Sourcescrub alternatives: Grata, private-company data and AI research

Executive Summary

The private-company deal-sourcing market that a corporate development or M&A team is buying into in late 2026 looks nothing like the one Sourcescrub defined a decade ago. Three shifts have redrawn the map. First, consolidation: Datasite, backed by a $500 million commitment from owner CapVest, bought Grata and then Sourcescrub in 2025 and has stated that Sourcescrub's data and capabilities "will ultimately merge" into Grata [72][79]. A buyer signing a Sourcescrub contract today is, in practice, buying a roadmap into Grata. Second, the AI turn: semantic and agentic search has moved from differentiator to table stakes — Grata (Agentic Search, MCP server), Sourcescrub (SourcingGPT), PitchBook (Navigator) and S&P (ChatIQ) all now ship it [9][31][39][72][78]. Third, the emergence of AI-native workbenches such as CorpDev.Ai, which reframe the purchase from "which database?" to "how much of the analyst workflow — sourcing, research, screening, pipeline, memo — should software do?" [50][64].

The verdict in one paragraph. For a team whose sourcing edge depends on where companies appear — trade-show exhibitor lists, association rosters, buyer's guides — Sourcescrub's source-first data remains genuinely differentiated, and the rational move is to buy it through Grata with migration protections written into the contract rather than as a standalone product with an uncertain shelf life. For a team whose edge depends on what companies do — thesis-driven semantic search across a very wide universe, followed by fast research, screening and memo production — CorpDev.Ai offers materially broader workflow coverage at published prices that overlap the cited Sourcescrub range, but with a younger data layer (Apollo firmographics plus on-demand AI profiling rather than a human-curated proprietary universe) and a shorter track record that a buyer must test rather than assume [64][65]. PitchBook and Capital IQ Pro remain the right answer when transaction, valuation and ownership data outweigh origination. Most serious teams will run a two-layer stack — a discovery/intelligence layer plus a relationship/workflow layer (Affinity, DealCloud or CorpDev.Ai's built-in pipeline) — and the real budgeting question is how many layers one vendor can credibly collapse.

The axes describe a useful procurement distinction, not an objective ranking: depth and provenance of usable data versus breadth and quality of work performed. Original visual’s “no native company universe” CRM assertion was overbroad; assess each integration and entitlement.

Three decisions that determine the right answer

• Is your target universe visible on the open web or only at trade shows? If your best targets are founder-owned industrial, healthcare-services or specialty-distribution businesses with thin web presence, conference and association lists are the highest-signal data available and Sourcescrub/Grata own that asset [1][3][75]. If your targets are software, services or consumer businesses that describe themselves online, semantic search over websites (Grata, Inven, CorpDev.Ai) will find them at lower cost.
• How many analyst hours do you want the software to absorb? A database saves search time; a workbench saves research, screening, drafting and CRM-entry time. CorpDev.Ai's economic case rests on the second category and is only compelling if the team actually produces memos, market maps and screening output at volume [58][64].
• What is your tolerance for vendor transition risk? Sourcescrub carries integration risk (feature parity, repricing, forced migration timing that Datasite has not published) [72]; CorpDev.Ai carries early-stage vendor risk (limited independent review base, smaller company). PitchBook and Capital IQ carry the least platform risk and the highest price.

1. Why This Comparison Matters Now

For most of the 2015–2024 period, the deal-sourcing software decision was stable and binary: institutional teams bought PitchBook or Capital IQ for transaction data, and origination-focused teams added Sourcescrub or Grata for private-company discovery. Renewals were routine and the vendors' positions were clear. Four developments in 2025–2026 have made the decision genuinely open again, and a buyer who treats a Sourcescrub renewal as business-as-usual is likely to overpay for a product whose future form is not yet defined.

What the consolidation means for a buyer. Datasite now owns the two products that between them defined private-company origination for a decade. The stated end-state is one Grata-branded platform carrying Sourcescrub's conference and list data — Grata's 2026 disclosures already cite 230,000 conferences and industry lists alongside 23 million companies, figures consistent with integrating source-list assets, though the exact contribution is not independently established here [75]. What Datasite has not published is a migration timetable, a feature-parity commitment for Sourcescrub-specific workflows (scoring rules, tracked-company tiers, Data Connect), or a pricing framework for legacy customers [72]. This is not unusual in a platform consolidation, but it converts a routine renewal into a negotiation: the buyer's leverage is highest precisely while the vendor needs retention through the transition.

