The Anatomy of a Trusted Data Market

trusted data markets cheqd blog

A deep dive into the fundamentals of what a Trusted Data Market is and how cheqd’s infrastructure enables them.

This is part of a series, read the second blog ‘The role of cheqd in Trusted Data markets’ here.

In short, a “Trusted Data Market” is a vision for both consumers and businesses where the paradigm of data ownership is inverted to the user, trust is verifiable, and can be transacted upon within a privacy-preserving data market.¹


If you look up the word “trust” in the dictionary, the first definition is typically one about general trust between two parties, but almost always, the second definition is a financial one. Take, for example, the language around finance; the United States is backed “by the full faith and credit” of the U.S. government. “Credit,” from the Latin credere, literally means “to trust.” Trust is known to be fundamental to better economic outcomes² and we all trust in others and reciprocate that trust with trustworthy actions as part of our everyday lives. Moreover, economic historians debate the relative importance of models of trade growth and the importance of capital infusion to fuel innovation. But where there is broad agreement is in the central notion that goes back to civilisation’s origin: trust undergirds cooperative behaviour. But what is trust’s central role in markets? And how could verifiable data change trust’s role in markets to form a new data paradigm?

Defining Trust

Philosophers have an important definition of what ‘trust” is. Most agree the dominant paradigm of trust is “interpersonal” and this type of trust is a type of reliance, although it is not mere reliance.³ Rather, trust involves reliance “plus some extra factor.”⁴ Although this definition is lacking when applied to trust in current data markets, we will explore later its applicability within a trusted data market.

This extra factor typically concerns why the trustor (i.e., the one trusting) ought to rely on the trustee to be willing to do what they are trusted to do. This is further conditioned by another layer of trust: whether the trustor is optimistic that the trustee will have a “good” motive for acting. This is especially important should the trusted interaction involve a transactional relationship (the exchange of assets or risk), to optimise incentive alignment within a market context.

This is demonstrated in the logic of why one should trust someone.

T(x,y) = x trusts y

R(x,y) = x relies on y

E(x,y) = x ought to believe there is an ‘extra factor’ for trusting y

W(x,y) = y is willing to do what they are trusted to do

M(x,y) = y acts with good motive

For all x and y,

T(x,y) → [R(x,y) ∧ E(x,y) ∧ W(y) ∧ (T(x,y) → M(y))]

This translates to: for all x and y, x trusts y iff (if and only if) x relies on y and believes there is an extra factor for trusting y and y is willing to do what they are trusted to do (not dependent on x trusting y) and if x trusts y, then x believes that y has a good motive for acting. This logic breaks down if y would not be willing to do what they were originally trusted to do and/or y acts with a “bad” motive.

Critically, to establish these relations as “trusted,” time and repetition are necessary. The more “trusted interactions” are performed successfully, the more likely one can safely assume trust as I can likely assign a ‘good’ motive for acting, and admit a history of actions than consistently show y is willing to do what they are trusted to do.

In this blog, we’ll explore how a market dynamic could shape if certain trust conditions in our introduction became verifiable from inception, what this subsequently could mean for time-to-reliance to form trust, and the consequences in terms of transparency, accountability, and reliability this could entail. We will then explore how this could form a “Trusted Data Market.”

The Role of Trust in Markets

Trust is, and always has been, an essential component of markets. Therefore, we must ascertain why trust formed in markets, and why sociologists and anthropologists cite the advent of market economies as representing a significant break in the organisation of human societies.

From an economist’s perspective, markets represent a transition from a system where product distribution was based on personal relationships to one where distribution is governed by transparent rules. While these rules are necessary to ensure that markets operate effectively, trust is also crucial to ensure that transactions are conducted in good faith, and to promote compliance with market regulations. The rules themselves now represent a ‘shared truth’ which replaced the previous social contracts and interpersonal relationships that pre-dated market economies.⁵

Primarily, trust reduces the risk of opportunistic behaviour within rule-based frameworks. When buyers and sellers trust each other to act in good faith, they are more likely to engage in mutually beneficial transactions. As described above, these ‘trusted interactions’ form a basis for trust over time. Therefore, trust is an important property for promoting compliance with market regulations to ensure that the market functions efficiently and that all participants can compete fairly. Concerning data, trust is also critical for promoting transparency and accountability in markets with rules. When buyers and sellers trust each other, to be honest, and transparent in their transactions, they are more likely to provide accurate information about the goods and services they offer. This promotes transparency in the market and helps to ensure that all parties have access to the information they need to make informed decisions. Moreover, trust can also foster self-regulation and promote agency, defined by Hickman et al. as:

“… intentionality, responsible for defining strategies and plans; anticipation, related to temporality, in which the future tense represents a motivational guide, driving force of prospective acts to reach goals; self-regulation, which are personal patterns of behaviours that monitor and regulate their actions; self-reflection, responsible for self-inquiry into the value and meaning of their actions.”⁶

And with agency undergirded by trust, businesses and individuals who are trusted by their peers are more likely to adhere to ethical standards and social norms.

