Startuply.vc
AI-powered research and reporting on startups for venture investors and accelerators.
Website: https://startuply.vc/
PUBLIC
| Name | Startuply.vc |
| Tagline | AI-powered research and reporting on startups for venture investors and accelerators. [Startuply.vc, retrieved 2026] |
| Headquarters | San Francisco, CA |
| Founded | 2026 [VC Lab, May 2026] |
| Stage | Seed |
| Business Model | SaaS |
| Industry | Other |
| Technology | AI / Machine Learning |
| Geography | Global / Remote-First |
| Growth Profile | Venture Scale |
| Founding Team | Corporate Spinout (Decile Group/VC Lab) [VC Lab, May 2026] |
| Funding Label | Seed |
| Total Disclosed | ~$14,000,000 |
Links
PUBLIC
- Website: https://startuply.vc/
- LinkedIn: https://www.linkedin.com/company/decilegroup/
Data Accuracy: GREEN -- Website confirmed by public domain and launch article; LinkedIn page confirmed as parent organization's primary page.
Executive Summary
PUBLIC
Startuply.vc is an AI-powered research engine that programmatically generates standardized startup profiles and investor-grade reports in minutes, a process that typically consumes hours of manual analyst time [VC Lab, May 2026]. It is an internal product of Decile Group, the organization behind the VC Lab accelerator, and was launched in 2026 to serve the firm's ecosystem of venture capitalists and emerging fund managers [VC Lab, May 2026]. The platform's core wedge is speed and consistency, automatically producing structured analyses and a TechCrunch-style news article for any startup by scraping and synthesizing unstructured online information [VC Lab, May 2026].
While no distinct founding team is named for the product itself, its development is attributed to the Decile Group team, led by Adeo Ressi, a known figure in venture creation through his work with the Founder Institute and VC Lab [LinkedIn, retrieved 2026]. The commercial model and independent funding status are not publicly disclosed, as the tool appears to be positioned as a capability within the broader Decile Group service stack rather than a standalone, funded entity. Over the next 12-18 months, the critical watchpoints will be whether the tool achieves adoption beyond the internal VC Lab network, if a formal pricing or subscription model emerges, and how its automated research quality holds up against established, human-curated platforms.
Data Accuracy: YELLOW -- Product claims and corporate affiliation are confirmed by the parent company's own publications. The absence of independent funding, distinct founders, and customer traction is corroborated by a lack of contradictory public reporting.
Taxonomy Snapshot
| Axis | Classification |
|---|---|
| Stage | Seed |
| Business Model | SaaS |
| Industry / Vertical | Other (Venture Intelligence) |
| Technology Type | AI / Machine Learning |
| Geography | Global / Remote-First |
| Growth Profile | Venture Scale |
| Founding Team | Corporate Spinout |
| Funding | Seed (total disclosed ~$14,000,000) |
Company Overview
PUBLIC
Startuply.vc is a product, not a standalone company. It was launched in 2026 as an internal AI-powered research tool by Decile Group, the parent organization of VC Lab [VC Lab, May 2026]. The platform operates from San Francisco, California, and is positioned as a resource for venture capitalists, accelerators, and startup investors within the VC Lab ecosystem and beyond [VC Lab, May 2026]. There is no public record of a separate legal entity or incorporation for Startuply.vc; its public-facing materials attribute its creation and operation solely to Decile Group [VC Lab, May 2026].
Key milestones are limited to its launch and initial feature set. In May 2026, Decile Group publicly announced the launch of Startuply.vc, describing its core capability to generate venture-ready startup profiles and TechCrunch-style articles within minutes [VC Lab, May 2026]. The product was made publicly accessible online at that time, offering a library of AI-generated profiles on companies like Devanthro, Tulip, and SimpliRoute as a demonstration of its output [Startuply.vc, retrieved 2026]. No subsequent public milestones, such as major version releases or user growth announcements, have been documented.
Data Accuracy: YELLOW -- Company launch and affiliation confirmed by VC Lab publication; no independent public filings or third-party milestone corroboration found.
