Auditable AI for defensible decisions

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June 5, 2026

2026 Rank: #1882

Dedoctive

Decision making

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Auditable AI for defensible decisions

AI Transparency Auditable AI Trustworthy Decision Making AI Explainability Defensible-decisions AI Interpretability

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Overview

Dedoctive is an Artificial Intelligence tool designed to build trustworthy and defensible decision-making systems. Its focus is on ensuring the reliability of AI-powered solutions by grounding their outputs in trusted evidence and governed workflows, while providing comprehensive source-level provenance.

Its functionalities cater to organizations aiming to build AI models embedded with human-in-the-loop safeguards, aiming to resolve the issue of trust in AI-based systems.Essentially, Dedoctive allows organizations to construct auditable AI workflows, underpinned by verified knowledge, controlled reasoning, and explicit traceability back to original sources.

It achieves this by overlaying any Large Language Model to yield highly reliable outputs with detailed traceability back to the original data.In its operations, Dedoctive utilizes curated knowledge models to assure responses drawn solely from validated information.

It provides granular provenance hyperlinks that help trace every source, potentially to individual table cells or image background text. Dedoctive optimally combines AI reasoning within transparent, compliant processes using visually designed hybrid workflows.Consequently, Dedoctive provides comprehensive scenarios in finding solutions, identifying deeply linked data items, and extracting the most relevant knowledge to answer queries.

All of its responses are based on expert verified sources, ensuring no inaccurate or misleading information is presented.Finally, Dedoctive assures user data safety with best practice security protocols, offering solutions such as on-premises, private cloud deployments, or automated redaction mechanisms for added data protection.

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Initial release

June 4, 2026

Initial release of Dedoctive.

Author

Keith Harrison-Broninski

@keith-harrison-broninski

Keith Harrison-Broninski is an author, researcher, and keynote speaker specialising in cross-boundary collaboration, community antifragility, and technology for good. Keith's awards include from Gartner for social enterprise and from the NHS for technology to replicate healthcare innovations.

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Pros and Cons

Pros

Builds trustworthy decision-making systems

Provides source-level provenance

Explicit data traceability

Overlays any Large Language Model

Curated knowledge models

Granular provenance hyperlinks

Visually designed hybrid workflows

Comprehensive response scenarios

Deep linked data items

Verified knowledge sources

Private cloud deployments

Provides on-premises solutions

Incorporates best practice security

Automated redaction mechanisms

Human-in-the-loop safeguards

Controlled reasoning capabilities

BPMN compliant workflows

Avoids misleading information

Tailored for problem-solving scenarios

Multi-step workflows

Interconnects related information

Direct hyperlinks to sources

Transparent, compliant processes

Enterprise-friendly deployment models

Prevents data hallucination

Free IDE for workflows

Complete data quality assurance

Certified with Cyber Essentials

Unable to generate inaccurate responses

View 24 more pros

Cons

Requires curated knowledge models

Possibly complex workflow creation

Relies heavily on source traceability

Non-intuitive for basic users

No support for unsourced data

Depends on expert-verified sources

No hallucination ability limits creativity

Limited use without reliable data

Potentially slower due to traceability requirements

Security limited to known protocols

View 5 more cons

Q&A

What is the main purpose of Dedoctive?

The main purpose of Dedoctive is to build trustworthy and defensible decision-making systems. It focuses on ensuring the reliability of AI-powered solutions by grounding their outputs in trusted evidence and governed workflows, providing comprehensive source-level provenance.

What are the key features of Dedoctive?

The key features of Dedoctive include: auditable AI workflows, provenance tracking, human-in-the-loop safeguards, data traceability, curated knowledge models, granular provenance hyperlinks, transparent processes, automated redaction mechanisms, verified knowledge, controlled reasoning, deep linked data items, private cloud deployments, security protocols, data protection, and on-premises deployment.

How does Dedoctive ensure the reliability of AI-powered solutions?

Dedoctive ensures reliability of AI-powered solutions by grounding their outputs in trusted evidence and governed workflows. It overlays any Large Language Model to yield highly reliable outputs with detailed traceability back to the original data. It utilizes curated knowledge models to assure responses drawn solely from validated information.