For these advancements to occur, verifiable data could reduce the need for trust (its current role in markets), as defined by time + trusted interactions, by ensuring data integrity and authenticity, promoting fair competition, reducing fraud risk, improving transparency, and simplifying regulatory enforcement. Forming a powerful tool for modern rule-based markets’ efficiency and effectiveness and ability to innovate with new use cases, commercial models and trust dynamics.

Data in Markets

Data is well-referenced as the “new oil” of the digital economy. This is perhaps more evident than ever before as its driving innovations like Chat GPT, creating new knowledge and insights from various machine learning and reasoning techniques, and increasing efficiency in many fields.⁷

However, certain types of data markets are not transparent where the user is the product. Typically, consumers are not cognisant of what happens to their data, and without self-sovereign identity, cannot control data use.⁸ Researchers and academics have persuasively argued a legitimate trade of data in a “shadow market”⁹ has evolved, however, this “shadow market” (intermediary platforms that trade on subject’s data) is not lucrative for the subjects of data, but is for data controllers. This has presented a conflict and misalignment of incentives between consumers’ data rights, assumed privileges and increasing desire for privacy and the current market demands for data.

A vision for both consumers and businesses, where the paradigm of data ownership is inverted to the user, and trust is verifiable within a privacy-preserving data market¹⁰ is what we at cheqd refer to as a “Trusted Data Market” powered by cheqd’s infrastructure.

cheqd’s infrastructure provides privacy protection and verifiable informational self-determination for consumers. Inverting the current Data Market paradigm whilst critically providing economic advantage and innovation for businesses who currently control and participate in the preexistent data economy. This type of data sharing, when willingly shared by “Issuers’ (companies who own, control and monetise consumer data) can provide verifiable information, through cheqd’s verifiable data registry and infrastructure, shared by ‘Holders’ (consumers, customers, companies or even objects within a supply chain) can afford low market entry barriers, transparency for the parties involved as well as verifiable data. And critically, new economic data models where trust has a new property within the data paradigm: verifiability, forming the foundation for a new, innovative type of Data Market.

The Trust Game

Before bringing this all together, it’s important to note what Verifiable Data can solve within economic theory for Data Market models. Economic theory commonly models this through a thought experiment known as the “Trust Game.” The “Trust Game” involves a principal-agent scenario where the following economic features of trust — risk, shared values, sacrifice, and reputation — are observed. At its core, the game involves a sequential exchange in which there is no contract to enforce agreements. In the game, most variants of it endow subjects with $X. Subjects are then paired anonymously and assigned to either the role of “sender” or “receiver.” Stage 1: The sender (trustor) may either pass nothing or any portion X of the total Y to the receiver (Trustee). If the sender keeps X, the experiment conditions triple the total amount so that 3x is passed onto the receiver. Stage 2: The receiver (trustee) may either pass nothing or pass any portion Y of the money received back to the sender. The amount passed by the sender is said to “capture trust,” as it infers an intent that the other party “… will reciprocate a risky move (at a cost to themselves),’’ and the amount returned to the trustor by the trustee, therefore, capture trustworthiness.¹¹

Importantly, the “Trust Game” models how trust occurs in the context of a cooperative relationship with repeated interactions over time. In iterations of market dynamics where trust must be assumed, gained, reciprocated, and then maintained, we can only gage trust by how parties act within a transactional instance, and how the accumulation of these trusted interactions forms “trust” and creates dependencies on how those actions will affect cooperation in the future. Simultaneously, how market participants behave today is also determined by cooperative behaviour in the past.

In the Trust Game model, repeated interactions over time are the critical factor in determining whether those interactions can be fairly assumed as worthy of a trusted reputation. Players may initially be cautious and invest a small amount, and then gradually increase their investment as they learn more about the other player’s trustworthiness. This may not always lead to high levels of trust and cooperation between the players.

