Apteo Pitch Deck (2019): 12-Slide Seed Deck

Apteo's 12-slide pitch deck is a textbook example of a Seed-stage narrative that prioritizes problem-solution fit over raw traction metrics. Raised in 2019, the $1.1M round was built on the premise that financial firms were drowning in 'alternative data'—information outside of traditional SEC filings and price feeds—but lacked the data science resources to process it. The deck uses a clean, three-column layout across most slides to contrast the 'Before' (weeks of manual work) with the 'Now' (minutes of automated analysis). While the deck lacks specific revenue figures or a detailed cap table,…

Key takeaways

• The deck identifies a specific market shift: a 5x increase in employees hired to work with alternative data over five years (Slide 2).
• The core problem is defined as data being 'hard to acquire, clean, analyze, and share' due to lack of centralization (Slide 3).
• Apteo positions its product as a dual offering: a 'Data Library' with 2M+ public datasets and a 'Do-It-Yourself A.I.' engine (Slide 4).
• The value proposition is framed as a time reduction from '2 Weeks + Outsourced Contract' to 'Answers In Minutes' (Slide 5).
• A feature-by-feature comparison table pits Apteo against six competitors, including Quandl and DataRobot (Slide 6).
• The market size is segmented into a $440 Billion total US market and a $28 Billion addressable market for market data (Slide 7).
• The business model includes a '6-figure contract' integration fee for large institutions plus monthly per-user fees (Slide 9).
• The team slide highlights significant domain expertise, including a CEO from Twitter and a COO from Point72 (Slide 10).

Frequently asked questions

How much did Apteo raise with this deck?

According to the catalogue facts, Apteo raised $1.1M in 2019. The deck itself does not specify the dollar amount being sought, only that the funds would be used for product development, technical infrastructure, and hiring initial sales personnel to launch paid pilots.

What is the primary product Apteo is selling?

Apteo describes its product as a Data Science Platform for financial firms. It consists of a live public catalog with over 2 million datasets and a 'Do-It-Yourself A.I.' tool that allows users to test hypotheses and forecast KPIs automatically without needing a dedicated data science team.

Who are the competitors mentioned in the deck?

Slide 6 lists six competitors: Quandl, Collibra, Crux, DataRobot, and 7Park Data. Apteo differentiates itself by being the only one (according to their chart) that aggregates public data, catalogs internal data, discovers relevant data, specializes in timeseries, and offers A.I.-based forecasting simultaneously.

What is the business model described in the deck?

The business model is a three-tiered approach. Large institutions pay a 6-figure annual integration fee for security and compliance. All customers pay a monthly per-user recurring fee for platform access. Finally, the company generates additional revenue through the sale of premium data on the platform.

What stage of development was the product in during this raise?

Slide 4 notes that the 'Core A.I. technology' was built, but the 'front end' was still in progress at the time of the deck. Slide 8 further clarifies that they were in the process of finalizing paid pilots with initial design partners among banks and asset managers.

Apteo pitch deck: the facts

Company

Apteo

Year

2019

Stage

Seed

Slides

12

Sector

Fintech / Data Science

Deck type

Investor Pitch Deck

Outcome

Raised $1.1M

Headquarters

New York, USA

Apteo pitch deck PDF

The full Apteo deck is embedded on this page and can be read slide by slide in the browser — no download or account required. Each slide is covered in the breakdown above.

What the Apteo pitch deck was used for

This is Apteo’s 2019 **seed-stage** pitch deck used to raise a **$1M seed round** focused on building an alternative data infrastructure and analytics platform for finance professionals. The deck presents a 12‑slide narrative around the rapid growth of alternative data in finance, the difficulty of sourcing and using it, and Apteo’s proposed data science platform with a 2M+ dataset catalog and “Do‑It‑Yourself A.I.” for hypothesis testing and KPI forecasting, targeted at banks, asset managers, and other investors. The funds were aimed at accelerating product development of the OneData platform, technical infrastructure, and initial sales to launch paid pilots with design partners.

