Spring Labs Pitch Deck (2017): 10-Slide Series B Deck

Spring Labs operates at the intersection of cryptography and data networking, providing a protocol that allows institutions to verify information without actually exchanging the raw data. Their 10-slide deck is a highly professional, enterprise-focused presentation that prioritizes institutional credibility and technical architecture over typical startup growth metrics. By leveraging a 'give-to-get' model and blockchain technology, they address the $85bn+ market for financial data sharing. The deck is notable for its heavy emphasis on a high-profile advisory board and its clear articulation o…

Key takeaways

• The deck identifies a massive market inefficiency where over $85bn is tied up in 'Give-to-Get' data models that suffer from siloed information (Slide 4).
• Spring Labs positions itself as a decentralized alternative to traditional aggregators like screen scrapers, which represent a $13.4bn segment (Slide 4).
• The core value proposition is the ability to share information without sharing the underlying data, solving for competitive sensitivities and privacy concerns (Slide 6).
• The founding team has significant pedigree, having previously built companies like Avant and Amount (Slide 7).
• The company boasts an exceptionally high-profile advisory board including former chairs of the FDIC and SEC, and former presidents of Goldman Sachs and Oracle (Slide 7).
• Total equity funding raised to date at the time of this deck was $38.75M (Slide 7).
• The 'Spring Protocol' is described as patent-protected technology at the intersection of cryptography and data networks (Slide 9).
• Initial live use cases include the PACE Network for loan stacking detection and MainNet for fraud and income data sharing (Slide 10).

Frequently asked questions

What is the primary problem Spring Labs is solving?

Spring Labs addresses the 'data silo' problem in highly regulated industries. Currently, institutions are hesitant to share data due to competitive sensitivities and privacy risks. This leads to inefficient fraud detection and identity verification. Spring Labs uses a blockchain-based protocol to allow these institutions to verify data points (like income or fraud flags) against each other's records without ever actually seeing or holding the other party's raw sensitive data.

How does the Spring Protocol actually work?

As shown on Slide 9, the protocol acts as a cryptographic intermediary. When a bank needs to verify an 'Entity ID' or 'Income,' the data is passed through the Spring Protocol which matches it against other participants' data. The result is a simple 'Verified' or 'Not Verified' signal. This ensures that the underlying PII (Personally Identifiable Information) remains with the original owner while the network benefits from the collective insight.

Who are the key people behind Spring Labs?

The leadership includes Adam Jiwan (Executive Chairman), John Sun (CEO), and Anna Fridman (Co-founder). They have a track record of building fintech giants like Avant and Amount. More impressively, their advisory board includes Nigel Morris (Capital One Co-founder), Gary Cohn (former Goldman Sachs President), and Sheila Bair (former FDIC Chair), providing the regulatory and institutional 'gravity' required for this type of infrastructure.

What are the specific products mentioned in the deck?

The deck highlights four main products: the PACE Network (live, for detecting loan stacking), MainNet (live, for fraud and income sharing), Crypto Middleware (in development, for KYC/AML in digital assets), and the Spring Index (in development, for sharing threat data). These products demonstrate the protocol's versatility across traditional finance and the emerging crypto sector.

Is there a financial 'ask' or valuation in this deck?

No. This deck follows a common pattern for late-stage or high-profile raises where the specific terms of the round are omitted from the general presentation. It mentions that they have raised $38.75M to date (Slide 7), but it does not specify how much they are seeking in the current round or what the target valuation is. This information is typically reserved for one-on-one data room discussions.

Spring Labs pitch deck: the facts

Company

Spring Labs

Year

2017

Stage

Series-B

Slides

10

Spring Labs pitch deck PDF

The full Spring Labs 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 Spring Labs pitch deck was used for

This deck is a **Spring Labs** Series B pitch from around 2020–2021, used to raise an additional round after earlier seed and Series A funding. The company builds the Spring Protocol, a decentralized, privacy-preserving infrastructure for sharing financial, credit, and identity data among institutions. The deck positions Spring Labs as transforming existing financial data-sharing models and highlights initial products such as PACE Network, MainNet Crypto Middleware, and Spring Index. At the time of the deck, Spring Labs states it had raised $38.75M of total equity funding to date (Slide 7), and catalogue data now attributes a total of $68.8M raised across rounds.