However, if verifiable trust is established from inception, players would have access to data about each other’s trustworthiness before the game begins. For example, they may have access to ratings or reviews from previous games, or other forms of verifiable data that indicate the other player’s trustworthiness, like data which is issued from a trusted issuer that provides assurance the information the player presents has an “extra factor” other plays can rely upon. This can create a positive expectation and lead to more initial trust between the players, reducing the need for a learning process to establish trust. Time-to-reliance within the market dynamic shifts, regardless of the use case as the verifying participant can assume the data is at base verifiable.

For example, in the Trust Game’s model, if the players have access to verifiable data indicating that the other player has a history of trustworthy behaviour in previous games, they may be more likely to invest a larger amount of money in the current game. This can lead to a higher degree of cooperation and reciprocity between the players, resulting in higher payoffs for both players and they may be more likely to view them in a positive light and exhibit more cooperative behaviour.

This also reduces the complexity of why a rational agent should trust someone. To travel back to our previous logic, let’s see how it becomes modified:

For all x and y,

T(x, y) if and only if [(R(x, y) ∧ P(x, y))]


P(x, y) = x has positive reasons (verifiable trust) to assume or validate that y is trustworthy.

trusted data markets cheqd blog
But how can we alter the time variable, and provide a verifiable “extra factor” to form a model where participants can establish that trust is warranted from inception in market dynamics?

Trusted Data Markets

In two essential references for this blog, Altman defines privacy as “the selective control of access to the self,”¹² and Mason describes the individual who trades private information about the self as a kind of currency in exchange for anticipated goods and services.¹³ Essentially, I should be able to exercise personal agency, select, control and subsequently act (for example, trade) upon information that accesses my personal data and determine with whom I share that access with. This is not a new digital precedent but has been deliberated on in terms of dignity, exceptionalism, and values by philosophers for centuries.¹⁴

Many other well-worked cheqd blogs on self-sovereign identity, trusted data, and cheqd’s payment infrastructure explain how self-sovereign identity and cheqd’s infrastructure can facilitate this paradigm, both from a technical and commercial perspective. What we’ll dive into in this blog is the relevance of this type of data, and selective control of it, to a market dynamic.

In a cheqd trusted data market, “holders” (users, companies, objects) have selective control, and their willingness to share data is dependent on a variety of factors, e.g: benefits, type of information, programming and culture.¹⁵

In “Trusted Data Markets,” companies issue verifiable data to holders, who in turn actively share their data with interested parties (known as “Verifiers”) who wish to verify that data. The reasoning behind this dynamic forming is multifold, but we will focus on commercial benefit for “Issuers”, and we will explore various use cases in subsequent blogs where Trusted Data Market dynamics form around a payment flow: “Verifier pays Issuer.”

Within a “shadow” data market, this payment has already formed, without the user, as we all already interact within Data Markets, but our data is traded upon without our selective control. With cheqd and self-sovereign identity, this paradigm is inverted via a privacy-preserving, standards-compliant data and payment infrastructure. This infrastructure forms the structure for both verifiable trust and payments to support the transactional flows of associated verified and trusted data within the format of verifiable credentials.

The “Issuer” issues Verifiable Data

The “Holder” receives this data, which can be trusted as 100% verifiably issued by the Issuer.

The “Holder” then presents said data (a Verifiable Credential) to the “Verifier/Receiver.

Upon presentation, in which the “Holder” maintains selective control, the Verifier can “check” the verifiability of the Verifiable Credential (the data), via cheqd’s network, and upon this “check” ascertain whether the data is: verifiability issued by the issuer, non-revoked, and of the correct standards.

It is via this “check” a privacy-preserving payment is released from the “Verifier/Receiver” to the “Issuer.” At no point within this market dynamic is selective control of the data removed from the “Holder” and at no point is the presentation of the “Holder’s” data gated by a payment wall.

The verifier can ascertain greater trust in the credential received, via the reputation of the issuer reducing time-to-trust and trust the data issued is from the issuer at genesis.

The price a Verifier is willing to pay correlates to the impact of Verifiable Trust on the market dynamic.

This price is set by the Issuer.

Crucially this solves two significant problems for data markets.