Business model: Apteo provides a data management and analytics platform for financial firms that sources, centralizes, and analyzes traditional and alternative data, combining a large public data catalog with tools to integrate firms’ proprietary data and apply AI-driven analysis.

Round

Seed.

Year

2019.

Lead investor

Ripple Ventures.

Investors

Ripple Ventures, Entrepreneurs Roundtable Accelerator, KPB Capital, CFV Ventures, Gavin Ezekowitz, Various angels from the finance ecosystem (as summarized in some reports)

Founders

Shanif Dhanani, Manan Shah

Headquarters

New York, NY, United States.

Industry

Fintech / financial data analytics / alternative data infrastructure.

Raised: Public announcements consistently describe this seed round as $1.0M, while several data providers list Apteo’s total funding around $1.0–$1.1M including other seed financings.

Total funding: Multiple sources report total funding of approximately $1.0–$1.1M across seed rounds.

Use of funds as presented: The seed funding was intended to accelerate development and expansion of Apteo’s OneData platform, including its cloud data management and analytics capabilities, growth of the 2M+ traditional and alternative dataset catalog, technical infrastructure, and initial sales and pilots with financial firms.

What happened after the Apteo deck

Following the 2019 seed deck, Apteo secured a $1M seed round led by Ripple Ventures, on top of a prior $100k seed investment from Entrepreneurs Roundtable Accelerators, and continued to develop and expand its OneData platform and alternative data catalog for financial institutions.

• January 7, 2019: Apteo raised a $100k seed round from Entrepreneurs Roundtable Accelerators, providing early capital and
• October–November 2019: Apteo closed a $1M seed round led by Ripple Ventures, with participation from Entrepreneurs Round
• Subsequent data providers (e.g., CB Insights and Craft) report Apteo’s total funding as approximately $1.0–$1.1M across

What the Apteo deck got right

• Clear quantification of the alternative data trend. The deck supports the opportunity narrative with concrete statistics on the growth of alternative data in finance (spend, number of vendors, and hiring of specialized staff), reinforcing that the problem is large and recent rather than hypothetical.
• End‑to‑end product vision combining data catalog and AI analytics. Apteo’s product is positioned as an integrated data science platform that both catalogs over 2M public datasets and centralizes firms’ proprietary data, with “Do‑It‑Yourself A.I.” for forecasting KPIs—providing a full workflow from sourcing to analysis rather than just data access.
• Strong alignment between articulated pains and solution features. The problem slide breaks the pain into sourcing, centralization, and lack of data science capabilities, which maps directly onto the features and benefits slides, helping investors see a tight linkage between customer pain and the proposed solution.[Slides 3–5]
• Clear competitive positioning against recognizable incumbents. The competitive landscape slide explicitly compares Apteo’s capabilities to well‑known players (Quandl, Collibra, Crux, DataRobot, 7Park) and highlights its differentiation via the combined ontology‑based catalog of 2M+ datasets and AI‑based forecasting geared to finance professionals.
• Credible enterprise go‑to‑market and monetization plan. Go‑to‑market and business model slides present a plausible enterprise strategy—design partners, vertical expansion, cross‑vertical expansion—with revenue derived from integration fees, per‑user subscriptions, and premium data sales, which is familiar and legible to B2B SaaS and data investors.[Slides 8–9]
• Highly relevant mix of technical and finance domain expertise. The team slide showcases founders with both advanced data science/machine learning experience at firms like Twitter/TapCommerce and deep buy‑side and banking experience at Point72, Coatue, Centerbridge, and Morgan Stanley, which strengthens credibility in selling complex data infrastructure to financial institutions.

