From automated multi-variable scoring algorithms to immutable audit logs. Everything you need to de-risk your most expensive choices.
Define custom criteria and assign weights based on priority. Best for complex, multi-variable evaluations.
Evaluate based on Reach, Impact, Confidence, and Effort. Industry standard for product feature prioritization.
Not every decision needs the same approach. Whether you're evaluating a multi-million dollar vendor contract or deciding which product feature to build next, Axiom provides the right analytical framework for the job.
Create clarity with explict criteria and scores that everyone can understand and agree on.
| Contender | SOC 2 Type II | API Base |
|---|---|---|
| Vendor Alpha | Pass | Pass |
| Startup Beta | Fail | Pass |
Don't waste time debating options that physically cannot meet your baseline requirements. Set binary (Pass/Fail) elimination criteria to immediately disqualify unviable candidates from the scoring matrix.
End the endless 4-hour alignment meetings. Team members can asynchronously propose alternative weights and scores, complete with comments and justifications. Project owners can review these suggestions in isolation and mathematically merge them into the master matrix with a single click.
"I strongly believe we need to bump the UI/UX score up to 8.0 because their new dashboard rollout fixes the navigation complexity."
When a new VP asks why a specific database, vendor, or candidate was chosen two years ago, you shouldn't have to decipher a 300-comment Slack thread. The project timeline acts as an enterprise-grade Architectural Decision Record (ADR), tracking the exact timestamp and user ID for every mutation.
Automate your zero-to-one research opportunities by plugging right into your existing AI workflows. Axiom exposes your decision matrices securely via the Model Context Protocol (MCP), allowing tools like Claude Desktop to evaluate options contextually based on your local documentation.
Crucially, integrating external models doesn't remove the clarity of why a decision has been made. Every MCP proposition leaves a perfect audit trail of where the AI stopped and the human took over—an essential mechanism in a tightening regulatory environment.
Evaluating "Vendor Alpha" via axiom-server
Combine AI-driven data synthesis with human expertise to make faster, unbiased, and completely auditable choices.
Get Started