What the AI turn means. Twelve months ago, natural-language search was a reason to choose Grata over Sourcescrub. Today every serious vendor has it, so the differentiation has moved one level up: to the depth of what the AI can answer (a firmographic field versus a business-model analysis) and to how far downstream the AI goes (search results versus a screened shortlist versus a drafted memo). This is where the category divides between data vendors that have added AI and AI products that have added data — and it is the central lens of this comparison.

2. How to Evaluate a Deal-Sourcing Platform

Vendor comparison pages — including those published by every company in this report — are built around whichever metric flatters the author: company counts, source counts, review scores, or price. None of those alone predicts whether a tool will change a team's deal flow. The five criteria below are the ones that, in practice, separate platforms that get renewed from platforms that become shelfware.

The decision is how much of the seven-stage process one vendor should handle. Coverage labels describe scope, not a measured outcome or complete replacement of specialist data and governance.

A practical corollary: run the evaluation on a live mandate, not a demo dataset. Give each shortlisted vendor the same real thesis — for example, "founder-owned specialty-chemicals distributors in DACH with 50–300 employees" — and measure three things: how many genuinely relevant companies each surfaces that the others miss, how many of the returned companies are wrong or defunct, and how long it takes to get from the query to a screened shortlist an executive would look at. Those three numbers, not the marketing counts, are the basis for the purchase decision.

3. Sourcescrub: The Incumbent Under New Ownership

Sourcescrub built its franchise on an insight that remains valid: the best private-company targets for a proprietary deal are often invisible to conventional databases but highly visible to their own industries — as exhibitors at trade shows, members of associations, entries in buyer's guides, and winners of regional awards. Rather than starting from a company registry, Sourcescrub crawls those "sources" and links them to company profiles, producing a universe of roughly 16–17 million companies connected to 220,000–290,000 sources (figures vary across the company's own materials and the Datasite acquisition release) [1][2][3].

What the product does well

Source-first discovery is a real moat. For sectors where companies do not describe themselves online in detail — industrial services, distribution, healthcare providers, construction, food manufacturing — the fact that a company exhibited at a specific specialist trade show is frequently the single most useful qualifying signal available, and it is a signal whose systematic collection and maintenance can be difficult to replicate [3][4]. Investment banks use the same data in reverse to plan conference calendars and identify buyers.

Tracking, scoring and CRM enrichment are mature. The Plus and Professional tiers add configurable scoring rules and tracked-company lists (250 and 1,000 companies respectively), while the CRM/Data Connect tier syncs and enriches Salesforce-style records with employee counts, growth rates and funding data; a Chrome and Firefox extension pushes companies into CRM accounts and leads while browsing [6][7][8]. SourcingGPT, launched December 2024, provides a natural-language layer over the company's own company, source, contact and investment data, hosted in Sourcescrub's Azure environment [9].

Customer roster is institutional. Publicly cited users include TA Associates, Hg, Permira, Vector Capital, AKKR and General Catalyst — a signal that the data holds up under demanding PE origination teams [16][17].

Where it falls short

Independent reviews are consistent on four points. Data accuracy and freshness is the most frequent complaint on G2 — inconsistent company records and outdated contacts, with at least one Capterra reviewer citing a high email bounce rate [20][21][22]. The interface has a learning curve; the breadth of filters, source types and scoring rules is powerful but described by several reviewers as clunky or slow [20][23]. Search sensitivity — some users report needing a near-exact legal entity name to find a company — undercuts the discovery promise for teams that work from informal names [22]. And conference-list timing can lag; one reviewer noted attendee data arriving only weeks before an event [23]. Finally, price: Sourcescrub publishes no list prices, and third-party estimates place typical deployments at $20,000–$60,000 per year, rising to $60,000–$100,000+ with multiple seats, Data Connect API or custom research [6][10][11][12]. For a two- or three-person corporate development team, that is a significant line item for a discovery database that still requires a separate CRM and separate research effort.

Basis: tier structure and limits from Sourcescrub's pricing page [6]; dollar ranges are market estimates from third-party comparison sites, not vendor list prices [10][11][12].

The ownership question

Sourcescrub is no longer a Francisco Partners portfolio company. Datasite acquired it on 8 August 2025 with the explicit intent to combine it with Grata, which Datasite had bought earlier that year [14][15][72]. Grata's 2026 metrics — 23 million companies and 230,000 conferences and industry lists — indicate the data is already flowing into the Grata platform [75]. For a buyer, this changes Sourcescrub's risk profile in two directions at once. On the upside, the source data now sits inside a better-funded, AI-forward platform with a $500 million owner commitment, and the eventual combined product should be stronger than either predecessor [79]. On the downside, Sourcescrub's own application, scoring model, tracked-company tiers and Data Connect API have no published continuation guarantee, and no migration timetable or legacy-pricing framework has been disclosed [72].