What could have been stronger

• Limited evidence of signed pilots or revenue traction. The deck does not present concrete customer traction metrics (e.g., number of pilots signed, active users, or contracted ARR), instead only stating that Apteo is “currently in talks to close design partners” among banks and asset managers, which may leave investors uncertain about market validation at the time.
• Competitive moat not clearly quantified or defended. While the competitive landscape slide lists features across Apteo and competitors such as Quandl, Collibra, Crux, DataRobot, and 7Park, it remains mostly feature‑checklist based and does not quantify switching costs, long‑term differentiation, or defensibility beyond the 2M+ public datasets and ontology; this can make it harder to judge durable competitive advantage.
• Market sizing high‑level rather than bottom‑up. The market slide cites large figures like $440B in US financial services IT spending and $28B in financial market data and news revenues, but does not translate them into a clearly reasoned serviceable obtainable market (SOM) with bottom‑up assumptions, leaving the practical revenue potential somewhat abstract.
• Pricing and unit economics not specified. The business model slide outlines integration fees, per‑user monthly subscriptions, and premium data fees, but does not provide example price points, average contract sizes, or unit economics (e.g., gross margins on premium data), which limits an investor’s ability to assess revenue scalability and margin structure.
• Technical depth and evidence of AI performance are thin. The deck leans heavily on the 2M+ public datasets and core AI technology being built, but offers little technical detail on the AI approach, data quality controls, or performance benchmarks (accuracy, latency, or compliance posture), which may be important for institutional finance buyers and technical investors.

How an investor would read this deck

What draws attention

• Evidence of real usage and paying customers for the OneData platform or the described data science product—number of pilots, conversion to contracts, and early ARR would be closely scrutinized, given the deck’s emphasis on “talks” rather than signed deals.
• Integration with banks’ and asset managers’ existing data stacks and workflows, including compliance, security, and single sign‑on, since the business model relies on large institutions paying significant integration fees and recurring subscriptions.
• Differentiation versus established data providers (e.g., Quandl) and analytics platforms (e.g., DataRobot), especially around the ontology, alternative data coverage, and AI forecasting capabilities.
• Team’s ability to sell into conservative financial institutions, given the go‑to‑market strategy combining high‑touch enterprise sales with automated outreach to mid‑sized firms.
• Scalability of maintaining and expanding the 2M+ dataset catalog—cost of data acquisition, cleaning, and refresh, and implications for gross margins.

Risks that stand out

• Enterprise sales into regulated financial institutions often involve long cycles and stringent compliance requirements; without clear traction or case studies, Apteo may struggle to convert “talks” into significant revenue, delaying scale.
• Dependence on maintaining a large catalog of public and alternative datasets could drive substantial ongoing infrastructure and data‑ops costs, potentially compressing margins if not offset by high‑value contracts.
• The competitive field in alternative data and analytics includes well‑funded incumbents and specialized providers; Apteo’s differentiation via ontology and AI forecasting may be challenged if competitors add similar features or if buyers prefer established brands.
• If most customers lack in‑house data science expertise, the platform’s usability and support model become critical; inadequate onboarding or training could limit adoption despite strong technical capabilities.
• Regulatory and data‑privacy considerations around alternative data (e.g., use of credit‑card transactions, geolocation, or other sensitive signals) could introduce compliance risk that necessitates additional legal and technical investment.

Questions this deck invites

• What concrete pilot agreements or paid contracts were signed with banks, asset managers, or other financial institutions following the outreach described in the deck, and what ARR or revenue resulted from these deals?
• How does Apteo technically ingest, clean, and maintain its 2M+ public datasets and customers’ proprietary data, and what SLAs or compliance frameworks (e.g., data lineage, access controls) are offered to institutional clients?
• How do Apteo’s features and pricing compare in detail with competitors such as Quandl, Collibra, Crux, DataRobot, and 7Park, and what is the observed win‑rate or replacement pattern in actual sales cycles?
• What are the economics of the integration and subscription model: typical contract size by customer segment, sales cycle length, gross margins, and cost of onboarding new enterprise customers?
• How has Apteo’s platform and positioning evolved since the 2019 seed round—for example, product focus (OneData vs other offerings), target segments, and revenue growth trajectory?