What is meant by 'human-in-the-loop safeguards' in the context of Dedoctive?

'Human-in-the-loop safeguards' in the context of Dedoctive refers to the implementation of checks and measures in the AI workflows that require human intervention or approval. This hybrid approach ensures automation efficiency while maintaining human oversight for critical decision-making aspects.

How does Dedoctive implement traceability back to original sources?

Dedoctive implements traceability back to original sources by providing granular provenance hyperlinks. These links help trace every source, potentially to individual table cells or image background text. Hence, every output can be traced back and verified from its original source.

How does Dedoctive utilize curated knowledge models?

Dedoctive utilizes curated knowledge models by ensuring that responses are drawn only from validated information. This process involves curating knowledge from reliable sources to provide definitive and accurate responses.

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Can Dedoctive provide provenance hyperlinks to individual table cells or image background text?

Yes, Dedoctive can provide provenance hyperlinks to individual table cells or image background text. It gives granular level traceability to every piece of information it uses to formulate responses.

How does Dedoctive ensure transparency and compliance in its processes?

Dedoctive ensures transparency and compliance in its processes by optimally combining AI reasoning within visually designed, compliant processes. It uses hybrid workflows that embed AI reasoning within transparent processes.

What kind of solutions does Dedoctive provide to find and extract relevant knowledge?

Dedoctive provides comprehensive scenarios in finding solutions, identifying deeply linked data items, and extracting the most relevant knowledge to answer queries. It uses workflows created by human experts and can identify deep connections linking information items, even those that seem unrelated.

How does Dedoctive ensure the accuracy of the information it presents?

Dedoctive ensures the accuracy of the information it presents by basing all responses on expert-verified sources. It provides only the knowledge most relevant to the question from verified, reliable sources and if no answer is available, Dedoctive explicitly states so.

What security protocols does Dedoctive use to assure data safety?

Dedoctive uses best practice security protocols to protect user information and ensure confidentiality. It's certified with Cyber Essentials, demonstrating its commitment to cybersecurity.

Does Dedoctive offer on-premises and private cloud deployments?

Yes, Dedoctive does offer on-premises and private cloud deployments. These provide more control over data and processes, while ensuring data safety.

What automated redaction mechanisms does Dedoctive use for data protection?

In addition to its other security measures, Dedoctive offers automated redaction mechanisms for added data protection. These measures ensure that no sensitive information is ever transmitted openly to cloud services, thereby further ensuring data privacy.

How does Dedoctive's controlled reasoning work?

Dedoctive's controlled reasoning involves underpinning every output of the AI workflows with verified knowledge. It uses controlled reasoning to maintain the integrity, accountability, and audibility of the workflows.

What is meant by 'deep linked data items' in Dedoctive?

'Deep linked data items' in Dedoctive refers to the identification of connections linking information items, even those that seem unrelated. Dedoctive uses this feature to make hidden knowledge and new opportunities available to users.

Can Dedoctive overlay any Large Language Model?

Yes, Dedoctive can overlay any Large Language Model. It uses this capability to yield highly reliable outputs with detailed traceability back to the original data.

How does Dedoctive construct auditable AI workflows?

Dedoctive constructs auditable AI workflows by grounding them in verified knowledge, controlled reasoning, and full source provenance. This process allows the creation of transparent and defensible decision-making systems.

What does it mean that Dedoctive provides source-level provenance?

Providing source-level provenance means that Dedoctive traces back every piece of data used in its reasoning to its original source. This level of traceability enables users to verify the authenticity and accuracy of the information used by Dedoctive.

How does Dedoctive utilize validated information?

Dedoctive utilizes validated information by basing all its responses on data from expert-verified sources. In doing so, it ensures that no inaccurate or misleading information is presented.

Can I trace back every source of Dedoctive's response?

Yes, you can trace back every source of Dedoctive's response. Dedoctive provides granular provenance hyperlinks that trace every source, potentially to individual table cells or image background text.

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