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Blockchain-as-a-Service for Responsible AI

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dataProvinance

Data provenance for AI

Build trust in AI by tracking your
data's journey—its source, changes,
and use. Maintain clear records for
integrity and ethical compliance.

dataProvinance

AI Model Lineage

Clarify AI model development by tracing datasets, models, algorithms, and hyperparameters, ensuring reproducibility and accountability.

dataProvinance

AI Decision Attribution

Clarify AI decisions with clear audit
trails to validate outcomes and ensure accountability for responsible AI.

Data provenance for AI
AI Model Lineage
AI Decision Attribution

Data provenance for AI

Data lineage provides a verifiable audit trail of your data's origins, transformations, and usage, ensuring integrity and compliance from inception to AI model training and beyond. For AI experts, robust data provenance is critical for building models that are not only accurate but also fair, compliant, ethical and reliable.

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Challenges for AI Experts

Opaque Data Pipelines
Opaque Data Pipelines: Difficulty in tracing data origins especially when using diverse, third-party, or publicly available datasets, leading to uncertainty about data quality and suitability.
Data Provenance Uncertainty
Lack of clear documentation for datasets raises questions about their origin, collection methods, inherent biases, and adherence to ethical and privacy standards (e.g., GDPR, CCPA, HIPAA).
Compromised Data Integrity
Undocumented alterations or accidental corruptions in data can go unnoticed, leading to flawed model training and skewed AI outputs.
Regulatory & Compliance Nightmares
Increasing regulations like the EU AI Act demand demonstrable data transparency and accountability, which is challenging without meticulous data lineage.
Difficulty in Debugging & Bias Detection
When models produce unexpected or biased results, tracing back to problematic data points becomes a time-consuming and often inconclusive task.
solution

FLEXBLOK Solution

Immutable Data Records
Every data source, transformation, and access event is recorded as a timestamped, tamper-proof entry on the blockchain, ensuring data integrity and preventing unauthorized modifications.
Transparent Data Lifecycle
Track the complete journey of your datasets, from initial collection and preprocessing to their use in model training and retraining, providing a single source of truth.
Enhanced Auditability & Compliance
Simplify regulatory audits with easily verifiable data provenance records, demonstrating adherence to data governance and ethical AI principles. Our platform supports features like digital notarization and proof of authenticity for datasets.
Secure Data Sharing & Collaboration
Utilize features like our Data Tracing API and token-gating smart contracts (as seen in our AI Training Model use case) to securely share and manage access to data with verifiable permissions.
Rapid Root Cause Analysis
Quickly identify and rectify issues by backtracking through the immutable data lineage records if biases or errors are detected in AI models.

AI Model Lineage

AI Model Lineage cuts through complexity by creating a detailed, verifiable history of your model's entire lifecycle – from the datasets used, pre-trained architectures incorporated, to every fine-tuning iteration and deployment. For AI experts, this means enhanced reproducibility, better collaboration, and true accountability.

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Challenges for AI Experts

Model Composition Ambiguity
Difficulty in tracking the layers of influence when models are built upon other pre-trained models (e.g., from Hugging Face), making it hard to understand foundational dependencies.
Intellectual Property Concerns
Difficulty in proving ownership or lineage of custom models, especially in collaborative or open-source influenced environments.
Lack of Reproducibility
Inability to consistently replicate model performance due to undocumented changes in code, data, or environment parameters.
"Black Box" Phenomenon
Limited visibility into the internal workings and evolutionary path of a model, making debugging and explaining its behavior challenging
Accountability & Attribution Gaps
Without clear documentation of contributions (data, code, parameters), assigning credit or responsibility for model behavior, including biases or errors, becomes problematic.
Versioning & Experiment Management
Managing countless model versions, configs, and results often leads to confusion, hindering auditable, scalable AI Ops workflows.
solution

FLEXBLOK Solution

Complete Model Traceability
Our system enables users to backtrack from a deployed AI model to its source datasets, specific versions of pre-trained models, training configurations, and all algorithmic components.
OpenLineage Compliant
Capture detailed model lineage metadata, including datasets, jobs, runs, and facets in a way that is fully compliant with the OpenLineage standard, and anchor this verifiable history securely on the blockchain.
Immutable Versioning & Audit Trails
Every change, update, or experiment related to the model is cryptographically secured and logged on the blockchain, creating an unalterable history.
Transparent Model Composition
Clearly document and verify every pre-trained model, library, and custom code segment incorporated into your AI system, tracing them back to their origins.
Enhanced Accountability
Immutable blockchain records ensure that every modification, parameter change, or dataset used is transparently documented, fostering clear accountability among development teams.
Integration for Data Quality
Seamless integration with tools like Cleanlab helps ensure that the datasets used for training your AI models are clean and high-quality, directly impacting the reliability and trustworthiness of your model lineage.
Streamlined Model Governance
Simplify the often-complex process of managing model lifecycles, ensuring that only validated and approved model versions are deployed.

