Cyndx

Cyndx is a purpose-built AI platform for investment professionals — investment bankers, PE/VC firms, family offices, and corporate strategists — that covers the full deal lifecycle from sourcing through valuation. Founded by an investment banker, the platform is organized around five interconnected tools (Finder, Acquirer, Raiser, Scholar, Valer) all drawing from a shared proprietary database of 33M+ private and public companies. It competes as an integrated alternative to assembling separate sourcing, research, investor-identification, and valuation tools.

Finder: NLP and ML-powered deal search and discovery across 33M+ companies; dynamic market mapping that escapes static industry classifications; lookalike search; patent-based search; filters for funding stage, geography, financials, and CRM/LinkedIn connections via the Connector feature. Acquirer: Predictive M&A targeting using machine learning to flag bolt-on acquisition fits. 'Projected to Raise' (P2R) algorithm: proprietary signal that flags private companies likely to seek equity within six months, claiming 86%+ precision. Raiser: Investor identification tool that matches deal parameters (sector, stage, size) against investor history and portfolio data; surfaces contact information for ~80M investors. Scholar: Generative AI deep-research tool that produces 15–20 page cited reports (plus summary doc and PowerPoint deck) on companies, industries, or deal scenarios in minutes using a multi-agent approach combining Cyndx's proprietary data with external sources. Valer: AI business valuation software that accepts uploaded financials and produces banker-grade reports with adjustable DCF, VC method, public comps, precedent transactions, WACC, and PGR. Cyndy: Conversational natural-language interface across all tools — runs searches, builds market maps, finds investors, pulls comps, and predicts funding events in plain language.

AI is central to the platform architecture, not a layer on top. NLP-driven dynamic mapping categorizes companies by what they do rather than static SIC/NAICS codes, updated daily. The P2R algorithm ingests large datasets to produce six-month fundraising predictions with claimed 86%+ precision. Scholar uses a multi-agent GenAI architecture combining Cyndx's proprietary 33M-company dataset with external resources to produce long-form, citation-backed research reports — not chatbot snippets. Cyndy, the conversational interface, uses routing algorithms to direct simple vs. complex queries to the right tool combination. No public API documentation found on the vendor site as of research date; no MCP server available or announced. API access is not publicly documented — likely available on enterprise contracts but not self-serve. Firms requiring programmatic data access or AI-agent integration via MCP should treat this as a gap.

Not publicly disclosed on the vendor site; Cyndx operates a subscription model with pricing negotiated directly. Third-party sources note pricing can be a barrier for smaller firms. Revenue is reportedly ~$5.7M ARR as of 2024 (per GetLatka), suggesting mid-market subscription tiers. Prospective customers must request a demo to receive pricing. No free tier confirmed for the core deal platform (Finder/Scholar/Valer); CyndX Owner (a legacy cap table product, now largely separate) has historically been cited at $45–$85/month on third-party directories, but those figures date to 2022 and should not be relied upon.

CRM integration referenced as a feature — users can upload CRM or LinkedIn contacts to the Connector feature to surface warm introductions within search results. Direct CRM integrations (Salesforce, HubSpot, etc.) are mentioned as a category but specific native connectors are not publicly documented. No Zapier, API marketplace, or third-party integration directory listing found. No MCP server. The platform is largely self-contained; external data flows in (LinkedIn contact upload, financial document upload for Valer, document upload for Scholar) but outbound integrations to external systems are not publicly detailed.

SOC 2 Type II certified — confirmed directly on the Cyndx platform and product pages. Explicit policy: customer data is never used to train AI models. Proprietary documents uploaded to Scholar or Valer are stated to remain private and secure. Encryption at rest and in transit implied by SOC 2 posture but not separately itemized on the vendor site. No ISO 27001, GDPR, or CCPA certifications publicly listed. SAML SSO and specific MFA requirements not publicly documented — verify with vendor during procurement.

Founded 2013; headquartered in West Palm Beach, Florida. Founded by Jim McVeigh (CEO), who brings 20+ years of investment banking experience at Salomon Brothers, DLJ, Credit Suisse, and Bank of America/Merrill Lynch (ran TMT banking), and Jay Kirsch (co-founder, now departed per Tracxn). CTO is Sebastian Okser. Total funding reported at ~$10M (per GetLatka and CB Insights), with investors including Rakuten Capital and Amino Capital; most recent funding round was 2017. Approximately 51 employees as of 2026. Reported ARR of $5.7M in 2024, up from $3.7M in 2023. Customer count not publicly disclosed; self-described as trusted by '100+ world-class organizations.' No major named enterprise logos publicly disclosed.

Moderate. Cyndx is explicitly designed for investment professionals and the deal workflow maps directly to a firm's sourcing and diligence process: Finder for market mapping niche sectors (deep-tech sectors, etc.), P2R signals for identifying companies approaching a raise, Scholar for rapid deep-research reports on targets, Valer for quick private company valuation benchmarks, and Raiser for identifying strategic co-investors. The 33M-company global database and daily-updated NLP categorization are well-suited to a firm's need to map emerging, intersecting verticals that lack clean industry classifications. Limitations keep this from a Strong rating: no public API or MCP server constrains AI-native workflow integration; pricing opacity requires a sales cycle; the platform is relatively small (~$5.7M ARR, 51 employees) raising questions about data freshness and enterprise SLA depth; and no publicly documented SAML SSO, which matters for a security-conscious firm. Strong alternative to PitchBook for niche private company discovery; weaker on public market data depth.

No public API documentation and no MCP server — limits programmatic access and AI-agent integration, a meaningful gap for an AI-native firm. Pricing is fully opaque and requires a sales demo cycle. Relatively small company (~51 employees, ~$5.7M ARR) may affect data coverage depth, refresh rates, and enterprise support SLAs. Access control specifics (SSO, SAML, MFA) are not publicly documented — must be verified in procurement. No named enterprise customer logos publicly disclosed, making peer validation difficult. CRM integrations are limited to contact upload rather than deep bidirectional sync. Coverage skews toward private companies; public-market data depth is thinner than PitchBook or Bloomberg.