Bring decision structure to the AI environments where you already work.
Use datrix’s inline AI for targeted help inside the product, or connect supported AI environments through MCP-powered workflows for deeper exploration, existing conversations, and broader AI-heavy work. Either way, the matrix gives AI a shared decision frame to work from and a structured place to contribute back.
Start in datrix, continue in a connected AI environment, or move between both. Users can seamlessly move between connected environments while working on the same matrix.
Use inline AI. Work in connected AI environments. Move between both.
Targeted help inside the product
Use datrix’s built-in AI when you want targeted help inside the product: adding options, suggesting criteria, weighing in on scoring, surfacing tradeoffs, or refining the matrix directly.
Where the broader work happens
Use supported AI environments for AI-heavy exploration, expansive conversations, existing threads, or any workflow where the decision is part of a larger discussion. Through MCP-powered workflows and supported extensions, those environments can read from and contribute to the matrix.
Inline AI, connected AI environments, manual refinement, and stakeholder input can all contribute to the same living decision workspace.
Targeted help, right inside the product.
When you want a focused assist without leaving datrix, inline AI is there: add options, suggest criteria, weigh in on scoring, surface tradeoffs, or refine the matrix directly.
Inline AI is convenient, focused, and surgical — not the only or most expansive AI mode. It keeps you moving without turning every change into a conversation.
Add & suggest.Surface options and criteria you may be missing.
Weigh in on scoring.Get a second read on scores and tradeoffs in place.
| COMMERCIAL | PRODUCT | DELIVERY | Score | ||||
|---|---|---|---|---|---|---|---|
Price / TCO w5 | Contract w2 | Ease of use w4 | Features w4 | Setup ease w2 | Support w2 | ||
| HubSpot | 7 | M | 8 | HIGH | 9 | 78.3 | |
| 2Zoho | 9 | XL | 9 | MED | 8 | 76.6 | |
| 3Pipedrive | 8 | L | 6 | HIGH | 7 | 72.5 | |
You may be missing a criterion: Security posture. Add it?
Work where the decision already lives.
Use supported AI environments for AI-heavy exploration, expansive conversations, existing threads, or any workflow where the decision is part of a larger discussion. Those environments can read from and contribute to the matrix.
datrix goes where the decision work is already happening — the matrix gives the conversation a shared frame, and the conversation gives the matrix new input.
| COMMERCIAL | PRODUCT | DELIVERY | Score | ||||
|---|---|---|---|---|---|---|---|
Price / TCO w5 | Contract w2 | Ease of use w4 | Features w4 | Setup ease w2 | Support w2 | ||
| HubSpot | 7 | M | 8 | HIGH | 9 | 78.3 | |
| 2Zoho | 9 | XL | 9 | MED | 8 | 76.6 | |
| 3Pipedrive | 8 | L | 6 | HIGH | 7 | 72.5 | |
MCP lets datrix meet users where they already work.
MCP support lets connected AI environments interact with the matrix as structured decision context. Instead of trapping the decision inside one chat, the matrix can stay portable across the tools and conversations where the work is happening.
Extensions make this approachable by giving supported AI environments the right guidance and workflows for working with datrix. The goal is simple: make powerful external AI workflows feel natural without making users think about the plumbing.
Shared context in
Connected AI environments can reason from the current state of the matrix: what matters, what’s being compared, what changed, where uncertainty remains, and why.
Structured contribution back
AI conversations can contribute useful decision input back into the matrix, where it becomes part of the same structured workspace.
Less re-explaining
Move between connected environments without rebuilding the decision from scratch every time.
Designed for the AI tools people already use.
datrix extensions and MCP workflows are intended to support familiar AI environments and make decision capture, refinement, and structured contribution easier to use.
AI input stays transparent.
Every matrix element can carry provenance details: who or what contributed it, whether it was explicitly set, inferred by an agent, or extracted from a source, plus the rationale and citations behind it.
AI-contributed content also includes confidence scores, so people can judge how much trust to place in a suggestion. Users can edit, correct, extend, revert, or mark AI-generated content as approved as the decision develops.
Provenance.Track author, attribution type, rationale, and source citations for decision inputs.
Confidence scores.See confidence signals on AI-generated contributions so uncertainty is visible instead of hidden.
Approval status.Mark AI-generated content as approved when it has been reviewed.
Timeline and snapshots.Use the audit trail and snapshots to understand how the matrix changed and revisit earlier states when needed.
Connect AI to the decision, not just the conversation.
Start in datrix, work in connected AI environments, or move between both. The matrix keeps the decision context connected.
Free forever for individuals · 3 decisions/month · No credit card required