The deal you'd never find with a Google search, on your Monday shortlist.
Corp dev teams typically find acquisition targets one of two ways: bankers pitch them, or someone remembers a name from a conference. Between those two channels, the best targets — the ones with the perfect stack, the right team, and the valuation you can actually stomach — slip past because nobody had capacity to look when they were briefly buyable.
Your AI M&A target screening agent runs a tight deal box across 50+ public and licensed sources every day — Crunchbase, PitchBook, Apollo, LinkedIn, Tracxn, the industry-specific sources that matter to your sector. Every Monday, 5-8 fit-scored one-pagers land in your Slack. Ranked. Sourced. Ready for a first conversation.
The systematic top-of-funnel a mid-market PE firm pays a two-person research team to build. Running for you while you sleep.
You miss 70% of the deal box because nobody was looking that week.
The standard corp dev workflow at mid-market companies is episodic: your head of strategy spends a concentrated week sourcing before a board meeting, generates a list of 12-20 targets, works the top 3, and the rest gets forgotten until next quarter. Between those sprints, the best targets — the ones that were briefly "buyable" for eight weeks — close rounds or get snapped up by a strategic and disappear.
The firms that consistently win on acquisition price aren't smarter. They're just always looking. They have a two-person research team doing exactly what your strategy lead does every quarter — except every week, across 50+ sources, against a documented deal box. You can't hire that team at your scale. You also can't afford not to have one.
Set your filters once. Let the box run against 50+ sources forever.
During deployment, we translate your acquisition strategy into filterable criteria — revenue band, ARR, team size, tech stack, geography, funding stage, industry vertical, custom attributes. Most deployments end up with 8-15 filters combining into a tight, specific deal box. Then the agent runs that box across Crunchbase, PitchBook (if your firm licenses it), Apollo, LinkedIn company data, Tracxn, GitHub, and any industry-specific sources that matter to your sector.
Every day. Every new company that crosses into range gets captured. Every existing target that moves closer to ready gets a second look. No more "I wonder if Company X has closed a round since we last checked." The agent checked this morning.
Every shortlisted target gets a one-page brief. Not a link dump.
Each one-pager has a consistent structure so you can scan seven of them in ten minutes: company overview, revenue estimate (with source and confidence), team composition, tech stack, funding history, competitive position, and a fit score against your deal box with the specific filters that drove the score. Every data point shows its source and when it was last updated — Crunchbase as of Q1, LinkedIn as of last week, PitchBook as of March 15.
When sources disagree — which they regularly do on revenue estimates for private companies — the agent surfaces the conflict instead of silently picking one. You see "Crunchbase says $12M, Apollo says $18M" and decide which you trust based on provenance and recency.
Your deal box gets sharper every week you use it.
Every Monday shortlist comes with three quick actions per target: kill, keep (move to first conversation), or watch (monitor until ripe). The agent learns from every decision. Kill a target because the customer concentration was too high? The agent deprioritizes similar targets next week. Flag a "watch" target because they're six months from buyable? The agent starts tracking hiring velocity, product launches, and funding signals, and pings you when something meaningful fires.
After a quarter of use, most deal boxes have tightened to the point where the weekly shortlist is 80%+ worth a first meeting. You're not screening out noise anymore. You're picking between qualified options.
Three questions every corp dev team raises first.
Public data on private companies is notoriously unreliable.
True — and that's why every one-pager shows source and last-updated date for every figure. When sources disagree (Crunchbase says $12M revenue, Apollo says $18M), the agent flags the conflict instead of silently choosing one. Revenue estimates are a starting point for conversations with the target, not a valuation. The agent is honest about what it can and can't know.
Does this replace our relationship-driven deal flow?
No. The agent complements proprietary deal flow rather than replacing it. Your network still surfaces the founders you'd otherwise never hear about. The agent covers the 30-40% of the deal box you can't possibly see through relationships alone — and keeps a tight watch on adjacencies you'd miss entirely.
What about our licensed database subscriptions — do we have to rebuy them?
No. The agent uses your existing firm subscriptions to PitchBook, Preqin, or industry-specific databases — your credentials, your access, your license terms. The agent layers those on top of free public sources (Crunchbase basic, LinkedIn, GitHub) to build a combined view. You don't pay twice.
AI M&A target screening — answered.
Which databases does the AI M&A target screening agent access?+
Default sources include Crunchbase, PitchBook (if your firm licenses it), Apollo, Tracxn, LinkedIn company data, Product Hunt, GitHub (for tech stack signals), and industry-specific registries. Licensed sources require your firm's credentials — the agent uses your existing subscription, not ours.
How specific can I make the acquisition criteria?+
Very. Standard filters include revenue range, ARR, employee count, geography, funding stage, tech stack, and industry vertical. Custom filters can include anything your deal box cares about — specific tech in the stack, leadership pedigree, customer concentration, funding runway. Most deployments end up with 8-15 filters combining to a tight deal box.
How does the fit score actually work?+
Each filter in your deal box is weighted based on what you said mattered most during onboarding. Targets get a 0-100 score based on how many filters they hit and how close to center they sit on each. A score above 75 triggers inclusion in the weekly shortlist. Your feedback on past shortlists (kept / killed / passed) tunes the weights over time.
What if the public data on a target is out of date or wrong?+
Every one-pager shows the source and last-updated date for each data point — Crunchbase revenue estimate as of Q1 2026, LinkedIn team count as of last week, PitchBook funding data as of Mar 15. When sources disagree, the agent flags the conflict instead of picking one silently. You always see the provenance.
Can it track targets over time, not just surface them once?+
Yes. Any target you mark "watching" goes into a monitored set — the agent tracks hiring velocity, product launches, funding events, and leadership changes, and pings you when meaningful signals fire. A target that's 9 months from "too early" to "ripe" becomes a tracked relationship, not a forgotten one-pager.
Does the agent surface proprietary deal flow from our relationships?+
No — it scans public and licensed databases only. The agent complements proprietary deal flow rather than replacing it. Most corp dev teams find the agent surfaces 30-40% of targets they wouldn't have seen through their network, while their relationship-driven deal flow stays as-is.
How much does AI M&A target screening cost?+
Included in every beeeowl deployment tier, starting at $2,000 for Hosted Setup. One-time payment — no per-target fee, no per-screen charge, no monthly database access tier. You bring your own licensed data subscriptions. See the pricing page for the full breakdown.