Buyer's bottom line on Sourcescrub. Buy it for the conference and source-list data if that data matches your target universe — test competing source-list coverage rather than assuming all vendors collect the same lists. Do not buy it as a standalone platform without contractual protection on migration, data continuity and price. And recognise that even at its best it covers only the discovery and tracking stages of the workflow; research, screening narrative, pipeline and memo production remain the team's job or another vendor's.

4. The Alternatives

The twelve platforms below fall into six groups: the direct successor (Grata), the AI-native workbench (CorpDev.Ai), the institutional terminals (PitchBook, Capital IQ Pro), specialist discovery engines (Inven, Cyndx, Udu), venture-oriented databases (Crunchbase, Dealroom, Tracxn) and the workflow layer (Affinity, DealCloud). Each profile applies the five criteria from Section 2 and closes with who should — and should not — buy it.

4.1 Grata (Datasite)

Grata is the closest functional substitute for Sourcescrub and — since 2025 — its designated successor within Datasite. Where Sourcescrub started from sources, Grata started from company websites, using machine-read business descriptions to power semantic search: a user describes what a company does and Grata returns lookalikes, including firms that appear in no funding database [25][68][69]. Its 2026 figures are the largest in the private-company discovery segment: more than 23 million companies, 230,000 conferences and industry lists, 180,000 companies with filing data, 1 million transaction events, 11.5 million contacts and 600 research analysts, with customers in 26 countries [75].

Who should buy it. Lower-middle-market PE, independent sponsors and corporate development teams whose targets are North American or Northern European private companies with a web presence, and who value discovery quality above all. Existing Sourcescrub customers should treat Grata as their default renewal path and negotiate the combined package.

Who should not. Teams that need transaction multiples and valuation history (PitchBook/Capital IQ), teams that need the software to do the research and drafting (CorpDev.Ai), or budget-constrained teams for whom a $30,000+ annual database is disproportionate to deal volume.

4.2 CorpDev.Ai

CorpDev.Ai represents the category's newest architecture: an AI-native workbench that treats the company database as one input to an agentic analyst rather than as the product itself. Founded by Kal Kilpi (co-founder of M&A SaaS Midaxo) and Atul Tiwary (20+ years in M&A and investment banking), it packages sourcing, company intelligence, market mapping, screening, pipeline CRM, monitoring, data-room analysis and document generation into a single subscription with published pricing and a 14-day self-serve trial [50][62][63][64].

What is genuinely different

The unit of value is the deliverable, not the search result. CorpDev.Ai's AI analyst researches a company or sector from public sources and data-room files, produces cited profiles, market maps, screening scores, investment memos and board decks, and maintains the pipeline — the work that, in a Sourcescrub- or Grata-equipped team, is done by an associate after the database has returned a list [53][54][58]. The pricing page lists memo generation, market research reports, PowerPoint generation, AI fit scoring, a "zero-entry" CRM that builds the pipeline from Microsoft 365 or Google Workspace email and calendar activity, and data-room ingestion in all tiers [56][64].

Coverage is broad by construction, not curation. The 70M+ figure derives from an Apollo firmographic database (named on the pricing page) supplemented by Google Maps geographic search and on-demand AI profiling of any company a user names, whether pre-indexed or not [64][65]. This is the reverse of Sourcescrub's model: breadth and instant depth-on-request rather than a human-validated proprietary universe.

Commercial model is the opposite of the incumbents. Published prices — $1,000/month (AI Pro, one seat, 12,000 search credits per year) and $3,000/month (AI Pro Team, three seats, 36,000 credits, dedicated CSM) invoiced annually, or 20% more by monthly card; Enterprise on quote with SSO, unlimited seats and financial modelling — plus monthly terms and cancel-anytime flexibility [64]. For a three-person corporate development team the base Team subscription is $36,000 annually before usage or Enterprise requirements. This overlaps the cited Sourcescrub firm-level and Grata seat-level ranges; the billing units and included scope differ.

