A single systematic literature review takes an average of 1.72 years and costs approximately $141,000 (Borah et al., 2017). The ten largest pharma companies each publish around 119 per year. But for every SLR you commission, there are dozens of internal evidence questions — portfolio decisions, business development & licensing (BD&L) due diligence, medical affairs queries — that never justify the spend. The evidence base decays the day after completion, and every month spent waiting for evidence is a month patients wait for access decisions.
What if you could run a full systematic review overnight, at a fraction of the cost — and get evidence to decision-makers while it still matters?
Every Evidensity engagement is delivered as a single, self-contained interactive bundle — the same format as the two worked examples below. It opens in any browser with nothing to install: browse the full corpus, trace every claim to its verbatim source quote, inspect the per-paper credibility scorecard, and explore the forest plots and pooled estimates. Not a static summary — the complete, auditable analysis, in your hands.
Two live examples — each generated from a single plain-text question, then checked against the published landmark meta-analysis of the same trials.
A worked example — and a test of the method against a known answer. Generated from a single plain-text question, then checked against the published landmark meta-analysis of the same trials.
Explore the interactive evidence bundle →
Browse every paper, trace each claim to its source quote, inspect the credibility scorecard and forest plots — the full analysis, not a static summary.
Across the eight pivotal GLP-1 cardiovascular outcome trials, our pooled estimates — produced from the plain-text question above, every figure traceable to its source — reproduce the published landmark meta-analysis (Sattar et al., Lancet Diabetes & Endocrinology, 2021) closely across every outcome:
| Outcome (pooled risk ratio) | Evidensity | Sattar 2021 |
|---|---|---|
| 3-point MACE | 0.86 (0.82–0.90) | 0.86 (0.80–0.93) |
| Cardiovascular death | 0.87 (0.81–0.94) | 0.87 (0.80–0.94) |
| All-cause mortality | 0.89 (0.84–0.94) | 0.88 (0.82–0.94) |
| Fatal / non-fatal stroke | 0.86 (0.76–0.99) | 0.83 (0.76–0.92) |
| Fatal / non-fatal myocardial infarction | 0.86 (0.74–1.01) | 0.90 (0.83–0.98) |
| Heart-failure hospitalisation | 0.92 (0.82–1.03) | 0.89 (0.82–0.98) |
The two columns are independent: ours generated by the pipeline from the question above; Sattar's transcribed from the open-access publication. The headline MACE estimate matches to two significant figures with the same low heterogeneity (I² ≈ 1%); every outcome agrees in direction and magnitude. The wider intervals on MI and stroke reflect fewer poolable arms, not a divergent result.
A second worked example — generated from a single plain-text question, then benchmarked against the landmark meta-analysis of the pivotal trials (Ruff et al., Lancet, 2014).
On the outcome that defines the class, the pooled estimate across the pivotal randomised trials is 0.86 [0.79–0.94] with zero between-trial heterogeneity (I² = 0%) — a 14% relative reduction in stroke or systemic embolism, produced from the plain-text question above with every estimate traceable to its source. This reproduces the primary efficacy finding of the landmark meta-analysis (Ruff et al., 2014: risk ratio 0.81 [0.73–0.91]).
20 specialised agents · 10+ academic databases · PRISMA-aligned methodology
Once the evidence base is built, follow-up questions come back almost instantly. Need the same corpus reframed for a payer committee? A different therapeutic comparison? A sub-population breakdown? Same evidence, new analysis — overnight.
Output is tailored to audience: Cochrane-style with GRADE certainty ratings for HTA dossiers, executive briefs for leadership, or data-forward insight reports for internal teams.
Scope the evidence landscape overnight before committing a six-figure engagement or entering a reimbursement negotiation. Know whether the evidence base supports your position before you walk into the room.
Portfolio decisions, BD&L due diligence, medical affairs queries, competitive intelligence. The questions that matter but never justify a formal SLR.
Re-run the same search criteria quarterly. Flag new papers that would change conclusions. Keep your evidence base current between formal reviews.
This doesn't replace regulatory-submission SLRs. It handles everything else.
| General AI Research | Academic AI Tools | Traditional SLR | Evidensity | |
|---|---|---|---|---|
| Search | Unstructured web | Academic databases | Systematic, manual | 10+ academic databases, systematic criteria |
| Extraction | Prose summary | Abstract-level | Full-text, manual | Full-text structured schemas |
| Grounding | Often hallucinated | Citation-level | Gold standard | Verbatim quote + section |
| Quantitative Pooling | None | None | Separate biostatistician | REML random-effects, forest plots |
| Credibility | None | None | Manual (RoB-2, GRADE) | Multi-dimensional per paper |
| Timeline | Minutes | Minutes | 1.72 years avg | Hours |
| Cost | Low | Low | ~$141,000 per review | Enquire |
| Output | Chat response | Paper list | Publication-ready report | Interactive evidence bundle + report |
Strong Partial Limited or none
If faster evidence would have changed any access timeline on your watch, I'd welcome the chance to show this to the people still fighting that fight.