Business model: Spring Labs develops the **Spring Protocol**, a blockchain-based/decentralized infrastructure for secure, privacy-preserving exchange of credit and identity-related financial data among institutions, aiming to replace centralized data aggregators like credit bureaus.

Round

Series B

Investors

TransUnion (commonly cited as Series B lead investor), GreatPoint Ventures (listed as investor across rounds, including Series B in some trackers)

Founders

Adam Jiwan, Anna Fridman, John Sun

Headquarters

Marina del Rey, California, USA.

Year: 2021 (Series B round commonly dated to April 2021; the deck itself references funding raised to date and was used in the lead-up to that event).

Raising: Series B capital to scale the Spring Protocol, expand institutional participation, and build out products like PACE Network, MainNet Crypto Middleware, and Spring Index.

Raised: Approximately $30M for the Series B round announced around April 21, 2021, with total funding across rounds reported at about $68.5M–$69M by multiple sources and catalogues associated with this deck citing $68.8M.

Lead investor: TransUnion (as identified by CB Insights, Caplight, and pitch-deck analyses as lead in the 2021 Series B round).

Industry: Fintech / blockchain-based financial data infrastructure and identity & credit information sharing.

Total funding: External trackers report total funding in the ~$66.5M–$79.3M range, with multiple sources noting a $30M Series B in April 2021 and cumulative funding near $68.5M–$69M.

Use of funds as presented: To develop and scale the Spring Protocol infrastructure, onboard financial institutions, and commercialize data-sharing products such as fraud detection, income verification, and crypto middleware solutions.

What happened after the Spring Labs deck

After the time of this deck, Spring Labs continued its fundraising journey and secured a significant Series B round (around $30M, led by TransUnion) in 2021, bringing total funding into the upper-$60M range, and has remained active as a fintech infrastructure company focused on decentralized credit and identity data sharing.

• Spring Labs went on to raise a **Series B** round of approximately **$30M** around April 21, 2021, with **TransUnion** c
• Across all rounds, external databases report that Spring Labs has raised around **$68.5M–$69M** in total, with some sour
• By 2023, Spring Labs reported having around **55 employees** based largely in Marina del Rey, continuing to develop its

What the Spring Labs deck got right

• Strong articulation of the systemic data-sharing problem in financial services. The deck clearly defines a **macro-level problem** in financial data sharing—centralized "give-to-get" models, screen scrapers, aggregators, siloed data, infosecurity risk, and limited consumer control—using familiar industry pain points and large market references (Slide 4), which makes the opportunity immediately legible to investors.
• Compelling, technically differentiated vision for decentralized data infrastructure. Spring Labs positions the **Spring Protocol** as a privacy-preserving, trustless, data-agnostic infrastructure with capabilities like graph analysis and deterministic entity resolution (Slide 8), providing a compelling technical vision that aligns with trends in decentralized finance and secure data exchange.
• Credible founding team and deep bench of regulatory and financial services advisors. The leadership slide (Slide 7) highlights founders **Adam Jiwan, Anna Fridman, and John Sun** as proven business builders and showcases a roster of high-profile advisors from major regulatory bodies and financial institutions (e.g., Sheila Bair, Raj Date, Brian Brooks), which helps de-risk execution and regulatory navigation in a complex domain.
• Meaningful prior funding showcased to build credibility for the round. The deck states that Spring Labs had already raised **$38.75M total equity funding** prior to this Series B (Slide 7), signaling meaningful prior investor validation and providing comfort that the company has resources and runway to build substantial infrastructure.
• Clear initial product use cases that illustrate monetization paths. Slides 8 and 10 translate the core protocol into **concrete initial products**—PACE Network, MainNet Crypto Middleware, and Spring Index—with labeled differentiators and statuses (live vs. in development), helping investors see near-term monetization and vertical applications rather than only abstract infrastructure.
• Mission framed around a widely recognized "enterprise data paradox". The deck leverages the narrative of solving the "fundamental paradox of enterprise data"—sharing insights without exposing sensitive information—tying Spring Labs’ mission to a widely recognized strategic challenge in financial services and enterprise data, which can resonate strongly with institutional investors.