Typically, trust takes significant time to develop and maintain and this in turn informs market dynamic structures. With the import of Verifiable Data, time-to-reliance in the market dynamic is significantly improved. If I can ascertain as a “Verifier” the data Issued to the Holder is 100% from the Issuing participant, I can mitigate the risk of fraud and form a new trust dynamic. This, in turn, can form new reputation metrics, and new commercial models spurring growth as this data has an associated value attached to it for all market participants.


Selective control of data, in a privacy-preserving fashion for “Holders,” where they can meaningfully participate in a new data market paradigm is established. No longer will users participate in “shadow” data markets, they can meaningfully participate and retain ownership and control of their information. Whether this is to access benefits, or indeed indicate what they “prefer,” this data will form a value category which we believe will create better economic outcomes.


cheqd’s trusted data markets present novel solutions for both problems, institutional and consumer participants, whilst maintaining privacy-preserving transactions and interactions, a new data paradigm emerges; with verifiable trust. One where the user is at the centre of their own data universe, and institutions can discover new revenue streams, new reliable ‘trusted’ data, new ways to innovate with customer data and participate in an emerging paradigm fit for the new data economy.

Learn more

We will be following this initial blog with a deep dive into how cheqd’s infrastructure supports the advent of Trusted Data Markets, followed by specific use cases we’re exploring. Beginning with credit data.

If you’d like to learn more, please reach out to us directly: [email protected]

[1] Gkatzelis, V., Aperjis, C., & Huberman, B. A. (2015). Pricing private data. Electronic Markets, 25(2), 109–123. s12525–015–0188–8

[2] Arrow, K. (1972). Gifts and exchanges. Philosophy and Public Affairs, I, 343–362, Fukuyama, F. (1995). Trust. New York: Free Press, Putnam, R. (1993). Making democracy work: Civic traditions in modern Italy. Princeton, NJ: Princeton University Press.

[3] Goldberg, Sanford C., (2020), “Trust and Reliance”, in Simon 2020: 97–108.

[4] Hawley, Katherine, (2014(, “Trust, Distrust and Commitment”, Noûs, 48(1): 1–20. doi:10.1111/nous.12000


[6] pp. 30–32

[7] Spiekermann, S., Acquisti, A., Böhme, R., & Hui, K. L. (2015). The challenges of personal data markets and privacy. Electronic Markets, 25(2), 161–167. 0191–0

[8] Spiekermann, S., & Novotny, A. (2015). A vision for global privacy bridges: Technical and legal measures for international data markets. Computer Law and Security Review, 31(2), 181–200. 10.1016/j.clsr.2015.01.009.

[9] Conger, S., Pratt, J. H., & Loch, K. D. (2013). Personal information privacy and emerging technologies. Information Systems Journal, 23(5), 401–417.

[10] Gkatzelis, V., Aperjis, C., & Huberman, B. A. (2015). Pricing private data. Electronic Markets, 25(2), 109–123. s12525–015–0188–8

[11] Camerer, C. (2003). Behavioral game theory: Experiments in strategic interaction. Princeton, NJ: University Press, Princeton.

[12] Altman, I. (1976). Privacy — a conceptual analysis. Environment and Behavior, 8(1), 7–29.

[13] Mason, R.O., Mason, F., Conger, S. & Pratt, J.H. (2005). The connected home: poison or paradise. Proceedings of Academy of Management Annual Meeting, Honolulu, HI, August 5–10

[14] Floridi, Luciano, On Human Dignity and a Foundation for the Right to Privacy (April 26, 2016). Available at SSRN: or

[15] Hallam, C., & Zanella, G. (2017). Online self-disclosure: The privacy paradox explained as a temporally discounted balance between concerns and rewards. Computers in Human Behavior, 68, 217–227.

Scale your SSI start-up with cheqd


cheqd provides eligible SSI start-ups with the network tools, payment utility and grants to build with us and scale. Entrepreneurs in self-sovereign identity can leverage cheqd’s network and grant program to launch their start-up journey and join us in solving some of the most critical problems in privacy, and identity today.

The ability to build and scale with minimal costs and maximum support in SSI is critical, and at cheqd, we enable our SSI start-ups to utilise our network for verification of trusted data, the utility of our payment rails, use of our sophisticated tooling and productcustomisable business models and ongoing support. 

On top of the network’s core utility and functionality, cheqd also offers inclusion in our forthcoming partnership network. In this self-serve ecosystem, SSI start-ups can engage with cheqd consulting partners, enterprises, and web3 organisations or enter knowledge sharing and potential partnerships with mature SSI vendors. We’re building a budding open-source community and we’re constantly looking for co-creators, builders and innovators to join us on our mission; to reclaim our data and commercialise SSI at scale. 

