Velvet raised a seed round in 2020, with investors in the company including Winklevoss Capital, Material V, Singularity Capital, Atma Capital, IronPine, Alumni Ventures, and Arieli Capital.

Velvet is an AI copilot designed specifically for private markets investors. The platform connects to all the scattered data sources that venture capital and private equity professionals use during deal evaluation - pitch deck folders in Google Drive and Dropbox, email threads with entrepreneurs, Excel spreadsheets tracking pipeline, data room documents, and notes from investor calls.

Once connected, Velvet uses large language models to parse and index all this unstructured information into a searchable knowledge graph. An investor can then ask natural language questions like "show me all SaaS deals over $2 million ARR we saw last quarter" or "summarize the traction risks in the Acme Seed deck." The AI pulls relevant information from across all connected sources and provides answers with citations back to the original documents.

The platform automates much of the manual work in investment workflows. It can generate first-draft investment memos by pulling key metrics and risk factors from pitch decks and due diligence materials. It flags missing diligence items by comparing deals against the firm's standard checklist. It surfaces comparable company data to help with valuation analysis.

Velvet also includes collaboration features that let investment team members share and comment on AI-generated outputs, similar to Google Docs. The platform sends Slack and email notifications when new information becomes available or when teammates ask questions about specific deals.

The company emphasizes security and privacy, promising never to train its models on user data and using enterprise-grade encryption to address limited partner confidentiality concerns that are critical in the private markets.

Velvet operates a B2B SaaS model targeting institutional investors in private markets. The company charges subscription fees typically ranging from $5,000 to $60,000+ per fund, positioning itself as premium workflow automation software.

The platform's value proposition centers on time savings and decision quality improvements. By automating what Velvet claims is 80% of the workflow from initial pitch deck review to investment decision, the software allows smaller investment teams to evaluate more deals while maintaining thorough due diligence standards.

Velvet's go-to-market approach focuses on direct sales to mid-market venture capital and private equity funds. These firms typically have 5-15 investment professionals who spend significant time on manual data gathering and analysis but lack the resources to build custom AI tools in-house.

The company has also launched a marketplace component through its partnership with Templum, positioning itself as a front-end for secondary trading of private shares. This creates a potential transaction-based revenue stream beyond the core SaaS subscriptions, though the marketplace appears to be in early stages.

Velvet's cost structure benefits from the scalable nature of AI software, where the same underlying models and infrastructure can serve additional customers with minimal incremental costs. The company recruits specialized talent in both financial services and machine learning to maintain its vertical focus advantage.

Affinity has emerged as a major competitor with over 3,000 funds using its relationship intelligence CRM. The company launched AI-powered features in 2024, including automated note-taking and industry insights generation. Affinity's strength lies in its deep email and calendar integration that creates network effects around relationship data, making it difficult for funds to switch once they've built up their contact graph.

ListAlpha focuses specifically on mid-market private equity and has gained traction in Europe by offering GPT-based semantic search and built-in company research. The platform has attracted over 20 private equity customers who migrated from legacy systems like DealCloud in less than 12 months, competing directly with Velvet's private equity focus.

4Degrees and newer entrants like Edda and Attio are building relationship-centric CRMs with machine learning recommendations. While they have smaller user bases, these companies iterate quickly and could undercut Velvet's pricing to gain market share among emerging fund managers.

DealCloud, owned by Intapp, dominates the large fund segment with approximately 1,600 private equity and credit clients. The platform offers end-to-end deal and investor relations modules but faces usability complaints that create opportunities for newer entrants. DealCloud's recent AI initiatives show incumbents are responding to the threat from AI-first platforms.

Dynamo and eFront serve similar roles in the established private markets software ecosystem. These platforms have deep integration with fund administration and back-office systems, creating switching costs that protect their installed base but limit their ability to innovate quickly.

Microsoft, Salesforce, Notion, and Airtable are embedding AI copilot features into their general-purpose platforms. While these lack Velvet's private markets specialization, they could capture price-sensitive customers who prefer familiar interfaces over vertical-specific tools. The risk is particularly acute for smaller funds that might choose a $20-per-month Notion AI upgrade over a $60,000 specialized platform.

Velvet can expand from AI-powered due diligence into a comprehensive deal operating system. The company's existing document parsing and knowledge graph infrastructure could power portfolio monitoring dashboards, LP reporting automation, and fund modeling templates. These adjacent workflows would allow Velvet to increase seats per fund by 30-40% while leveraging the same underlying technology.

The platform could also develop an API layer that lets other applications query Velvet's private company embeddings and analysis. This would transform Velvet from an interface into a data infrastructure provider, expanding the addressable market from 15,000 global VC and PE firms to any of the 300,000 professional services organizations that work with private company data.

Velvet's partnership with Templum for secondary trading creates opportunities to earn transaction fees on private share trades rather than just SaaS subscriptions. A functioning transaction layer could generate revenue from the multi-billion dollar private secondaries market.

Emerging managers represent a significant growth opportunity. Sub-$100 million micro-VCs and solo general partners lack analyst capacity, making them ideal customers for AI automation. A lower-priced, self-serve tier could triple Velvet's logo count while maintaining AI-driven gross margins.

Family offices and corporate venture arms face similar data management challenges but often have larger budgets than traditional VC funds. Corporate venture arms, numbering over 2,000 globally, could provide higher average contract values while expanding Velvet's market beyond traditional fund structures.

The platform could also target wealth managers and investment advisors who work with high-net-worth individuals interested in private market allocations. This would significantly expand the potential customer base beyond institutional fund managers.

Velvet's planned Middle East office provides access to Gulf sovereign wealth funds and family offices that are rapidly increasing private market allocations. The region's growing venture capital ecosystem creates demand for sophisticated deal management tools.

European expansion could leverage AI privacy regulations to Velvet's advantage. The company's commitment to not training models on user data positions it well for EU compliance requirements, potentially creating a competitive moat against US-based competitors in European markets.

Asian markets, particularly Singapore's growth equity ecosystem, represent another expansion opportunity. These markets are early adopters of modern software tools and could provide geographic diversification for Velvet's revenue base.

Model commoditization: As large language models become more capable and accessible, Velvet's core AI advantage could erode. OpenAI, Anthropic, and other foundation model providers are rapidly improving their ability to analyze financial documents and generate investment insights, potentially making Velvet's specialized models less differentiated. If general-purpose AI tools can match Velvet's performance on private markets tasks, customers might choose cheaper horizontal solutions over specialized vertical software.

Market concentration: Velvet's success depends heavily on continued growth in private markets fundraising and deal activity. Economic downturns that reduce venture capital and private equity activity could significantly impact demand for deal management software. The private markets industry is also highly cyclical, with funding and deal volumes fluctuating based on interest rates, public market performance, and investor sentiment.

Data privacy regulations: Increasing scrutiny of AI training data and privacy regulations could limit Velvet's ability to improve its models or expand internationally. While the company promises not to train on user data, evolving regulations around AI governance and data handling could create compliance costs or operational restrictions that impact the business model. Limited partners are becoming more sensitive about data sharing, which could limit adoption among funds with strict confidentiality requirements.