Where a buyer should push back

• "70M vs 20M" is a vendor framing, and Grata now reports 23M. More important, the two numbers measure different things: Apollo-sourced firmographic records versus Grata's curated, analyst-validated profiles with 11.5 million contacts and 230,000 source lists [65][75]. Breadth helps recall; curation helps precision. A buyer should test both on the same mandate.
• No proprietary conference or source-list data. CorpDev.Ai's pricing page lists "Conference Management" as a CRM feature, but it does not claim a crawled universe of exhibitor and association lists [64]. For trade-show-driven sectors this is a real gap relative to Sourcescrub/Grata.
• On-demand AI profiles need verification discipline. The vendor itself states that "we always recommend human review for critical decisions" [64]. A profile generated in minutes from public web sources is a first draft, not a diligence finding; teams must budget review time and treat citations as things to click, not decorations.
• Limited independent review base. Unlike Sourcescrub (56 G2 reviews) and Grata (81), CorpDev.Ai has no comparable body of third-party reviews surfaced in this research; the customer claims ("hundreds of CorpDev professionals") are the vendor's own [64]. The 14-day trial mitigates this, but a buyer should ask for reference customers in its own sector.
• Contact data and outreach are secondary. Apollo provides people search, but the platform is not positioned as an outreach engine, and bounce-rate performance is untested here [64].
• Vendor scale. A young company with a two-founder leadership profile carries continuity risk that Datasite (CapVest-backed) and Morningstar (PitchBook) do not [63].

Who should buy it. Corporate development and strategy teams (one to ten people) that run thesis-driven searches across broad or international universes, produce written deliverables at volume, lack a dedicated deal CRM, and prefer transparent, flexible pricing. Also teams already paying for PitchBook or Grata that want an analyst layer on top rather than a second database.

Who should not. PE origination teams whose edge is conference and intermediary coverage in offline sectors; banks that need transaction-level comps and league-table data as the primary asset; institutions that require a large installed base and long vendor track record before adopting a system of record.

4.3 PitchBook and S&P Capital IQ Pro

PitchBook and S&P Capital IQ Pro are not Sourcescrub substitutes so much as the reference terminals that Sourcescrub-type tools were built to sit beside. Both are broad, institution-grade datasets whose strength is transactions and financials — who owns what, what was paid, at what multiple — rather than the discovery of obscure founder-owned businesses.

When they are the right answer. A corporate development team that regularly values targets, benchmarks against precedent transactions, or reports to a board that expects institutional comps needs one of these regardless of what it uses for discovery. A bank or PE fund will already have one. The buying question is therefore rarely "PitchBook or Sourcescrub" but "PitchBook plus which discovery/workflow layer" — and, increasingly, whether an AI workbench can reduce the number of PitchBook seats a team pays for by absorbing the research that junior staff currently perform inside it.

When they are not. As a primary discovery tool for founder-owned businesses with no financing history, the sourcing tools warrant a sector-specific recall test. This review does not establish that every sourcing tool beats both terminals on recall or costs less per seat.

4.4 Inven, Cyndx and Udu

Three specialist engines occupy the middle ground between the Datasite duo and the terminals. Each is narrower than Grata but often faster or cheaper for a specific job.

Comparative note. All three deliver the semantic-discovery function that made Grata famous, generally at lower or comparable cost, but none carries Grata's combined data assets (source lists, transaction events, 11.5 million contacts) and workflow scope varies; Inven explicitly offers one-pagers, so the distinction is the breadth and editability of deliverables and pipeline functions, not their complete absence. They are best understood as tactical tools for a specific mandate type, or as lower-cost discovery layers beneath a research terminal.

4.5 Crunchbase, Dealroom and Tracxn

These three are frequently listed as Sourcescrub alternatives, but they serve a different universe: venture-backed and technology companies, where funding events rather than trade-show appearances are the organising signal. For a corporate development team whose thesis involves acquiring startups or scale-ups, one of them is usually necessary; for a team acquiring profitable, founder-owned operating businesses, none is sufficient.

Positioning versus Sourcescrub. A Sourcescrub user acquiring in, say, industrial automation will find Crunchbase surfaces the venture-funded sensor startups and misses the 40-year-old family-owned integrators; Sourcescrub does the reverse. Dealroom is the strongest of the three for European innovation landscapes and is often paired with Grata or Inven by European corporates. For teams already using CorpDev.Ai, PitchBook or Grata, Crunchbase Pro at under $600 per year is a low-cost supplement for funding alerts rather than a competitor.