In announcing our first SSI start-up cohort, we’re also proud to announce a bespoke grant program tailored for SSI start-ups in ideation, pre-seed, seed stages. Eligible SSI start-ups can access $CHEQ token grants to enter cheqd’s ecosystem, begin validating the cheqd network and utilise the network and all its advantages to go-to-market, focus on building, and join us disrupting established paradigms to create new and fair data landscapes of the future.

Here is our first SSI cohort profile and the incredible challenges they’re looking to solve:


“Doshy is an Australian-based digital credentialing platform making it easy for individuals and organisations to issue, receive and verify SSI-enabled verifiable credentials. Doshy joined the cheqd network to enable us to offer our customers an even stronger business case for leveraging verifiable credentials, and contribute to a network that is critical to enabling mainstream SSI adoption. Together, Doshy and cheqd’s partnership will accelerate the decentralisation of digital credentials and identities.” Liem Truong (CEO, Doshy)

cheq Doshy out:


“YellowDotPink was founded on principles ensuring sustainability is equitable, transparent, and truly effecting of change – not just symbolism. We are currently focused on empowering renewable asset owners to benefit from new and emerging energy market structures aimed to deliver Net Zero benefits through electrification. Verifiable credentials (through SSI) deliver significant benefit in the energy sector with the ability to provide consent, verification, and an overarching cyber security capability to meet (through governance) strict critical infrastructure regulations for market participation. YellowDotPink is excited to partner with CHEQD as a leader in the SSI space. The CHEQD network is forging new and progressive governance to SSI and associated payment rails from core values of privacy and security by design. We look forward to working closely with CHEQD to help empower our clients.” Paul Grehan (Director, YellowDotPink)

cheq out YellowDotPink:

And follow them on Twitter.


“We chose cheqd as the payment rail for Known Privacy because of its focus on Self Sovereign Identity. chedq is engineered and optimized specifically to support SSI, making it an easy choice.

Known Privacy was created to give consumers ownership and control over their personal data. We are focused on changing how marketers acquire and use personal data for targeted advertising and personalization. We leverage SSI to empower marketers to pay consumers directly for their data, rather than pay for data acquired through secretive tracking and surveillance companies.

Joining the cheqd community has been an incredibly positive experience. The support directly from the cheqd team and members of the community has been great, even in the midst of an incredibly busy launch. We are looking forward to continued participation and engagement with this amazing project.” John Ruder (CEO, KnownPrivacy)

cheq out KnownPrivacy:

And follow them on Twitter.


“WOPLLI Technologies is an early-stage startup with a vision to make our experiences [as we work, play, learn, live] to be safe, fair and trusted. WOPLLI has adopted 5 key architecture principles to progress this work human centricity, decentralization in identifiers, distribution in data processing, heterogeneity in controls and self-healing. With this, we are working on creating a trusted platform that will enable a person to transact with verified things. DID, SSI and VC are central to the architecture of this platform. Along with the platform, we are creating and contributing to standards and frameworks including at TOIP, DIF, IEEE and NIST. We’re excited to join the cheqd network, to accelerate our vision and join the partnership network to help scale our start up and utilise cheqd’s technology and tooling to build our platform out from our original idea, to product.” Vikas Malhotra (CEO, WOPLLI)

cheq out WOPLLI:

And follow WOPLLI on Twitter.

cheq out WOPLLI on LinkedIn:


“As a founder looking to leverage Self Sovereign Identity to help firms fight financial crime, I was looking for a firm that had great products, people who are passionate about what they do, great to work with and I found cheqd ticked all those boxes.

Client Fabric is on a mission to fight financial crime and fuel commerce by engaging, enabling, empowering clients to Own, Earn from and Do Good with their data. An ecosystem, to support that aspiration, has several elements and we are starting with what, we believe, will make the single biggest difference in accelerating adoption i.e., skilled professionals who can serve both end clients and firms to meet their needs. We are building a global network of Certified Due Diligence Professionals (CDDPs) with the verified credentials that clients and firms can trust.” Arun Kiezpadathil (CEO, ClientFabric)

cheq out ClientFabric:

We know starting a start-up is an exciting, yet challenging time. Whether you’re in ideation, pre-seed or seed, supporting our SSI start-up partners is critical to the success of our network.