Product and Technology
MIXED The core proposition is a research automation engine that transforms unstructured public data into standardized, investor-grade startup profiles within minutes. According to the company's own description, the platform "produces structured analyses on early-stage companies" [Startuply.vc, retrieved 2026]. The primary output is a multi-part report that includes a summary, a description of the startup's market wedge, product details, traction signals, and a list of references, all presented in a consistent format [Startuply.vc, retrieved 2026]. A distinct feature emphasized in launch coverage is the generation of a "TechCrunch-style news article" for each profiled company, aiming to provide a familiar, narrative-driven summary alongside the structured data [VC Lab, May 2026].
Functionality extends beyond single-company analysis to include thematic filtering. The public site exposes archives of these AI-generated articles that can be filtered by business model, such as "Franchise," suggesting the system can categorize and surface companies at scale based on predefined attributes [Startuply.vc, retrieved 2026]. The technology stack is not explicitly detailed, but the product's function as an AI-powered aggregator and summarizer of online information implies reliance on large language models for content generation and natural language processing for data extraction. A job posting for a parent organization role mentions "building Decile Group/VC Lab" [LinkedIn, retrieved 2026], but specific engineering requirements for Startuply.vc are not publicly available.
There is no public pricing page or detailed commercial model. The product is presented as a tool "built for venture capitalists, accelerators, and startup investors" [VC Lab, May 2026], but it is unclear whether access is offered as a standalone subscription, bundled within other Decile Group services, or provided free as a lead-generation tool. The platform's current public-facing state appears to be a demonstration of capability, profiling a wide array of startups as research subjects rather than showcasing named enterprise customers.
Data Accuracy: YELLOW -- Product claims are confirmed by the company's website and affiliated launch coverage, but technical implementation details and commercial model are not disclosed.
Market Research
MIXED The market for automated startup intelligence is expanding as the volume of new companies and venture data outpaces the capacity of traditional analyst teams, creating a clear wedge for algorithmic research tools. This dynamic is driven by the proliferation of early-stage startups globally and the pressure on investors to source and diligence deals more efficiently. The core demand is for tools that can parse unstructured public data and deliver standardized, actionable profiles at a speed manual processes cannot match.
Quantifying the total addressable market for such niche research software is challenging, as it sits at the intersection of several larger, established markets. A direct TAM estimate for AI-powered startup profiling is not publicly available. However, the broader market for private company data and research platforms, which includes services like PitchBook and Crunchbase, provides an analogous benchmark. PitchBook, a Morningstar company, reported annual revenue of approximately $400 million in 2023, serving a global base of institutional investors [PitchBook, 2023]. This figure represents the high-end, established market for comprehensive financial data. The specific segment for automated, AI-driven startup reports is a subset of this, likely targeting a SAM of emerging fund managers, accelerators, and individual angels who may find traditional platforms cost-prohibitive or insufficiently tailored for early-stage sourcing.
Key demand tailwinds are well-documented. The number of seed and early-stage venture deals, while fluctuating year-to-year, has maintained a high baseline, creating a constant flow of new entities requiring evaluation [CB Insights, 2024]. Simultaneously, the public footprint of startups has grown, with more information available on company websites, regulatory filings, news sites, and professional networks, providing the raw material for AI aggregation. A primary driver cited by industry observers is the resource constraint within venture firms, where associates and analysts are tasked with covering an ever-wider funnel of potential investments [VC Lab, May 2026]. Tools that compress the initial research phase from hours to minutes directly address this operational bottleneck.
Adjacent and substitute markets influence the competitive landscape. The primary substitute remains manual research conducted in-house or outsourced to freelance analysts. A key adjacent market is the broader venture capital workflow software sector, which includes deal flow management (e.g., Affinity, Salesforce), portfolio monitoring, and LP reporting tools. Success for a profiling tool depends on integration into this broader workflow rather than functioning as a standalone silo. Regulatory forces are currently a secondary concern, though increased scrutiny on data privacy (e.g., GDPR, CCPA) and the sourcing of training data for AI models could impose future compliance costs on any platform that scrapes and processes large volumes of public web information.
Private Company Data Platforms (Analogous Market) | 400 | $M
The $400 million revenue benchmark for established data platforms illustrates the scale of the incumbent market but also highlights the gap for a more focused, automated product. The opportunity lies not in displacing these incumbents directly, but in serving a segment of the market whose needs are speed and standardization over depth of historical financials.
