Run systematic reviews in days.
Keep them current.
End-to-end meta-analysis with team roles and two-person sign-off, automated Cochrane RoB 2.0 risk-of-bias and GRADE certainty, and Living reviews that flag the day the evidence changes. Ship reviews in days. Keep them defensible for years.
Free tier runs the full pipeline on one real review, end to end.
synthesis teams
Systematic reviews are essential—and painfully slow.
A single review can take 6–9 months, involve hundreds of references, and require stitching together a patchwork of database portals, screening tools, spreadsheets, and ad-hoc statistical scripts.
Most of that time is spent on repetitive work: configuring search strategies, screening thousands of abstracts, extracting data from dense PDFs, and re-running statistical models whenever studies change.
Axelium replaces the patchwork with a single, auditable platform—so you can focus on the science, not the plumbing.
The deadlines for evidence are getting shorter.
Two regulations now in force compress the window for credible evidence synthesis to weeks, not quarters — and synthesis is the slowest link in the chain.
A standardised clinical evidence dossier is now required for every new oncology drug in the EU — and the window is 100 days.
If you don't have a defensible meta-analysis ready, you don't have a JCA submission.
US pharma must defend value with HEOR evidence at speed, against a Medicare price-setting calendar.
Slow evidence becomes a pricing concession.
From PICO to publication.
Chapters
11 sections · 2:58When evidence arrives late, decisions get made anyway.
These are the failures that make HTA reviewers, guideline committees, and HEOR teams demand defensible synthesis on time — not just defensible synthesis.
“Benefits ‘too small to justify the cost to the NHS’”
NICE rejected lecanemab and donanemab — the first disease-modifying Alzheimer’s drugs in a generation — denying access to an estimated 70,000+ early-stage patients in England and Wales.
“£424M spent stockpiling a drug that didn’t reduce hospitalisations”
Cochrane’s review of 160,000+ pages of clinical study reports — extracted from the manufacturer after a 4.5-year transparency battle — concluded Tamiflu didn’t reduce flu hospitalisations or pneumonia. The audit was true; the stockpile was already paid for.
Slow evidence is expensive. Fast evidence is defensible. That’s the trade we built Axelium to break.
A 7-RCT meta-analysis, reproduced in 2 days by a single analyst.
AI hallucinates numbers. A pooled effect estimate is only worth its weight if it’s reproducible and auditable — the moment an LLM is in the calculation path, no HTA reviewer at NICE, IQWiG, or CADTH will accept it. So we don’t put one there. AI handles the language work; deterministic code handles every number, judgement, and certainty rating.
We independently reproduced a peer-reviewed meta-analysis of neoadjuvant immunotherapy in resectable NSCLC — originally a five-author project. One analyst using Axelium completed it end-to-end: screening, extraction, pooled analysis, sensitivity, publication bias, subgroups, meta-regression, and GRADE.
Why Axelium instead of spreadsheets, point tools, or generic AI?
End-to-end workflow built for systematic reviews, with AI-assisted extraction, deterministic stats, and full traceability — benchmarked against published research.
| Capability | Manual workflow | Screening-only tools | Generic AI | Axelium |
|---|---|---|---|---|
| End-to-end in one platform | No | No | No | Yes |
| Search & database ingestion | Partial | Yes | Partial | Yes |
| Search across 8+ databases | No | Partial | No | Yes |
| Citation chasing (Semantic Scholar) | No | No | No | Yes |
| AI-assisted abstract screening | No | Yes | Partial | Yes |
| Full-text PDF extraction | No | No | Partial | Yes |
| Deterministic statistical models | Partial | No | No | Yes |
| Risk-of-bias appraisal (RoB 2) | Partial | No | No | Yes |
| GRADE certainty of evidence | Partial | No | No | Yes |
| Source-linked audit trail | No | Partial | No | Yes |
| PRISMA-style reporting | Partial | No | No | Yes |
| Living review automation | No | No | No | Yes |
| Role-based access (admin / lead / reviewer / viewer) | No | Partial | No | Yes |
| Two-person protocol sign-off | No | No | No | Yes |
Faster reviews. Fully defensible.
Accelerate your workflow.
- Configure PICO or PEO questions in minutes instead of days.
- Automate search planning, screening, data extraction, and statistics.
- Cut project timelines from months to days, with focused updates in hours.
Stay methodologically sound.
- Structured PICO and PEO frameworks keep every question methodologically sound from the start.
- Built-in support for common effect measures (RR, OR, HR, MD, SMD, proportions).
- Cochrane RoB 2.0 risk-of-bias appraisal and auto-derived GRADE certainty ratings, plus PRISMA-style flow diagrams—methodological rigor built into every stage.
Trust every number.
- AI extracts only structured values (events, totals, means, SDs) into deterministic analysis code—never runs stats directly.
- Every data point is tied to its source snippet in the original PDF for full traceability.
- Trace any pooled estimate back to the exact studies and effect sizes behind it—a complete audit trail across searching, screening, extraction, and modeling.
Built for review teams.
- Four roles enforced server-side—admins, lead reviewers, reviewers, and viewers—so every mutation is gated to the right person.
- Comments and @mentions on every study, decision, and outcome, with a unified inbox plus optional email digest.
- Two-person protocol sign-off across five milestones with immutable version snapshots—Cochrane- and HTA-grade governance built in.
Sign-off, comments, and an audit trail—out of the box.
Solo researcher, three-person Cochrane group, or a CRO running twenty parallel reviews—the team layer is built into the same workflow you already use.
Admin, lead reviewer, reviewer, and viewer roles control who can edit what across every analysis.
Anchor threads on analyses, studies, decisions, and outcomes. Mentions land in one inbox.
One bell-icon inbox for mentions, sign-offs, and the living-review digest — per-user cadence.
Five milestones from protocol-locked to manuscript-locked — the requester is never the approver.
Split studies across reviewers, pick single or dual review, resolve conflicts with a field-by-field diff.
Every sign-off freezes an immutable snapshot. Diff any two versions; every effect traces to a PDF snippet.
Pick the next step that matches your work.
HTA / Institutions
Per-cycle provenance and frozen rollup snapshots give the audit trail submissions need.
See the deep pageClinicians / Guideline committees
Living reviews flag the day a pooled estimate moves, so recommendations stay aligned with the evidence.
See the deep pageCROs / HEOR consultancies
Locked recipes and two-person sign-off make the comparator analysis defensible client after client.
See the deep pageResearchers / Solo PhDs
Free tier runs the full pipeline on one real review, end to end.
See the deep pageYour review stays current — and tells you when it matters.
Turn any completed review into a living review. On a daily, weekly, or monthly schedule, Axelium re-runs the whole pipeline — search, screening, extraction, risk of bias — surfaces new studies in a review inbox for triage, and re-pools the meta-analysis so your evidence base never goes stale.
Instead of restarting a review from scratch every few years, you maintain one continuously updated evidence base — and hear about it the moment a pooled result moves.
See how living reviews workA systematic review begun in 2016 synthesised more than 700 papers on Zika virus. By the time it published in January 2017, a further 1,400 had appeared — out of date before it arrived.
Living reviews exist so that never happens to yours.
Programs that supported the Axelium build.
Microsoft for Startups
NVIDIA Inception ProgramReady to automate your next review?
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