4.6 Affinity and DealCloud — the Workflow Layer

Affinity and DealCloud are not company databases and should not be evaluated as Sourcescrub replacements. They are the systems into which Sourcescrub, Grata and PitchBook data flow — the relationship and pipeline layer that a discovery tool alone does not provide. They belong in this comparison because their cost is part of the true price of a Sourcescrub-based stack, and because AI workbenches such as CorpDev.Ai now bundle a native pipeline CRM that may make a separate purchase unnecessary for smaller teams.

The stack implication. A mid-sized PE or banking team typically runs discovery (Sourcescrub/Grata) + terminal (PitchBook) + CRM (Affinity or DealCloud), and the CRM alone can match or exceed the discovery tool's cost. CorpDev.Ai's "zero-entry" pipeline — which builds the deal timeline from Microsoft 365 or Google Workspace activity — targets the Affinity function directly for small corporate development teams [56][64]. Whether it is a full substitute depends on the team's need for relationship scoring across a large partner network (Affinity's strength) or complex permissioned workflows (DealCloud's); for a three-person corporate strategy group, a bundled pipeline is usually enough.

5. Head-to-Head Comparison

The matrix below scores each platform on the five buying criteria from Section 2. Scores are analyst judgements (1 = weak, 5 = strong) grounded in the evidence cited in Sections 3–4; they are relative to the needs of a corporate development or M&A team sourcing private companies, not absolute product quality.

Basis: universe fit and signal quality reflect vendor-disclosed coverage and independent review sentiment [1][3][25][26][36][64][75]; workflow depth reflects the stages covered in the Section 2 diagram; cost reflects published or third-party-estimated pricing and contract terms [6][10][27][29][39][64]; trajectory reflects ownership, funding and AI release velocity [72][78][79]. Sourcescrub standalone is scored down on trajectory because of the undisclosed merger timetable; Grata is scored on the combined data asset.

Feature-by-feature

Counts are vendor-stated and not comparable measures of usable coverage. Stage counts are coarse author judgments; Inven one-pagers and terminal/CRM AI features make categorical “no deliverables” boundaries inappropriate. Cost scenarios differ in entitlement and configuration; see Section 6.

6. Total Cost of Ownership — What the Stack Really Costs

No team buys a discovery database in isolation. It sits inside a stack that also has to cover transaction data, relationship management and the production of research and investment materials — and the true comparison is stack against stack. The three archetypes below cost a three-person corporate development team over one year at indicative prices. Every figure is a list price or a third-party market estimate; quotes from sales-led vendors vary widely and should be obtained directly.

Reading the numbers. Stack A and Stack B cost roughly the same; the consolidation trades a separate CRM for a higher discovery bill and, in exchange, delivers the strongest proprietary private-company data available. Stack C is about a third cheaper on software — but that understates the intended economic difference, because the workbench is priced to replace labour, not tools. If the AI analyst reliably drafts the research profiles, screening rationales and first-draft memos that an associate would otherwise produce, the relevant comparison is against a fraction of a full-time salary, not against a $40,000 database. If it does not reach editable quality in the team's sector, the saving collapses to the software delta and the team has lost Sourcescrub's source data for it.

For institutional PE and banking teams of ten or more seats, the arithmetic shifts: PitchBook and a configured DealCloud become fixed costs, Grata's per-seat pricing scales, and the AI workbench is more plausibly an additional analyst layer than a replacement for the data stack. For those buyers the question is whether CorpDev.Ai at Enterprise pricing reduces junior headcount growth, not whether it replaces Grata.

7. Recommendations by Buyer Profile

The scoring tie in Section 5 resolves cleanly once the buyer's profile is specified. Five profiles cover most corporate development, strategy and M&A teams.

Profile 1 — Corporate development team, 1–5 people, thesis-driven acquirer

Recommendation: CorpDev.Ai as the primary platform, tested on a live mandate; one PitchBook or Capital IQ seat if the board expects precedent-transaction comps. This profile produces the most deliverables per head and has the least analyst capacity; the workbench's value is highest here and its price ($12,000–$36,000 published) provides a visible licence budget, subject to the buyer’s own procurement requirements [64]. The conditions: run the 14-day trial against the team's actual thesis, measure profile accuracy in the team's sector, and confirm that the AI output is edited rather than redone. If the team's targets are in trade-show-driven sectors, add Grata for discovery rather than expecting CorpDev.Ai to replicate source-list coverage [65][75].

Profile 2 — Lower-middle-market PE or independent sponsor

Recommendation: Grata (with Sourcescrub data) as the discovery layer; Affinity or Datasite Pipeline for relationships; PitchBook seat for comps. Proprietary origination in fragmented offline sectors is this profile's entire edge, and Grata's combined universe — semantic search plus conference and intermediary signals — is the strongest asset in the category [72][75]. Negotiate hard: Datasite is mid-integration and needs retention. Consider CorpDev.Ai as an added analyst layer for IC memo drafting once deal flow justifies it.