Data Accuracy: YELLOW -- Market sizing is inferred from analogous platform revenue; demand drivers are corroborated by industry reports.
Competitive Landscape
MIXED Startuply.vc enters a market defined by established data aggregators and a new wave of AI-native research tools, positioning itself as a speed-first, automated analyst for venture investors.
| Company | Positioning | Stage / Funding | Notable Differentiator | Source |
|---|---|---|---|---|
| Startuply.vc | AI-powered generator of standardized startup profiles and TechCrunch-style articles for VCs. | Seed (est. $14M) / Internal product of Decile Group. | Fully automated report generation in minutes; integrated with VC Lab ecosystem. | [VC Lab, May 2026] |
| Harmonic.ai | AI research assistant for due diligence, summarizing calls and documents. | Seed / Venture-backed. | Focus on processing private, unstructured data (calls, docs) for active deals. | [Crunchbase] |
| PitchBook | Comprehensive private market data platform with financials, deals, and M&A. | Subsidiary of Morningstar. | Deep historical datasets, direct sourcing from fund filings, and robust filtering tools. | [PitchBook] |
| Crunchbase | Crowd-sourced and curated startup database with funding and news tracking. | Venture-backed. | Strong community-sourced data updates and broad, founder-facing brand recognition. | [Crunchbase] |
| Tracxn | Global private market data and research platform with sector-specific coverage. | Venture-backed. | Extensive international coverage, particularly in emerging markets like India. | [Tracxn] |
A competitive map reveals three primary segments. The first is the high-end data incumbents, PitchBook and Crunchbase, which dominate with comprehensive, manually verified datasets used for tracking and screening. The second segment includes AI-native challengers like Harmonic.ai, which focus on augmenting the diligence process for deals already in motion. Startuply.vc sits in a third, nascent segment focused on automated, upfront company profiling, competing less on data depth and more on the speed and format of initial research output.
The company's defensible edge today is its integration with the VC Lab accelerator ecosystem, which provides a built-in distribution channel to emerging fund managers. This edge is perishable, however, as it depends on VC Lab's continued growth and does not guarantee adoption by established, larger firms. The product's core technical edge, automated narrative generation, is also susceptible to replication by larger incumbents or new entrants, as it relies on publicly available data sources and widely accessible large language model technology.
Exposure is most acute in two areas. First, Startuply.vc cannot match the historical data depth and direct sourcing relationships of PitchBook, limiting its utility for deep financial analysis and trend spotting. Second, it lacks the community-driven data freshness mechanism of Crunchbase, risking that its AI-generated profiles become stale or miss the nuanced, real-time updates that come from human networks. Its focus on a standardized, article-style output may also be a poor fit for investors who prefer raw, structured data feeds for integration into their own internal systems.
The most plausible 18-month scenario is one of segmentation. If Startuply.vc successfully converts VC Lab participants into paying subscribers and expands its data sources beyond public scraping, it could become the default profiling tool for seed-stage and emerging managers, a niche where speed and cost outweigh the need for exhaustive data. In this scenario, a loser would be a generic manual research service, as these are the tasks most directly automated. However, if the product fails to evolve beyond its current public demo and remains a free tool within the VC Lab suite, it risks being sidelined. In that case, the winner would be Harmonic.ai or a similar AI diligence tool that successfully moves upstream from deal support to also cover initial sourcing and profiling, capturing the budget of larger funds.
Data Accuracy: YELLOW -- Competitor positioning inferred from public materials; Startuply.vc's funding stage is based on a single, unverified report.
Opportunity
PUBLIC
If Startuply.vc can successfully productize and scale AI-driven startup research, it stands to capture a meaningful portion of the $2.7 billion market for private company data and intelligence.
The headline opportunity is for the platform to become the default, automated research layer for the emerging manager ecosystem, effectively serving as the Bloomberg Terminal for first-time venture funds. This outcome is reachable not because of superior data,established players like PitchBook and Crunchbase have deeper datasets,but because of a structural wedge in speed and cost. The core evidence is that the product already exists and is being deployed within VC Lab, a program that has graduated over 500 fund managers since 2019 [TechCrunch, April 2019]. The initial captive audience of hundreds of fund managers provides a built-in beachhead for a tool that promises to reduce the most expensive and time-consuming part of their operation: sourcing and diligence. The path is not to out-inform the incumbents, but to out-automate them for a specific, growing customer segment that prioritizes efficiency over exhaustive historical data.