What could have been stronger

• Limited visible traction metrics in the excerpted slides. Although Slide 7 states that Spring Labs had raised **$38.75M total equity funding** to date and names a strong advisory roster, the deck text provided does not show a clear breakdown of revenue, customer count, or concrete commercial traction metrics (live deployments, paying institutions, or growth figures), making it harder for investors to assess traction relative to ambition.
• Technical mechanism and regulatory posture not fully explained in text. The slides describing the Spring Protocol emphasize properties like "trustless," "data agnostic," "graph analysis," and "deterministic entity resolution" (Slide 8), but in the excerpted text there is no simple architectural diagram or step-by-step explanation of how data flows, which may make technical and compliance audiences work harder to understand the mechanism and its regulatory posture.
• Market sizing numbers shown without clear sourcing in visible text. The OCR excerpts for the problem slide (Slide 4) reference large market figures (e.g., "$85bn" and "$13.4bn") in relation to financial data sharing and screen scrapers, but the deck text we have does not attribute these numbers to sources or clarify exactly what they measure, which can weaken the credibility of the market sizing claim.
• Use case status and adoption not clearly tied to customers or KPIs. The initial use cases slide (Slide 10) lists several differentiators and statuses such as "Live" and "In Development" for PACE Network, MainNet Crypto Middleware, and Spring Index, but the copied text is fragmented and does not clearly tie these to specific customers, pilots, or KPIs, which may leave investors with questions about adoption risk.
• Advisor slate impressive but their active role is not clearly defined. The leadership slide showcases a large slate of high-profile advisors (e.g., Sheila Bair, Nigel Morris, Bobby Mehta, Gary Cohn, Raj Date, Brian Brooks; Slide 7), but in the text we have, their roles are described mainly by past titles rather than specific ongoing contribution, time commitment, or governance structure, which may prompt diligence questions about how actively engaged these advisors are.

How an investor would read this deck

What draws attention

• The deck tackles a **large, well-known pain point** in financial services: insecure, centralized data-sharing models and issues with credit bureaus, "give-to-get" models, and screen scrapers/aggregators (Slide 4), which investors will see as a significant, timely market opportunity.
• Spring Labs proposes a **decentralized, trustless Spring Protocol** with privacy-by-design data sharing and anonymized ownership encoded in software (Slide 8), aligning with secular trends toward data privacy, blockchain infrastructure, and regulated financial innovation.
• The leadership slide (Slide 7) positions **Adam Jiwan, Anna Fridman, and John Sun** as proven business builders and highlights a deep bench of advisors with top-tier regulatory and financial services experience (e.g., former FDIC Chair, CFPB leadership, Goldman Sachs, Fannie Mae), which mitigates pe
• The deck’s stated total equity funding to date of **$38.75M** (Slide 7) before this Series B round signals prior investor validation and capital intensity consistent with building new financial infrastructure.
• Initial product use cases (PACE Network, MainNet Crypto Middleware, Spring Index) show how the protocol might be monetized via fraud detection, income verification, and crypto middleware services, illustrating potential revenue streams and platform extensibility (Slides 8 & 10).

Risks that stand out

• Adoption risk: disrupting entrenched **credit bureaus and data aggregators** requires coordinated participation from multiple financial institutions, and the deck excerpts do not fully detail existing network effects, signed anchor partners, or contractual commitments, which may slow or cap adoption
• Regulatory and compliance risk: operating in identity, credit, and fraud data sharing inevitably engages complex regulatory frameworks (e.g., consumer data protection, fair credit reporting), and while the advisory roster is strong, the deck text does not spell out regulatory strategy or precedent,
• Execution and technical risk: building a **trustless, data-agnostic, decentralized protocol** with advanced graph analysis and deterministic entity resolution is technically complex; any missteps in design, security, or scalability could undermine institutional trust and adoption.
• Competitive risk: existing credit bureaus, anti-fraud vendors, and ID verification companies may respond aggressively, leveraging their data advantages and regulatory relationships, and the deck text we have does not clearly differentiate Spring Labs on defensible network or data moats beyond concep
• Business-model risk: the long sales cycles and integration demands of large financial institutions could delay revenue realization, and the deck excerpts do not show detailed financial projections or unit economics, making it harder to underwrite near-term returns.

