Matrix
dMatrix is a decision intelligence platform that unifies AI conversations, stakeholder input, and manual refinement into a single, evolving decision matrix. It preserves options, criteria, rationale, and provenance, enabling people and AI to reason from the same structured context and stay aligned on complex choices. The platform supports scoped contributor participation, AI confidence attributes, and full audit trails.
- Artificial Intelligence
- AI Agents
- Enterprise Software
- Software Only
Funding
Founders
Product
Problem
Complex decisions rarely happen in one clean conversation; they unfold across AI chats, meetings, research notes, stakeholder input, and private judgment. Without a shared structure, this reasoning fragments—criteria drift, rationale disappears, and every conversation starts from a different version of what matters, leading to misalignment and poor decision quality.
Solution
dMatrix organizes the thoughts, data, discussions, and contributions behind complex decisions into a living decision workspace—a structured matrix that captures what matters, what's being compared, and why. Users can start from an AI conversation, direct refinement, or stakeholder input, and seamlessly move between these modes as the decision develops. The platform extracts options, tradeoffs, and rationale from AI conversations with provenance and confidence attributes, while human contributors can participate in scoped areas of the matrix. The matrix stays current and becomes the shared frame that people and connected AI environments reason from, preserving audit trails and making the reasoning behind each decision transparent and revisable.
Target Audience
Primary users are product teams, strategy leads, and knowledge workers at startups and enterprises who need to align human judgment and AI assistance around complex, multi-stakeholder decisions involving tradeoffs and evolving information.
Features
- Living decision matrix capturing criteria, weights, options, scores, rationale, and approvals in one evolving workspace
- AI conversation integration that extracts decision structure (options, tradeoffs, unresolved questions) with confidence attributes and provenance
- Scoped stakeholder input, allowing contributors to vote or comment on specific parts of the matrix without needing the full picture
- Direct manual refinement for precise edits to structure, criteria, scores, or rationale without forcing conversational restatement
- Transparent provenance with citations, audit trail, snapshots, and AI confidence scores on every contribution
- AI context sharing, enabling connected agents to read the matrix and contribute structured input back, with human approval control
- Flexible starting points—begin from a chat, spreadsheet-like matrix, or stakeholder invite, with no fixed workflow