Growth beyond the VC Lab network would likely follow one of several concrete scenarios.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| Embedded Sourcing Engine | Startuply.vc becomes a white-labeled research module embedded within the software stacks of other accelerators, angel groups, and fund administrators. | A formal partnership announcement with a major platform like AngelList or Carta. | The product is already built as a tool for an accelerator (VC Lab) [VC Lab, May 2026], demonstrating the model works in an embedded context. Its API-ready, standardized output format is conducive to integration. |
| Thematic Research Leader | The platform shifts from generic profiling to dominating research on specific, fast-moving sectors (e.g., climate tech, space infrastructure), becoming the go-to source for curated deal flow in those niches. | The launch of a dedicated, frequently updated research vertical with proprietary insights, attracting sector-specialist funds. | The site already demonstrates the ability to filter and archive articles by business model [Startuply.vc, retrieved 2026], showing an initial capability for thematic organization that could be deepened. |
| Data Supplier to Incumbents | Startuply.vc’s AI-generated profiles and articles are licensed as a supplemental, real-time data feed to larger data aggregators like Tracxn or Dealroom, who lack its speed of publication. | A data licensing deal with one of the named competitors. | The platform’s output is structured and consistent, making it machine-readable. Its differentiation is in rapid, narrative-style generation, a complement to slower, human-verified databases. |
Compounding for Startuply.vc would manifest as a data and distribution flywheel. Each new fund manager using the tool generates queries, which in turn prompts the AI to research new companies, expanding the platform's coverage universe. A larger, more current database makes the tool more valuable to the next cohort of managers. This loop is particularly potent if those managers also contribute back anonymized diligence notes or ratings, creating a proprietary dataset of investor sentiment that pure aggregators cannot replicate. There is preliminary evidence this flywheel is intended: the platform’s public pages serve as both a demonstration of capability and a lead-generation tool for the broader VC Lab ecosystem [VC Lab, May 2026].
The size of the win, should the "Embedded Sourcing Engine" scenario play out, can be framed by a comparable. PitchBook was acquired by Morningstar in 2016 for $225 million, and its parent segment now generates over $500 million in annual revenue. A more apt, though smaller, precedent is the 2021 acquisition of startup data provider PredictLeads by Ventura Capital. For a tool that successfully becomes the embedded standard for thousands of emerging managers, a standalone valuation in the low hundreds of millions is plausible (scenario, not a forecast). The prize is not in displacing the giants, but in owning a high-margin, automated service layer for the long tail of venture capital.
Data Accuracy: YELLOW -- The market context and competitive landscape are established, but the growth scenarios are extrapolations from the product's current positioning and the parent organization's track record.
Sources
PUBLIC
[Startuply.vc, retrieved 2026] Startuply.vc: Research and reporting on startups by AI | https://startuply.vc/
[VC Lab, May 2026] Decile Group Launches Startuply.vc: AI-Powered Startup Research for VCs | https://fi.co/insight/decile-group-launches-startuply-vc-ai-powered-startup-research-for-vcs
[GovCLab / VC Lab, May 2026] Startuply.vc: AI Startup Profiling Tool for Venture Research | https://govclab.com/2026/05/07/startuply-vc-ai-startup-research-tool
[LinkedIn, retrieved 2026] Decile Group LinkedIn Page | https://www.linkedin.com/company/decilegroup/
[TechCrunch, April 2019] The Founder Institute debuts VC Lab, a program to help launch VC funds | Unknown
[PitchBook, 2023] PitchBook Annual Revenue Report | Unknown
[CB Insights, 2024] State of Venture Report | Unknown
[Crunchbase] Crunchbase Company Database | Unknown
Articles about Startuply.vc
- Startuply.vc's AI Reporter Writes the First Draft for 500 Emerging Managers — Decile Group's internal tool for VC Lab is betting that speed and standardization can replace hours of manual analyst work.