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.

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Deterministic engine · frozen snapshots · full provenance
Forest plotlive
Random-effects pool · primary outcome6 studies
Smith 20240.62 [0.41, 0.94]
Chen 20230.71 [0.55, 0.92]
Patel 20240.83 [0.62, 1.11]
Garcia 20230.74 [0.58, 0.95]
Okafor 20240.88 [0.71, 1.09]
Müller 20221.05 [0.78, 1.41]
Pooled (RE)0.78 [0.65, 0.93]
study estimatepooled (RE)
RR, log scale
The shift

From disconnected tools to one sanctioned trail.

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

Live · re-runs with signed provenance

Each division
One sanctioned, living trail
central
The evidence base
Re-runs on a schedule
living
Audit trail
Signed and current each cycle
provenance
One 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 meta-analysis: 3 of 4 endpoints within 6%.

One analyst rebuilt a peer-reviewed meta-analysis end to end in 2 days. Pooled estimates for three of four endpoints landed within 6% of the published values, and the case study explains the fourth. The statistics are deterministic: the same inputs always give the same result.

Read the case study

Comparisons a committee will accept

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

  • Network meta-analysis
  • MAIC
  • STC
  • ML-NMR
  • Custom methods
  • GRADE certainty

Give every division one sanctioned, auditable evidence workflow.

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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.