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Axelium
Existing users
For HTA bodies, payers & national institutions

An audit trail that survives the submission cycle.

Per-cycle provenance and frozen rollup snapshots give the audit trail submissions need — with a deterministic statistical engine a regulator can replay months later from the same inputs.

Read methodology
Deterministic engine · frozen snapshots · full provenance
www.axelium.ai · Forest plotLive
Random-effects pool · primary outcome6 studies
Smith 2024
0.62 [0.41, 0.94]
Chen 2023
0.71 [0.55, 0.92]
Patel 2024
0.83 [0.62, 1.11]
Garcia 2023
0.74 [0.58, 0.95]
Okafor 2024
0.88 [0.71, 1.09]
Müller 2022
1.05 [0.78, 1.41]
Pooled (RE)
0.78 [0.65, 0.93]
← favours interventionfavours control →
study estimate (size ∝ weight)pooled (RE)
RR, log scale
The shift

From disconnected tools to one sanctioned trail.

Same workflow, two states. Flip the switch to see what changes.

Assembled per submission · then set aside Live · re-runs with signed provenance
Each divisionIts own one-off workflowOne sanctioned, living trailsiloedcentral
The evidence baseFrozen at submissionRe-runs on a schedulefrozenliving
Audit trailReassembled every cycleSigned and current each cyclerebuiltprovenance
Static — reassembled each cycleOne auditable trail, kept current
How it works

Regulatory-grade synthesis, replayable on demand.

Build the comparison once, keep it current, and inspect every step — with a signed, replayable record at each stage.

Build

Build the comparison once

  • A systematic review and a network meta-analysis
  • Anchored against every relevant comparator
  • Defensible from the first submission
Keep live

Re-runs on a schedule

  • The whole pipeline re-runs on a cadence
  • Flags when a new trial moves the result
  • A signed record of what changed, each cycle
Inspect

No black box

  • Deterministic statistics — same inputs, same result
  • Every estimate traces to its source study
  • Change an assumption and re-run
Why trust it

A result a committee can reproduce.

Validation

We reproduced a published forest plot — exactly.

In our reproduction case study, Axelium rebuilt a published meta-analysis end to end and matched its pooled estimate. Same inputs, same numbers — because the statistical engine is deterministic.

Comparisons a committee will accept

Pairwise and network meta-analysis, plus population-adjusted indirect comparisons, with certainty grading — every step inspectable.

Network meta-analysisMAICSTCML-NMRGRADE certainty
✦

Give every division one sanctioned, auditable evidence workflow.

Talk to sales
Questions

The details.

Yes. The statistical engine is deterministic — the same inputs produce the same result — and every signed-off stage is frozen as an immutable snapshot.