Modern organisations increasingly rely on AI to support complex decision-making, but most systems fail when transparency, traceability, and accountability are required. This is where Dedoctive takes a different approach. It is designed as an auditable AI platform that ensures every output is grounded in trusted evidence and can be fully traced back to its source.

Rather than acting as a black-box AI tool, Dedoctive focuses on governed, human-in-the-loop workflows where every step of reasoning is visible, reviewable, and defensible. It helps organisations move from raw, unstructured information toward structured, evidence-based intelligence that can be used in high-stakes environments.

Key Features

User Interface

The platform is built around structured workflows and knowledge models, allowing users to interact with documents, data, tables, and images in a unified environment. The interface is designed to support clarity in complex decision processes rather than generic chat-based interactions.

Accuracy & Performance

Dedoctive focuses on grounding outputs in validated evidence. Every response is linked to source-level provenance, ensuring that information is not only generated but also verifiable. This improves confidence in environments where accuracy is critical.

Capabilities

• Transforms documents, tables, and images into structured knowledge models
• Generates traceable reports and formal diagrams
• Provides Response Maps showing how answers are formed
• Supports governed BPMN workflows with human-in-the-loop control
• Works with large language models without requiring model training

Security & Privacy

The system is designed for enterprise environments where governance, compliance, and auditability are essential. Its architecture ensures that outputs remain traceable to their original sources, supporting accountability in regulated industries.

Use Cases

• Compliance and regulatory decision-making
• Safety-critical and assurance workflows
• Healthcare and clinical decision support
• Supply chain and operational planning
• Crisis response and risk assessment
• Public sector and policy evaluation

Pros and Cons

Pros

• Strong focus on evidence-based AI outputs
• Full source-level traceability and provenance
• Designed for regulated and high-stakes environments
• Supports structured, governed workflows

Cons

• More suited for enterprise use than casual users
• May require onboarding for workflow setup
• Not designed as a simple consumer chatbot tool

Pricing Plans

The platform is available through enterprise deployments, micro-pilots, and partner-led projects. Organisations typically engage through pilot-based evaluation before scaling to full deployment depending on operational needs.

How to Use Dedoctive

Users begin by selecting a document-heavy decision process where evidence quality and traceability are important. Data such as documents, tables, and other inputs are then ingested into the system. The platform builds structured knowledge models and enables governed workflows where outputs can be reviewed, traced, and validated step by step.

Comparison with Similar Tools

Unlike generic AI tools that generate responses without transparent sourcing, Dedoctive is built around auditable AI principles. Its emphasis on provenance, structured workflows, and human-in-the-loop governance makes it particularly suitable for organisations that require defensible and reviewable outputs rather than purely generative results.

Conclusion

Dedoctive is positioned for organisations that need more than standard AI assistance. By combining structured knowledge models, governed workflows, and full traceability, it enables teams to make decisions that are not only faster but also explainable and defensible in critical environments.

Frequently Asked Questions (FAQ)

What makes Dedoctive different from standard AI tools?

It focuses on auditable AI with full provenance, meaning every output can be traced back to its supporting evidence.

Is it suitable for non-technical users?

It is primarily designed for enterprise and structured decision workflows rather than casual use.

What industries is it used in?

It is used in compliance, healthcare, safety, public sector, and other document-heavy environments.

Does it require training AI models?

No, it works with existing large language models without requiring model training.

How is trust ensured in outputs?

Through source-level provenance, Response Maps, and governed human-in-the-loop workflows.