a lending relationship or research franchise? Have you represented any company on my buyer list in the last three years? Vista Point and Windsor Drake both publish exclusively sell-side mandates, which answers all three in advance.
6. Ask who runs the process day to day. Get the name in the engagement letter if you can. The most common complaint we hear about large firms is not incompetence, it is substitution.
7. Cross-check the tombstones. Pick two deals off their site and search for the announcement. You are checking that they were credited publicly and in what role. Our advisor league table shows who was credited on tracked 2026 deals.

The traps in a "best AI M&A advisor" list

• Recycled league tables. Global deal value rankings measure who advised on the largest transactions, which means they measure bulge brackets. They tell you nothing about who can sell a $40M applied-AI company.
• Sector pages that never existed until 2024. Adding "AI" to a sector list costs nothing. We fetched every candidate's site for this guide and found several strong firms with no AI mention at all, which was more honest than the alternative.
• Tombstones that are financings. A Series B is not an exit. Drake Star's AI page, for example, mixes acquisitions and capital raises without separating them. Ask which were sell-side M&A mandates.
• Acquihires counted as deals. A team purchase with no purchase price is a hiring event with paperwork. If a firm's AI credentials are three acquihires, they have not sold an AI company.
• Lists with no exclusions. If every firm the writer looked at made the list, no filter was applied. Ours excluded six, including two firms we rate highly.
• Undisclosed commercial relationships. Two firms in this guide are ProCloser clients and are the only two with outbound links. That is disclosed here, in their profiles, and in the disclosure paragraph at the bottom.
• Advice that ignores your size. Any guide that recommends the same firm to a $6M company and a $600M company is not a guide, it is a brochure.

Where ProCloser fits

ProCloser is not an M&A advisor and does not run sale processes. We do two things relevant to this page. We maintain an open index of announced technology and SaaS acquisitions, currently 1,478 deals from 2026 with sources attached, which is where every number in this guide comes from. And we operate a matching service that introduces sellers to advisory firms, including some of the firms listed above.

That second thing is a commercial relationship and you should read this page knowing it. Our defence is the method: the four inclusion filters were applied identically to every firm, the two client firms carry the same "considerations" treatment as everyone else, and the deal-index numbers are published openly at procloser.ai/deals so you can check our arithmetic. If you find an error, corrections@procloser.ai.

The bottom line

The routing rule again, because it is the only thing on this page you need to remember. Under $30M of enterprise value, hire a founder-scale sell-side firm: Software Equity Group, Vista Point Advisors, Solganick & Co or Windsor Drake. Between $30M and $1B, hire an AI-dedicated specialist: AGC Partners, Union Square Advisors, L40, Woodside Capital Partners or Drake Star. Above $500M, hire a large-cap bank: Qatalyst Partners, Evercore, Houlihan Lokey, Lincoln International or Jefferies. Step up a tier if a strategic has already approached you unsolicited. Step down a tier if your revenue is under $3M.

And then do the two things that matter more than the firm you pick. Ask for three founder references and call all three. And ask the advisor to name three private AI transactions in your sub-vertical with rough pricing, because in a market where 84% of deals never publish a number, that knowledge is the entire product.

Related resources

• Best cybersecurity M&A advisors. 15 firms ranked on security deal record, with the firms we excluded and why.
• Best fintech M&A advisors. 15 firms across payments, banking software, insurtech and wealthtech.
• Most active M&A advisors in tech (2026 league table). Every advisory firm publicly credited on a tracked 2026 tech deal, with the sample size stated.
• ProCloser Tech & SaaS M&A Deal Index. The open dataset behind this guide: 1,473 announced 2026 deals with sources, free CSV, JSON and RSS, no login.
• Vertical AI deal cut. The 172 AI-company acquisitions used throughout this page, filterable by buyer type, size and geography.
• Tech M&A insights. Buyer mix, advisor credits and deal sizes by month across the whole index.
• Valuation benchmarks. Median and mean disclosed values by sector, including the $167.8M vertical AI median cited above.
• Most active acquirers. Who is buying technology companies in 2026, ranked by tracked deal count.
• Advisor league table. Every advisory firm credited on a tracked 2026 deal, with counts.
• EBITDA and SDE multiples by industry. The earnings-based benchmarks that apply to AI services and consultancies.
• SaaS revenue multiples by ARR tier. The ARR ladder used in the valuation table, including the AI differentiation premium.
• Best cross-border tech M&A advisors, US and Europe. For AI companies whose best buyer is on another continent.
• Best M&A advisors for SaaS and technology. The broader software ranking, from under $10M ARR to enterprise.
• Best investment banks for $20M to $200M SaaS exits. Where the real line sits between boutique and bulge bracket.
• Best boutique M&A advisory firms. Sector-agnostic boutique rankings for founders.
• Best M&A advisors for profitable mid-market SaaS. For AI-enabled software with real EBITDA.
• M&A advisory fees by deal size. The source for the cost table above.
• Get matched to an advisor. Tell us your size and sub-vertical and we will name the two or three firms that fit.

Sources

Firm facts were taken from the pages below, all loaded on August 21, 2026. Where a page would not load, the profile says so.

• AGC Partners: agcpartners.com, team, transactions
• Union Square Advisors: usadvisors.com, about, team
• L40: l40.com, team, transactions, Elipse.ai deal announcement
• Woodside Capital Partners: woodsidecap.com, team, transactions, about
• Drake Star: drakestar.com, artificial intelligence at Drake Star, Frontline Technology Report, August 2026
• Software Equity Group: softwareequity.com, about, transactions
• Vista Point Advisors: vistapointadvisors.com, team, transactions
• Solganick & Co: solganick.com, about, transactions
• Windsor Drake: windsordrake.com, the firm, team, AI M&A market analysis
• GP Bullhound: gpbullhound.com, team, about us, deals
• Qatalyst Partners: qatalyst.com, team, deals
• Houlihan Lokey: Form 10-K for the fiscal year ended March 31, 2026. Their own website blocked automated access on August 21, 2026.
• Lincoln International: technology practice
• Evercore: evercore.com, transactions, and CoreWeave's investor release confirming the advisory role on Weights & Biases
• Jefferies: TechTrek and their boardroom intelligence insights series
• Excluded firms checked: armapartners.com, sheaco.com, liontree.com, canaccordgenuity.com, daimagister.com
• Deal-index case study sources: Eldridge and Sudolabs, adesso and omni:us
• SpaceX and Cursor: SpaceX second quarter 2026 results, filed with the SEC
• Deal data: ProCloser Tech & SaaS M&A Deal Index, vertical AI cut, valuation benchmarks, advisor league table