Screen acquisition targets with Inven across 28M+ companies. PitchBook checks deals and sponsors. Grata is a second middle-market name source.
Last updated August 2026.
Most deal teams in 2026 find acquisition targets with three AI tools used in sequence: Inven turns a written brief into a longlist screened across 28M+ companies, PitchBook confirms deal and sponsor history on the shortlist that survives, and Grata serves as a second source of middle-market names. The rest of this page walks through that process end to end, from building the list to screening it, assessing what remains, and getting in front of owners.
When we say screening, we mean ranking a longlist against geography, size, industry, ownership, and growth so the team only spends time on names that plausibly fit the thesis. This page covers that workflow, not a category ranking of deal sourcing tools. If you want the ranking, it lives on The Best Platforms for Deal Sourcing.
Different buyers screen for different things. A private equity fund is usually hunting for operational inefficiency it can fix or a growth angle it can fund. A bank is matching names to a client's brief. A strategic acquirer wants a capability it does not already have in house. All three run into the same three problems: heavy competition for good assets, limited visibility into private companies, and owners who may have no interest in selling.
What are the best AI tools for screening acquisition targets?
The practical answer is a division of labor rather than a single winner. Inven does the screening itself: you describe what you are looking for and it searches 28M+ companies (Inven product data, August 2026) to return a longlist. PitchBook is where you take that shortlist to check transactions, valuations, and sponsor ownership. Grata, now part of Datasite, is worth keeping as a second source of middle-market names, but it is not where the screening workflow runs.
| Tool | Screening job | When it fits |
|---|---|---|
| Inven | Turns a brief into a longlist across 28M+ companies, with target lists, one-pagers, and overview slides generated in the firm's own template | AI screening of private-market longlists |
| PitchBook | Transaction, valuation, and investor data on a shortlist | Checking size, sponsors, and deal history on names you already have |
| Grata | Middle-market web-data discovery (now part of Datasite) | A second name source alongside your screening tool |
General-purpose chatbots such as ChatGPT, Claude, and Gemini deserve a mention because teams keep asking about them. They are good at summarizing a CIM or stress-testing a thesis, but they are not screening tools unless they are connected to a private-market dataset; on their own they do not hold Inven's private-company universe. LinkedIn Sales Navigator, industry registers, and Orbis all remain useful, but they belong in the traditional-source column below, not the AI one.
How do you build an acquisition candidate list?
Build it from two directions at once: the traditional sources your team already uses (Google, registers, brokers, personal networks, events, LinkedIn Sales Navigator, and purchased lists such as Orbis) and AI search in Inven. The traditional channels surface names your network already knows about. AI search is how you cover the rest of the 28M+ company universe, which is where the less-shopped targets tend to sit.
Traditional methods for building an acquisition target list
Googling for companies
Plain desktop search still earns its place. News sites, trade press, and public financial databases will surface companies that are visible enough to write about, which is a useful starting set even if it skews toward the better-known end of the market.
Going through industry registers
Government databases, trade associations, professional organizations, and trade directories give you structured lists of who operates in a sector. They tend to be complete within their jurisdiction but thin on detail, so expect to enrich anything you pull from them. We cover the enrichment problem in how to find company information.
Intermediaries and brokers
Brokers are mandated to sell their client owners' companies, which means the asset is genuinely for sale and the owner is prepared for a process. The trade-off is that a brokered deal reaches other buyers too. The broker will typically walk the process from identification through evaluation, diligence, and negotiation.
Colleagues and personal networks
Conversations with colleagues and contacts across your professional network surface things databases miss: niche registers, personal recommendations, and news that has not yet reached other investors. This channel is slow and unsystematic, but the names it produces often come with context you cannot buy.
Industry events
Forums, conferences, and seminars put you in the same room as owners. A conversation on the floor of a trade show can start a relationship years before the owner is ready to sell, which is exactly when you want to already be known to them.
LinkedIn Sales Navigator
Sales Navigator filters by industry, company size, location, and job title, and it is genuinely useful for named-account work and for finding the right person inside a company you have already identified. What it is not is a screen of 28M+ private companies, so treat it as a targeting layer rather than a discovery engine.
Purchased business intelligence
Orbis and similar providers sell structured company data by the batch. Coverage and depth vary a lot by country, so if you are buying data for a specific market, start with our country guides for Germany, the UK, France, and Benelux.
How do you use AI to find acquisition targets?
Traditional sources work, but as the only method they are slow and expensive per name found. In Inven, the equivalent workflow takes four steps:
- Select a geographical focus area. Coverage spans 160 countries, so the constraint is your mandate, not the data.