Questions this deck invites

• What specific financial institutions and data providers are already **committed, signed, or live** on PACE Network, MainNet Crypto Middleware, and Spring Index, and what usage, revenue, or pilot KPIs can be shared to demonstrate traction?
• How does Spring Labs ensure **regulatory compliance** (e.g., with U.S. consumer finance and data privacy laws) when financial institutions share anonymized credit and identity data via the Spring Protocol, and what legal opinions or regulat
• What is the **economic model** for participants on the Spring Protocol (fee structure, rewards for data providers, pricing for data consumers), and how does Spring Labs capture value while maintaining decentralization?
• What are the **technical and security assurances** (audits, formal verification, penetration testing) that support claims of "trustless" infrastructure, high infosecurity, and resistance to new centralization points or honeypots?
• How will Spring Labs **scale adoption** against entrenched credit bureaus and data aggregators—what is the go-to-market strategy, which segments are targeted first, and how do network effects and switching costs play out?

What founders can take from the Spring Labs deck

• A clear articulation of a **large, systemic pain point**—in this case, insecure and centralized financial data-sharing models—can anchor a deck and make a complex technical solution feel urgent and necessary when supported by concrete examples like "give-to-get" models and screen scrapers (Slide 4).
• Highlighting a **platform plus products** structure, as Spring Labs does with the Spring Protocol and specific offerings like PACE Network, MainNet Crypto Middleware, and Spring Index (Slides 8 & 10), helps investors understand both the infrastructure vision and near-term monetization paths.
• Explicitly stating **total equity funding raised to date** (e.g., $38.75M on Slide 7) builds credibility and context for the current raise, situating the round within the company’s broader capital and development journey.
• Showcasing a **deep bench of domain-specific advisors** with regulatory and financial services backgrounds (former FDIC Chair, CFPB leadership, major bank and credit bureau executives; Slide 7) can materially strengthen confidence when a startup operates in highly regulated, systemically important m
• Using initial **live and in-development use cases** (PACE Network, crypto middleware, Spring Index) to illustrate applications of a core protocol allows investors to visualize concrete value propositions across multiple verticals rather than only hearing a high-level infrastructure pitch (Slides 8 &
• Even in highly technical domains, decks benefit from pairing conceptual claims like "trustless" and "data agnostic" with simple explanations and diagrams of flow and compliance; the fragmented text here underscores how missing that can generate follow-up questions.

Spring Labs pitch deck: common questions

What does Spring Labs do?

Spring Labs is a fintech company that builds the **Spring Protocol**, a decentralized, blockchain-based infrastructure for secure, anonymized exchange of credit and identity-related financial data among institutions, aiming to provide more secure, accurate, and privacy-preserving alternatives to traditional credit bureaus and data aggregators.

How much funding has Spring Labs raised and what round is this deck for?

Public funding trackers and analyses report that Spring Labs has raised around **$68.5M–$69M** in total across several rounds, including a **$30M Series B** announced around April 2021, while Owler and Caplight list total funding somewhat higher, in the **$72.75M–$79.3M** range. The teardown catalogue listing associated with this deck cites **$68.8M** total.

What funding status does the Spring Labs pitch deck itself claim at the time it was used?

The deck focuses on Spring Labs’ **Series B** raise and notes that at the time of the deck the company had already raised **$38.75M in total equity funding** (Slide 7). Later external sources show that the Series B itself was for about **$30M**, led by TransUnion, bringing total funding into the upper-$60M range.

Who founded Spring Labs and what is their background?

Spring Labs’ co-founders are **Adam Jiwan** (Executive Chairman & CEO), **Anna Fridman**, and **John Sun**, all described as "proven business builders" with prior experience at firms like Avant and in digital finance and asset management (Slide 7). External sources confirm the same founding team and roles.

What products and use cases does the Spring Labs pitch deck highlight?

The deck highlights three initial product use cases built on the Spring Protocol: **PACE Network** (fraud and income data sharing, including loan stacking detection), **MainNet Crypto Middleware** (bridging off-chain financial services data to the digital asset world), and **Spring Index** (alternative data-driven credit and identity insights; Slide 8 & 10). These are positioned as modular data-sharing tools leveraging trustless, anonymized graph analysis and entity resolution.