We want our network to grow and scale with the innovation of everyone involved in the SSI industry and that’s why we’re proud to announce this first cohort and our grant program.

If you are an SSI start-up looking for a network to support your journey, please get in touch with Tobias Halloran, Head of Partnerships at [email protected]

Partnerships at cheqd

Partnerships at cheqd

This blog is the first part of two. It is addressed to the self-sovereign identity (SSI) community. Part two will be focused on cheqd’s partnerships approach with node operators who are not SSI vendors, and folks from the Cryptographic community.

Collaboration is the new innovation

No company is an island. The ecosystem which it inhabits is critical to its success. This is especially true for companies seeking to disrupt established paradigms. Simply put, industry-shifting innovation like SSI can be accelerated by a dynamic incentivised network like cheqd, and for this vision to materialise collaboration is paramount.

Collaboration through partnering is nothing new to innovation and has been critical throughout human history. Darwin himself famously remarked, “In the long history of humankind those who learned to collaborate and improvise most effectively… prevailed.” In the fast-changing business world of today, we could easily substitute “improvise” with “innovate,” and innovation coupled with collaboration can enable rapid mass-scale transformation, and consequently propel accelerated SSI adoption.

Central to this vision is cheqd’s offering to our partners. We’re offering never-before-seen commercial models, payment rails, innovation at scale, the creation of new ecosystems and what I refer to as; enhanced organisational ambidexterity.

Partnerships augment organisational ambidexterity

Partnerships at cheqd will provide a framework for new commercial approaches that can balance the importance for our partners of maintaining business continuity with necessary strategic change.

On the one hand, our partners can continue to profit from current business opportunities whilst maintaining continuity of service, and on the other hand, they can efficiently explore new commercial models and innovation our network enables that aligns with their overarching vision. Our partners can share risks and costs associated with innovation and value creation and benefit from a suite of mobile and backend software tools that can be embedded by cheqd, whilst implementing strategies to capitalize and capture new value created by cheqd’s network.

This is the kernel of partnerships with the SSI community at cheqd. We strongly believe we can augment our partners’ organisational ambidexterity and enhance strategic outlook with new value capture, monetise self-sovereign identity in innovative ways and ultimately bring SSI to more markets.

cheqd’s Partnership Methodology

As Head of Partnerships at cheqd, a strong conviction of mine is we must put our partners first and work from them backwards. It’s one of the main reasons I joined cheqd, as I quickly discovered this conviction was shared by cheqd’s founders Fraser Edwards, and Ankur Banerjee.

To achieve this for our partners, this is what you can expect. We will Explore, Engage, Empathise, Enable:

  • Explore: Preliminary discovery of value hypothesis and vision alignment.
  • Engage: Establish credibility and trust, deliver initial value propositions, initiate roadmap and goals towards shared vision, expand and transform with commercial & technical drivers.
  • Empathise: Relationship building, stakeholder engagement, long lasting value formed.
  • Enable: Building with our trusted partner, iterating and innovating cheqd’s resources and network to continually define success and create value for the entire ecosystem.

Our SSI partnership methodology will start with the focus on our partners, will culminate with the enablement of new ecosystems through our network and will continually prioritise our partners success through industry best practices and within a framework of innovation & delivery.

Join cheqd as a partner

We are currently running TestNet with strong inbound interest from key members of the SSI community and existing SSI partners like Evernym and DIDx leading the way. Our MainNet launch is on the imminent horizon as we value speed-to-market.

To join our network, we have an onboarding system in place where you will get started with:

  • A kick-off meeting with myself, and relevant internal stakeholders.
  • A welcome pack and partner intake form.
  • Access to our open-source Slack community for setting up and troubleshooting your node, and TestNet token distribution.
  • Regular meetings as required (we know sometimes a 10-minute call solves problems much faster than 30 minutes of text).
  • Weekly network updates through newsletters and access to all social media channels.
  • An iterative process, which will evolve and change pending your feedback.
  • We’re also open to co-marketing activities, such as events and digital campaigns.

We’ve run internal timers on setting up nodes for TestNet and have onboarded experienced technical operators and business stakeholders. We’re looking at minimal time commitment, ensuring our partner’s development sprints, cycles aren’t diverted or impacted. It’s simple, quick and easy!

If you’re interested in a partnership, or to know more about our program, please visit this page:

To get started and onboarded please contact Tobias Halloran at [email protected]

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