What founders can take from the Apteo deck

• Anchoring the deck in a clearly quantifiable macro trend—in this case, the rapid growth of alternative data spending, number of vendors, and hiring—creates a compelling context for why the product should exist and why now.
• Explicitly articulating pain points across the data lifecycle (sourcing, cleaning, analysis, sharing) helps frame the product as an end‑to‑end solution rather than a point tool, making the value proposition easier to understand.
• Combining a tangible asset (a catalog of 2M+ public datasets) with a higher‑level capability (“Do‑It‑Yourself A.I.” for forecasting KPIs) can make an early‑stage platform feel more concrete and differentiated.
• Using a feature‑comparison table against recognizable competitors (Quandl, Collibra, Crux, DataRobot, 7Park) helps investors quickly situate the company in an existing landscape, even if further detail is needed on moat and outcomes.
• Detailing a phased go‑to‑market—design partners, vertical expansion within finance, then cross‑vertical expansion—shows a thoughtful commercialization path rather than a vague plan to “sell to everyone.”
• Highlighting a team with both deep technical (big data, machine learning) and finance experience reinforces credibility for a product aimed at sophisticated financial institutions and data‑driven investors.

Apteo pitch deck: common questions

What does Apteo’s product do as described in the 2019 seed deck?

Apteo built a data science and analytics platform for financial firms that centralizes traditional and alternative data, offering a catalog of over 2 million public datasets and tools for firms to integrate their proprietary data and run AI‑driven analysis without large in‑house data science teams.

How much did Apteo raise with this 2019 seed deck and who invested?

Public reporting indicates Apteo raised a **$1M seed round** in 2019, led by Ripple Ventures, with participation from Entrepreneurs Roundtable Accelerator (ERA), KPB Capital, CFV Ventures, and angel investor Gavin Ezekowitz, following an earlier $100k seed investment from ERA in January 2019.

Who was the lead investor in Apteo’s seed round and which other investors participated?

Ripple Ventures led the $1M seed round, with Entrepreneurs Roundtable Accelerator as an existing investor adding more capital, alongside KPB Capital, CFV Ventures, and Gavin Ezekowitz; some sources also mention ERA and various angels broadly as follow‑on investors.

Which customer segments is Apteo targeting in this deck?

The deck targets banks, asset managers, equity research analysts, wealth managers, private equity, venture capital firms, and other financial institutions that increasingly rely on alternative data but lack centralized infrastructure and data science resources. The initial go‑to‑market focuses on paid pilots with banks and asset managers, then expansion across financial verticals.

Who are the key team members featured in Apteo’s pitch deck and what is their background?

The team highlighted in the deck consists of CEO Shanif Dhanani (data scientist and machine learning engineer with experience at Twitter/TapCommerce and finance firms), COO Manan Shah (portfolio manager and investment professional at Point72, Coatue, Centerbridge, Morgan Stanley), and CTO Mo Syed (lead engineer and head of analytics at Twitter), plus additional team members including a lead designer, a regulatory lawyer advisor, and a software engineer.

Sources

Funding and outcome facts on this page were researched on 2026-08-21 from the pages below.

• Ripple Ventures Leads $1M Funding Round For NYC Fintech Company Apteo
• Apteo Raises $1M to Enable Investors to Use Data Seamlessly
• Alt Data Startup Apteo Used This Pitch Deck to Raise $1...
• Apteo Company Profile - Office Locations, Competitors, Revenue, Financials, Employees, Key People, Subsidiaries | Craft.co
• Apteo helps investors gain information advantage through data...
• Apteo
• Top Apteo Competitors and Alternatives | Craft.co
• Apteo Preps Alt Data Platform Expansion - WatersTechnology.com

Apteo pitch deck slides

What each slide of the Apteo pitch deck says

Slide 1

Helping finance professionals make smarter decisions with data and analytics founders@apteo.co | apteo.co