Sources

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

• Spring Labs Pitch Deck to Raise $30m Series-B Round
• Adam Jiwan and Spring Labs - How Passion Can Power the Future...
• Spring Labs — AI-native conversational intelligence | DailyDropout.fyi
• Podcast 238: Adam Jiwan of Spring Labs | Fintech Nexus
• Spring Labs Jobs and Careers - Welcome to the Jungle | Login
• Spring Labs | Valuation, Funding Rounds & Stock Price
• Spring Labs - Catalyst Investors' Club

Spring Labs pitch deck slides

What each slide of the Spring Labs pitch deck says

Slide 1

Spring . N Spring Labs § ¢ The Future of Secure Data Exchange” _ _ . May, 2021

Slide 3

Bi ” ps \ Our Mission pe Transforming the Exchange of Sensitive Information | <> ml TR) 3 (®7 LJ Le

Slide 4

Spring Labs . Financial Data Sharing Models are Ripe for Transformation The way data flows through financial services today needs to be updated to best service and pratect consumers, Today, the . majority of this data is gathered in two main ways: Give-to-Get Models Screen Scrapers & "Aggregators" 8 = s Limited data shared = Highinfosecurity risk Exsepie s Limited consumer control of view 8 b » Consumers don't know what data anwhat is shared abeut them >$85bn ST By TR $13 4bn » Siloed data + Noenerprise data monetization . Centralization, leading to high - Siloed data infosecurity risk

Slide 5

Spring Labs Ps EN And Frankly, Lots of Valuable Data Are Not Even Shared Pa These models exist for good reasons: <> @ Competitive Sensitivities pig Institutions are hesitant to participate and yield ‘competitive advantage 2 Privacy & Data Control Concerns @ a Data sharing is not secure and data loss is rampant. the impact of which can be huge Paid Lack of Incentives to Contribute 4 2 Why share data if there's nothing in it for you? - In these madels, the risks outweigh the benefits involved in the exchange of proprietary industry data. This leads to silos &5 of valuable data owned by enterprises - but that ought to be = securely shared!

Slide 6

CREEL eo Our Vision: A Marketplace for Better Data Sharing Pa We have built the technology that enables institutions to finally == < access valuable, siloed data - a protocol allowing a direct and oO = secure information exchange. A iad Rog Realincentives toshare, enabling access to #4 & caico previously unavailable data UCLA TN Statefarm » ed - Bl Gy Fn 0 - £3 No active middie-man, a direct exchange between ma! participants N > i = Greater security and data ownership controls: share Spectrum. 4 . m information without sharing underlying data Broad participation (financial institutions, insurance, - [am] € ih payroll social media, etc.) brn K = = R=]

Slide 7

Spring Labs Proven Business Builders, Backed by Industry Leaders Leadership & Funding Adam Jiwan Jaho Sun Anaa Fridenan Cor-fomnder Co-tounder Co-founder Exrcuthve Chatrman CeQ With experience buliding: 2% AMOUNT n DIGITAL AVANT FUTURE <+ FINANCE $38.75M Total equity funding rained to-date Active Advisors {incl. former roles) 4 9 & 3 Sheila Bair Nigel Morris Bobby Mehta Gary Cohn Fannle Mae Capital One TramsUrion Goldman Sachs Chair; Co-founderand CEOQ and Board President FDEC Chair President Menber Ray Lane Manaolo Sanchez Raj Date Brian Brooks Ovacle President, BEVA Compuns CFPa OOC Comptralie HP Chalrman Chalrman &CEQ Deputy Director

Slide 8

Spring Labs < Spring Protocol and Use Cases TECHNOLOGY Spring Protocol < Innavative technology layer for modular data security and sharing properties. Partrers feel safer sharing when ownership and anomymity can be guaranteed in code. f R 8 B Trustless & Data Agnostic Graph Analysis Flexible Entity Deterministic Enabled Resolution USE CASES Data Sharing Products Valuable data sharing toods built on top of the Spring Protocol. products keverage the technology's unique properties to deliver immediate value 0 @ & @ PACE Network MainNet Crypto Spring Index Middleware

Slide 10

Spring Labs ' Initial Use Cases With real-time access to previously unavailable infarmation using the Spring Protocal, we built unique products: - PACE Network MainNet Crypto Middleware Spring Index Detecting loan stacking in the PACE Fraud and income data sharing. Verify Identity, AMLAKYC, and Tokenized black/white Ests sharing industry incame stability data Syrtbetic-ldentity Detection between competitive parties Live Live In Development Differentiation Défferentiation Differentiotion Differentiation Loan idormation shared with peers. - Fraud and Income & Bridging off-chain and on<chain i Rich siternative v ooyl 1 L MAaTy M shar on borrowers. financial se s o the digital asset workd - - A…

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

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