AI Decision Attribution

As AI systems take on critical roles across industries, understanding why they make certain decisions is essential. AI Decision Attribution links inputs, model logic, and outputs to ensure trust, fairness, and control.

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Challenges for AI Experts

The "Explainability Gap"
Many advanced AI models, particularly deep learning networks, operate as "black boxes," making it difficult to interpret their decision-making logic.
Assigning Responsibility
When an error occurs, pinpointing whether it stemmed from a specific dataset, model, or external data source is difficult, especially when systems combine proprietary algorithms and third-party inputs.
Bias Amplification & Unfair Outcomes
AI models can inadvertently learn and perpetuate biases present in their training data, leading to discriminatory or unfair decisions that are hard to trace back to their root cause without clear attribution.
Lack of Trust & User Adoption
Users are less likely to trust or adopt AI systems whose decisions they cannot understand or verify.
Regulatory Compliance & Auditability
Regulations increasingly require organizations to explain AI-driven decisions, especially in sensitive areas like finance, healthcare, and justice.
Difficulty in Recourse & Rectification
Without understanding how a decision was made, it's challenging to provide mechanisms for appeal or to correct systemic issues.
Multi-Model & AI Agent Collaboration
In complex AI systems where multiple models and agents interact, each trained on different datasets, it becomes challenging to trace how each component influenced a final decision. Understanding these interdependencies is essential for validating outcomes in collaborative or agentic AI environments.
solution

FLEXBLOK Solution

Secure Logging of Decision Parameters
Key input data, model versions used, and critical operational parameters influencing an AI decision can be securely hashed and recorded on the blockchain.
Immutable Audit Trails for AI Decisions
Create a tamper-proof history of the decision-making process, allowing for retrospective analysis and verification of how a specific outcome was reached.
Facilitating Explainable AI (XAI) Integration
While blockchain itself doesn't make a model inherently explainable, it provides a secure foundation to log the outputs and explanations from XAI tools, ensuring their integrity and verifiability.
Enhanced Accountability Frameworks
By providing a verifiable record of the factors contributing to an AI decision, FLEXBLOK helps establish clearer lines of responsibility when outcomes need to be reviewed.
Automated Attribution via Smart Contracts
For AI systems involving multiple contributing models or data sources in a decision pipeline, smart contracts can automate the logging and attribution of each component's influence.
Building Trust Through Transparency
Enable stakeholders to gain greater confidence in AI systems by providing access to verifiable (permissioned) records of decision pathways, crucial for critical applications.
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FLEXBLOK: Your Partner for Building Trustworthy AI

In today’s fast-evolving AI landscape, trust, transparency, and traceability are essential. FLEXBLOK is an enterprise-grade Blockchain-as-a-Service platform built to address AI’s unique challenges. It enables teams to move beyond the “black box” and build systems that are auditable, resilient, and aligned with ethical and regulatory expectations.

End-to-End AI Lineage Tracking
Comprehensive Traceability: From the origin of your training data, through every iteration of model development and pre-trained model integration, to the final AI-driven decision, FLEXBLOK provides a complete, immutable audit trail.
Granular Insight: Understand the precise components, configurations, and data versions influencing your AI's behavior and performance.
Immutable & Verifiable Records
Data & Model Integrity: Leverage blockchain’s inherent tamper-proof nature to ensure that your data and model artifacts are secure and their history is unalterable.
Enhanced Accountability: Create a clear, indisputable record of all activities, changes, and contributions within your AI development lifecycle.
Purpose-Built for AI Workflows
Rich API Suite:Utilize our specialized APIs (Data Tracing API, Document API, Contract API, Token API) to seamlessly integrate blockchain capabilities into your existing AI pipelines and tools.
Support for Pre-trained Models: Specifically designed to help you manage the lineage of models built with components from repositories like Hugging Face.
Facilitating Ethical & Responsible AI
Bias Detection & Mitigation Support:Transparent lineage helps in identifying and addressing biases in data and models more effectively.
Compliance & Audit Readiness:Simplify adherence to evolving AI regulations and standards with readily available, verifiable documentation.
Ethical AI Assurance:Our platform is a foundational layer for building AI systems that are fair, accountable, and transparent.
Enterprise-Grade BaaS Platform
Scalability & Performance:FLEXBLOK is designed to handle the demands of complex AI projects, ensuring high availability and throughput.
Security & Reliability:Benefit from enterprise-level security features inherent to our BaaS offering.
Ease of Adoption:Simplified development and deployment of blockchain applications tailored for AI use cases, reducing complexity and accelerating time-to-value. Interoperability:Designed to comply with enterprise standards for seamless integration.
Empowering Key AI Imperatives
Data Provenance:Know exactly where your data comes from and its entire lifecycle.
Model Reproducibility:Reliably replicate AI model results for validation and further development.
Decision Explainability:Provide the foundational data for understanding and justifying AI-driven outcomes.
model

Real-World Use Cases

Unlocking Responsible AI with Proven, Industry-Ready Solutions

Secure Patient Data Management

“FLEXBLOK’s blockchain backbone gave us unmatched confidence in securing patient records while maintaining compliance”

Solving AI lineage and Data Provenance

“With FLEXBLOK, we traced every transformation step in our AI pipeline—boosting trust and accelerating model audits.”

