The Problem We Solve

The current data economy is broken – here’s why:

High-Value Data is Locked in Silos

Most valuable data – like public live discussions, sentiment shifts, and emerging trends – is trapped within walled gardens or hidden behind costly API paywalls. This makes it inaccessible to businesses, independent researchers, and even emerging AI models that require diverse and large datasets to function effectively.

Teneo provides a decentralized alternative by enabling users to contribute their computing power to collect and unlock high-value public social media interactions offering a more open and inclusive approach to data aggregation.

Users Create Valuable Data But Never Benefit From It

Any time someone uses social media, visits a website, or interacts with digital content, they generate data that platforms monetize – without sharing any of the revenue with the user. Teneo flips this model by allowing users to contribute to data collection while receiving a fair share of its value.

Everyone Relies on Real Time Data but Access it Limited

Real-time data powers everything – from individual decision-making to business strategy and AI innovation. Whether you’re a trader tracking market sentiment, an AI model learning from live trends, a developer building data-driven apps, or a marketing team adjusting campaigns in real time, access to fresh, reliable data is essential.

Yet, despite its importance, accessing real-time data remains a major challenge. Businesses, developers, and individuals alike face outdated, incomplete, or overpriced datasets, limiting their ability to make informed decisions. Traditional APIs are expensive, restrictive, and unreliable – with sudden shutdowns or changes that cut off access overnight.

For everyone that depends on up-to-the-minute insights these barriers are more than just an inconvenience; they are bottlenecks that slow innovation, increase costs, and reduce competitive advantage.

Data is the Bottleneck of AI

The quality of AI models depends entirely on the quality of data they are trained on. If the input data is biased, polluted, or outdated, AI-generated outcomes will reflect these flaws – leading to misinformation, inefficiencies, and unreliable decision-making.

The current AI training trend involves feeding models with ever-larger datasets, often scraping the entire internet. However, as more AI-generated content floods the web, models are increasingly trained on low-quality, repetitive, and even false information.

Additionally, 50% of social media content is now generated by bots, creating a distorted view of online discussions. AI models trained on this unfiltered noise struggle to differentiate between real human interactions and synthetic engagement, leading to flawed insights and unreliable predictions.

Lack of Decentralization

Social media data is controlled entirely by centralized entities, limiting who can access it, how it’s used, and who profits from it. These gatekeepers decide:

• Who gets access (large enterprises with big budgets).

• How much it costs (expensive API pricing models).

• When it can be revoked (sudden API shutdowns and policy changes).

Web3 is built on openness, decentralization, and fair value distribution—but current data access models contradict these principles. Without decentralized alternatives, Web3 applications remain dependent on centralized data monopolies, restricting innovation and scalability.