Problem solved

Organizations using AI for critical decisions face significant trust issues: models can hallucinate, reasoning is opaque, and outputs lack verifiable sources. Simultaneously, building custom AI applications with proper governance is technically challenging. Dedoctive solves this by providing a platform to create auditable AI workflows where every response is derived from curated knowledge bases, includes granular source hyperlinks, and is embedded in transparent processes. This makes AI decisions defensible, compliant, and checkable.

Use cases

• Auditing financial reports with direct traceability to source data and cells
• Verifying regulatory compliance in healthcare by cross-referencing policies
• Generating evidence-based research reports with citations to original sources
• Conducting legal analysis with hyperlinks to specific paragraphs in case law
• Assessing supply chain risks using validated supplier data and documents
• Building transparent AI assistants for customer service in regulated industries

Audience

• Developers
• ML Engineers
• Data Scientists
• Business Users

Pros

• Granular source traceability down to table cells and image elements
• Prevents hallucination by drawing only from validated sources
• Visual BPMN workflow design for transparent, compliant processes
• Interactive analytical tools to uncover hidden connections and arguments
• Compliance with government data quality dimensions (e.g., correctness, completeness)

Cons

• Requires upfront effort to curate and maintain validated knowledge models
• Workflow design may require familiarity with BPMN notation
• Dependent on underlying LLM's reasoning capabilities for complex tasks