Profile 3 — Existing Sourcescrub customer at renewal

Recommendation: Do not renew standalone. Move to the Grata package with written protections, or run a parallel trial of CorpDev.Ai and Inven and decide on evidence. Obtain, in writing, whether the contract renews as Sourcescrub or Grata; which datasets, scoring configurations and tracked lists carry over; export rights during migration; price protection for at least one cycle; and the replacement roadmap for any discontinued feature [72]. Use the transition as leverage on price.

Profile 4 — Investment bank or M&A adviser

Recommendation: PitchBook or Capital IQ Pro as the core, Grata for sector mapping and buyer identification, DealCloud as the operating system. Advisers need transaction depth, buyer universes and conference planning simultaneously; this is the most expensive stack and the one where the consolidated Datasite offering (Grata → Pipeline → VDR) has genuine end-to-end logic [73]. CorpDev.Ai is a candidate for pitch and CIM drafting at the Enterprise tier, not a data replacement.

Profile 5 — Corporate venture or innovation team acquiring startups

Recommendation: Dealroom (Europe) or Crunchbase Business (US/global) for the venture universe; PitchBook if the team invests as well as acquires. Sourcescrub and Grata are mis-matched to this universe; funding events, not trade shows, are the organising signal. CorpDev.Ai's on-demand profiling is a useful complement for rapid diligence on shortlisted startups, since venture-backed companies describe themselves richly online [65].

8. Diligence Questions to Ask Every Vendor

Every vendor in this report will win a demo on its own dataset. The questions below are designed to be asked of all shortlisted vendors in the same words, with answers in writing, so that the evaluation compares like with like.

Universe and data quality

• Run our live thesis (provide it) and return the top 50 targets. How many are operating, independent and correctly described? We will check ten at random.
• What proportion of the returned companies has verified revenue or headcount rather than an estimate, and how is "verified" defined?
• For contacts: what is your measured email bounce rate over the last two quarters, and how is it measured?
• What share of your company universe was updated in the last 90 days?

Signal and coverage

• Which conference, association and directory sources do you crawl for our sector, and how far in advance of an event does attendee data appear?
• How do you handle companies not in your database — is there an on-demand path, and what does it cost in credits or fees?
• What geographic coverage depth can you demonstrate outside North America for companies under 200 employees?

Workflow and AI

• Show a full path from a natural-language thesis to a screened shortlist to a first-draft profile. Which steps are automated, which are human, and where are sources cited?
• What is the escalation path when the AI is wrong — can we see, correct and lock a fact?
• Is our data — theses, uploaded files, pipeline — used to train models or shared across customers? (CorpDev.Ai states it is not [64]; obtain the same statement from every vendor.)
• Do you offer API, Excel or MCP access, and is it included or an add-on?

Commercial and vendor

• What is the total three-year cost for three users including exports, connectors, API, seats and expected renewal uplift?
• Is monthly or quarterly commitment available? What are the cancellation terms?
• For Datasite (Sourcescrub / Grata): What is the Sourcescrub migration timetable, which features are guaranteed to persist, and what price protection applies to legacy customers [72]?
• For CorpDev.Ai: Provide three reference customers in our sector; what is the company's funding runway and customer count; and how are search credits consumed by a typical screening project [64]?
• For PitchBook / Capital IQ: What has been the average renewal uplift for comparable customers over the last three years [29][41]?

Key Facts & Sources

The load-bearing figures in this report, their sources and as-of dates. Figures marked "estimate" are third-party market ranges, not vendor list prices; figures marked "vendor claim" have not been independently verified.

Method notes. Company counts are not comparable across vendors: Sourcescrub and Grata count curated profiles, CorpDev.Ai counts Apollo firmographic records plus on-demand generation, Capital IQ counts legal entities including public companies. Pricing for sales-led vendors is drawn from third-party comparison sites (principally Prospeo, CostBench, Investables and G2), which are themselves marketing channels for competing products; ranges are shown rather than point estimates for that reason. Review counts and ratings were taken from G2 and Capterra pages as surfaced in September 2026 and will drift. This report was prepared for CorpDev.Ai; vendor claims for CorpDev.Ai were checked against its own published pricing and comparison pages and are labelled as vendor claims wherever no independent corroboration was found.