Data Provenance for AI Training Model

FLEXBLOK enabled granular tracking of data origins, making our training sets fully transparent and ethically sourced.”

Stop wrestling with opaque AI systems

 Let our experts show you how to integrate seamless traceability
and verifiable lineage into your AI workflows.

Client reviews

Hear first-hand from our incredible community of customers.

client1

We’ve been using Untitled to kick start every new project and can’t imagine working without it.

client1
Sienna Hewitt

Project Manager, Warpspeed

client2

Untitled has become an essential part of our design process. It speeds up our workflow and ensures every project starts with a solid foundation.

client1
Caitlyn King

COO, OdeaoLabs

client3

Every project starts with Untitled, and it's made a huge difference in our output. It's a game-changer for our design team.

client1
Olly Schroeder

Designer, Nietzsche

Frequently asked questions

Everything you need to know about the product and billing.

How can blockchain specifically solve data provenance issues in AI?
A: Blockchain creates an immutable, timestamped ledger for every piece of data and its transformations. This means you have a verifiable, unalterable history of your data's origin, who accessed it, and how it was changed. For AI, this ensures the integrity of training data, helps identify biases at the source, and makes auditing data straightforward, directly addressing data provenance uncertainty. Keywords: blockchain AI data provenance, immutable data ledger AI, AI training data integrity, verifiable data history AI.

Keywords: blockchain AI data provenance, immutable data ledger AI, AI training data integrity, verifiable data history AI.

What is AI model lineage, and how does blockchain enhance it?
A: AI model lineage is the documented history of an AI model's development, including datasets, code versions, hyperparameters, and pre-trained components. Blockchain enhances this by storing each step and artifact of the model's lifecycle as a tamper-proof record. This ensures complete traceability from a deployed model back to its origins, improving reproducibility, accountability for model behavior (like bias), and easier debugging.

Keywords: AI model lineage blockchain, traceable AI models, reproducible AI blockchain, blockchain for AI model versioning.

How does blockchain contribute to Explainable AI (XAI) and decision attribution?
A: While blockchain doesn't inherently make models explainable, it provides a secure and immutable infrastructure to log the inputs, outputs, and explanatory artifacts from XAI tools. This creates a verifiable audit trail for AI decisions, making it possible to reliably trace why a specific decision was made and attribute it to specific data or model versions. This enhances accountability and trust in AI outcomes. Keywords: blockchain explainable AI, AI decision attribution blockchain, auditable AI decisions, responsible AI blockchain.

Keywords: blockchain explainable AI, AI decision attribution blockchain, auditable AI decisions, responsible AI blockchain.

Can blockchain improve the security of AI models and data?
Blockchain's decentralized and cryptographic nature enhances security by making data and model records tamper-evident and resistant to unauthorized changes. It can secure the storage and access to sensitive training data, protect the intellectual property of AI models by proving lineage, and ensure that only authorized versions of models are deployed.

Keywords: blockchain AI data security, secure AI models blockchain, decentralized AI data, AI intellectual property protection blockchain.

Is integrating blockchain into existing AI workflows complex?
A: While it can seem daunting, platforms like FLEXBLOK (Blockchain-as-a-Service) are designed to simplify this integration. FLEXBLOK offers APIs (like Data Tracing API, Document API) and tools that allow AI developers to incorporate blockchain functionalities without needing deep blockchain expertise, focusing on easy adoption and interoperability with existing AI pipelines.

Keywords: Blockchain as a Service AI, easy blockchain integration AI, AI workflow blockchain.

What are "smart contracts," and how are they relevant for AI on the blockchain?
A: Smart contracts are self-executing contracts with the terms of the agreement directly written into code. They run on a blockchain, becoming transparent, immutable, and automated. In AI, smart contracts can automate processes like data access permissions based on predefined criteria, royalty distribution for AI model usage, or even trigger actions based on AI model outputs in a verifiable way.

Keywords: smart contracts AI blockchain, automated AI governance, blockchain AI automation, decentralized AI applications.

Does using blockchain for AI impact scalability or performance?
A: This is a valid consideration. While early blockchains had scalability limitations, modern enterprise blockchain solutions like FLEXBLOK are designed for high throughput and scalability. The specific impact depends on the blockchain architecture and how it's integrated. For AI lineage and provenance, not all data needs to be on-chain; often, hashes and critical metadata are stored, balancing integrity with performance.

Keywords: scalable blockchain for AI, AI performance blockchain, enterprise blockchain AI, FLEXBLOK scalability.

Flexlok

FLEXBLOK is a Blockchain-as-a-Service (BaaS) platform that simplifies blockchain adoption by enabling fast, cost-effective development of blockchain applications.

A Innoflexion Initiative
www.innoflexion.com

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PLATFORM IN-BUILT API

  • Token API
  • Decentralized ID API
  • Transaction API
  • Document API
  • Data Tracing API
  • Account API
  • Contract API

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