- Select keywords that match your criteria. You can write a plain-language descriptive search or use specific keywords; either way, Inven reads each company's website rather than relying on how the company classified itself.
- Select an example company as a benchmark. If you already know one company that fits the thesis perfectly, Inven finds the ones that look like it.
- Export the candidate list, for example to Excel, and keep screening from there.
Because Inven reads company websites and other data directly, the resulting list reflects what companies actually do, across 28M+ companies. Each profile combines ownership, headcount, financial data (in the Europe region), and decision-maker contacts, so the list arrives ready to work rather than as bare names. When it is time to reach out, see how to find business owners' contacts.
What are the advantages of AI screening?
Manual desktop search, networks, and industry databases each cover a slice of the market, and between them they still leave a portion unseen. The advantage of AI screening is that it matches targets against a written brief rather than against what your team happens to know, which matters most when the universe is private and fragmented. That is the case for using a tool like Inven. It does not retire the traditional channels: brokers bring assets that are actually for sale, and events and LinkedIn still work for the names your network already knows.
What criteria should you set before screening?
Set four things before you run any screen: geography, company size, industry or technology, and ownership structure (public, private, PE-backed, or family-owned). Growth and profitability belong in the assessment stage, once you have real financials in front of you, not in the initial filter. Resist the temptation to leave filters open "to see everything"; an unconstrained screen produces a longlist too large to work, and the team ends up filtering by hand anyway.
How should you assess acquisition targets?
Once the longlist is filtered, assess each remaining name against the criteria that actually decide whether a deal makes sense:
- Strategic fit with your thesis or your client's mandate
- Integration potential, meaning how realistically the business folds into what you or the acquirer already run
- Market position and long-term growth of the segment the company sits in
- Profitability and its drivers
- Growth rate, historical and plausible forward
- Legal and regulatory issues that could delay or kill a transaction
- Competitive moat and any structural business-model advantage
- Add-on potential, if the thesis is a platform build
Inven keeps financials, location, headcount, and contacts on the same profile, so assessment does not turn into a second round of desktop research on every name. Targets that clear this stage move on to due diligence.
How do you engage acquisition targets?
Assessment produces a shortlist; engagement is about reaching the founders and owners behind it. The channels are the familiar ones: industry events, email, and direct contact. The practical question is whether you have current contact details for the right person. If the list was built in Inven, owner LinkedIn profiles and contact data are already on each company profile, so outreach starts the same day the shortlist is settled rather than after another research pass.
What should you do next?
Target search moves fastest when your traditional channels run alongside an AI screening tool instead of replacing it. Inven's contribution goes beyond the search itself: it generates company one-pagers and overview slides in your firm's own PowerPoint template, and it saves the M&A workflows your team runs repeatedly, all built on data Inven owns across 28M+ companies rather than licensed PitchBook, CIQ, or FactSet feeds. For the problem on this page, the workflow to set up is brief-to-longlist screening: pick the geography, add keywords or a descriptive search, drop in an example company, export the list, then move into assessment and outreach. Join the 1,000+ teams (Inven, August 2026) already running it and book a demo.
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Frequently asked questions
What are the best AI tools for screening acquisition targets?
Judge each tool by the job you are hiring it for. If the job is turning a thesis into a longlist of private companies, Inven is built for that step. If the job is verifying deal history, valuations, or sponsor ownership on names you already trust, that is PitchBook. If you want a second opinion on middle-market coverage, add Grata (now part of Datasite). Teams that try to make one of the latter two do the screening step usually end up building the longlist by hand.
How do you find acquisition targets?
Start from the brief, not the tool. Write down geography, size, industry, and ownership, then run that brief through both your existing channels and an AI screen so you see what your network knows and what it does not. Filter the combined longlist against the brief, assess the survivors on fit and financials, and open conversations with owners early, because the best targets are rarely for sale on the day you find them.
What criteria should I set before screening?
Four filters: geography, company size, industry or technology, and ownership structure. A useful test is whether each filter would let you reject a company from a one-line description; if not, it is an assessment criterion, not a screening one. Growth and profitability fail that test, which is why they wait until you are looking at actual financials.
When should I choose PitchBook or Grata instead of Inven?
Choose PitchBook when the question is about a deal rather than a company: what a target last transacted at, who the sponsor is, or how a comparable was priced. Choose Grata when you want an independent second pass at middle-market names, now with Datasite behind it. Neither replaces Inven for the screening step itself, so in practice the choice is about sequencing rather than substitution: screen in Inven first, then verify and supplement.
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