We produce literature reviews and evidence syntheses for medical-device and life-science teams, using artificial intelligence (AI) under expert oversight. Your team keeps regulatory strategy and final decisions.
Building or funding a high-stakes workflow? Start here.We take on the literature review or state-of-the-art (SOTA) analysis within an agreed scope.
We check source links and cross-document consistency before your experts make the final calls.
Our work includes clinical-evaluation literature modules and research syntheses. We also provide evidence-production capacity for grant proposals under your funding strategy.
Two kinds of judgment run through a SOTA. The line between them is stable, and we keep it.
Search, screening, extraction, references, formatting. Rule-governed and time-intensive. This is where AI under expert supervision gives the most.
We execute against the scope you set.
Evidence narrative, first-pass appraisal, methodology, regulatory structure. Craft calls that have to hold up against clinical evaluation guidance and MDR expectations.
We execute, craft judgment included.
Submission pathway, product-specific clinical interpretation, benefit-risk, equivalence, notified body responses. This needs knowledge and intent no outside partner has.
That judgment stays with your team and your advisors, not with us.
"We make the craft calls our track record is built on. You make the strategic calls rooted in product knowledge and pathway context."
Regulatory strategy and data-protection decisions stay with your responsible teams and advisors.
Before confidential work starts, we agree which tools may process it, where processing may take place and how long data may be retained. Identifiable patient data needs a separately agreed processing arrangement.
We can scope a review of an existing SOTA or clinical-evaluation file for evidence gaps and inconsistencies. Your regulatory experts interpret the findings.
Every AI-assisted step lands in front of a reviewer, and every review decision is recorded in the workflow itself.
Recorded decisions. Accept-or-correct decisions are captured as they happen, not reconstructed later.
Severity grading. Corrections are graded from cosmetic to substantive, so signal is separated from formatting noise.
Per-task tracking. Screening, appraisal, extraction and synthesis are measured separately, never blended into one number.
Defensibility gates. A figure is quoted only after lineage, denominators and adjudication hold up.
We publish no performance number that has not passed those gates. What we can say plainly: we have delivered SOTA reports and clinical-evaluation evidence modules that were submitted to notified bodies, under client regulatory direction, for complex high-risk devices.
From our review records: recorded correction rates during mandatory human review fell sharply from May 2026. Two things changed at once: a new model generation arrived, and we rebuilt our workflow harness around self-correcting loops with adversarial checking. The gain comes from both together.
Recorded correction rate during mandatory human review.
Recorded correction rate during mandatory human review.
Residual unsupported claims across three self-correction passes, checked automatically before human review.
Verification is unchanged: every deliverable passes full human expert review. What dropped is how much correction that review requires, and with it the billable hours a verified document takes. We bill effort, so that saving passes to you. Increased protection against detectable inconsistencies, not guaranteed acceptance.
How we count: denominators are recorded human review decisions; a correction means a reviewer changed the AI-prepared result, and cosmetic edits count as accepted. Grounding figures are residuals from automated source checks that run before human review. Figures are per task type, never blended. One construct, marked corrections in article summaries during human review, held roughly steady at 1.8% to 2.0% under a fuller review protocol, so we do not claim uniform improvement across every task.
Medical device companies with complex portfolios and capacity pressure on senior regulatory, clinical and quality staff.
Regulatory and grant consultancies that need production capacity without hiring. White-label, under your client relationships and strategic direction.
Scale-ups and founders on regulatory pathways with tight capital and tight timelines.
Start with an evidence module. Build and HVDA Academy cover workflow implementation and team learning.
Tell us the document and deadline, without sending confidential files. We will confirm the scope and processing arrangements before work begins.
Email contact@hvdaccel.com