Overview
- Deliver fully auditable AI decisions that stand up to scrutiny by grounding every output in verified knowledge and controlled reasoning
- Trace every answer back to its exact origin with granular provenance hyperlinks—down to individual table cells or image text—for complete source-level provenance
- Eliminate hallucinated or misleading responses by ensuring all outputs are drawn exclusively from curated, expert-verified sources
- Uncover hidden connections between seemingly unrelated data items through deep-linked knowledge extraction, revealing new opportunities
- Maintain full compliance and transparency with visually designed hybrid workflows that embed AI reasoning inside governed, human-in-the-loop processes
- Protect sensitive data with automated redaction mechanisms that prevent confidential information from ever being transmitted to cloud services
- Retain complete control over your AI infrastructure with on-premises or private cloud deployment options, backed by Cyber Essentials certified security protocols
Pros & 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
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
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❓ Frequently Asked Questions
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.
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.
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.
'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.
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.
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.
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.
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.
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.
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.
Dedoctive uses best practice security protocols to protect user information and ensure confidentiality. It's certified with Cyber Essentials, demonstrating its commitment to cybersecurity.
Yes, Dedoctive does offer on-premises and private cloud deployments. These provide more control over data and processes, while ensuring data safety.
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.
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.
'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.
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.
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.
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.
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.
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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