Slide 2

Opportunity The Recent Growth In Alternative Data In Finance Has Enabled Better Decision-Making 2X 4X Spend on Firms Selling Alternative Data' Alternative Data' In the past three years, In the past four years, the financial firms have doubled number of firms selling what they spend on alternative data has alternative data quadrupled "https://alternativedata org/stats 5X Additional Employees Hired' In the past five years, firms have quintupled the number of employees they've hired to work with alternative data

Slide 3

The Problem Alternative Data Is Hard to Acquire, Hard to Clean, Hard to Analyze, and Hard to Share e = Sourcing Data Is Difficult Data is not clean, hard to access, and use effectively Firm Data Is Not Centralized Data sits in Excel files on employee laptops versus available to others Most People Aren''t Data Scientists Many firms don't have the resources needed to run predictive analytics

Slide 4

Our Product Our Data Science Platform Helps Financial Firms Source, Centralize, and Analyze New Datasets Data Library Do-It-Yourself A.l. Cleaned, centralized public, paid, & proprietary data Test hypotheses and forecast KPIs automatically e T e @i Live public catalog available with 2M+ public datasets Core ALl technology built, front end in progress

Slide 5

Benefits to Customers We Can Help Reduce The Time And Cost To Find Data, Test Hypotheses, And Get Answers iy - -8 -8 -4 - Determine Compliance Outsource to Scrape Predictive Deliver + Outsourced Contract Quastion fithini O ot Modeling Bt Now —»%—»fl Get an Data and paw Answers In Minutes Log In To Platform Set Up Analysis or Get Resuits Download Data

Slide 6

Competitive Landscape We Provide A Targeted Data and Analytics Solution For Finance Professionals g Features Apteo Quandl collibra CRUX pataRobot v v v v v "7PARK Aggregate public data Catalog internal data Discover relevant data J Specialize in timeseries LLLKLKL NS S A.l.-based forecasting v Our difference: we catalog 2M+ public datasets alongside firms' internal data in a structured ontology and surface key analytics to users

Slide 7

Market Opportunity The Financial Data and Analysis Industry Is Large And Growing $440 Billion (US) Total Market Banks, asset managers, equity research Worldwide financial analysts, wealth managers, private services IT spending (IDC, equity, and VCs are all using data o 2018) and analytics to make decisions, $28 Billion and are increasingly turning to alternative data ags a sourceg Addressable Market for valuable insights. Worldwide revenues from financial market data and news (Burton-Taylor, 2017)

Slide 8

Go-to-market We Are Finalizing Paid Pilots With Initial Design Partners And Will Then Move Into New Verticals Close Initial Design Partners i £ 2 % * Currently in talks to close design partners among banks and asset managers 2] Expand Within Vertical au High-touch enterprise sales for large banks and funds, automated outreach for mid-sized organizations Expand Across Verticals Expand into private equity, venture capital, & other financial verticals

Slide 9

Business Model Large Customers Pay an Integration Fee, All Customers Pay For Monthly Subscriptions il Q ot Integration Fee Recurring Fee Premium Data Annual Fee Monthly Fee Premium Data 6-figure contract for larger Per-user monthly fee for Additional sales from institutions that require continued access to core data premium data sold on the security, compliance, & single management features platform sign-on integrations

Slide 10

Our Team We Have Diverse Experience Across Big Data, Machine Learning, and Finance P . i Shanif Dhanani Manan Shah Mo Syed CEO COO CTO Data Scientist, Twitter « Portfolio Manager, Point72 +Machine Learning Engineer, Lead Engineer & Head of « Analyst, Coatue Twitter Analytics, TapCommerce + Principal & Associate, (acquired by Twitter) Centerbridge Associate, Booz Allen « Analyst, Morgan Stanley Investment Bank Additional team members: lead designer, leading NY fintech regulatory lawyer (advisor), software engineer

Slide text above is read directly from the Apteo deck PDF embedded on this page.

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