Free cross-vendor NGS unification for pathologists.
Upload reports from any major lab vendor. Get unified, citation-backed summaries. No paywall on usage. No subscription. We do not sell user data to pharma.
Beta is invitation-based. Closed beta opens Q4 2026; public beta Q1 2027.
Unified summary
Synthetic case- VariantEGFR p.Leu858Arg
- TierOncoKB Level 1 · FDA-approved
- TherapyOsimertinib (FLAURA)
- ContraindicationPD-1/PD-L1 monotherapy in EGFR+ NSCLC
- TrialNCT04988295 · MARIPOSA-2
Every claim links back to OncoKB, FDA label, or ClinicalTrials.gov on the source's own site.
The problem
Five vendors. Five layouts. Same genomics.
On a typical week, a working pathologist or oncology fellow opens NGS reports from Foundation Medicine, Tempus, Caris, Guardant, and Natera. Each clinical report runs 10 to 30 pages, with technical appendices that can run longer. Each follows the lab's own layout. Each labels biomarkers a little differently. Each puts the actionable variants somewhere different on the page.
The clinical content overlaps significantly across vendors. The presentation does not. EGFR L858R is EGFR L858R wherever it shows up. The variant nomenclature, the evidence levels, the drug-gene match logic do not change between Foundation and Tempus. What changes is everything around the variant: the page layout, the order of sections, the way “Tier I” is shown versus “Level 1,” the color coding of contraindications, the location of the trial-match block.
So clinicians spend mental cycles on translation. Reading three vendor reports for a complex case takes longer than it should because the brain has to context-switch between layouts before reasoning about the actual clinical content. The high-cognitive-load part of this work is not the genomics. It is the formatting.
Engine 4 fixes the formatting. Upload the PDF, get a unified summary in the same structure regardless of which lab it came from. Every claim is cited back to the source report, the underlying knowledge base, or the FDA label. No fabricated citations. No invented drug names. No “this might be relevant” hand-waving.
How it works
Upload the PDF. Get a unified summary back.
The pipeline behind it is the same one that powers the UNMIRI NGS Interpretation API, but the output here is rendered for a clinician's eyes rather than for downstream software ingestion.
Variants
Normalized to HGVS notation with transcript references, regardless of how the source vendor formatted them. EGFR p.Leu858Arg is shown the same way whether the report came from Foundation, Tempus, or Caris.
Biomarkers
TMB, MSI, HRD, and PD-L1 displayed in a consistent format with the lab-reported value, the test method (IHC clone for PD-L1, panel size for TMB), and the clinical interpretation.
Therapy options
Drug-gene matches with OncoKB evidence levels and FDA approval status. Same Level 1 designation for the same drug-variant pair regardless of which lab originally reported it.
Contraindications
Drug-class flags from openFDA labels. The PD-1/PD-L1 inhibitor flag for EGFR-mutant NSCLC fires the same way whether the variant call came from a tissue or liquid biopsy report.
Trial matches
ClinicalTrials.gov matches with NCT IDs, eligibility text, and recruiting status, all linkable back to the source registry.
Lab-specific addenda
If a vendor included an interpretive note unique to their workflow, the summary preserves it as a quoted passage with the lab's name attached, rather than absorbing it into the unified output.
The tool is honest about uncertainty. If a variant has no graph match, the summary says so rather than guessing. If an LLM contributed to extraction (which happens for unusual report layouts), the affected fields are flagged. If a citation is not available, no citation is invented.
Supported vendors at beta launch: Foundation Medicine, Tempus, Caris, Guardant, Natera, NeoGenomics, Strata Oncology, Personalis, OmniSeq. Other formats are added as user demand justifies the parser work.
Why it's free
The freeness is structural, not a growth tactic.
The engine itself (parsers, knowledge graph, deterministic rendering) is shared across all four UNMIRI products. The marginal cost of running it for a pathologist is small. The strategic value of having practicing clinicians use it is large: each parsing edge case clinicians flag improves the engine that the paid API products run on.
Monetization at scale will come from pharma educational sponsorship, beginning once the user base passes a credible threshold. The internal target is 1,500 or more active monthly users, with a late 2027 timeline. Sponsorship is on educational content adjacent to the tool (landscape reviews, KOL panels, congress recaps), clearly disclosed and labeled. The summary output itself is never paid placement and never promotes a specific drug.
UNMIRI does not sell user data to pharma. We do not aggregate de-identified usage and resell it. The user base is the asset; selling access to that asset directly would burn the trust that makes the asset valuable in the first place.
What's the catch
There isn't one.
The tool is free for individual clinical use, the citations are real, the data is not sold, and the freemium-to-paywall trap that has soured pathologists on consumer-grade clinical tools does not apply here.
Two honest caveats. First, we ask for a work email at signup, institutional rather than personal, so we can verify clinical use and so the field force at our pharma sponsors does not end up surveilling the user base via signup data. Second, the tool is not a CAP/CLIA-validated diagnostic. Output is a decision-support aid for clinicians who already have the underlying validated lab reports in hand. Don't paste a summary into a sign-out without the source report's lab director cosignature in the loop.
Compliance posture for the underlying platform (subprocessor list, BAA status, region pinning) is at /security. Engine 4 is scoped to non-PHI clinical use by default; institutional deployments that want PHI scope follow the API-product path on Engines 1 and 2.
Roadmap
Three stages.
- 01
Closed beta · Q4 2026
Selected pathologists, oncology fellows, and genetic counselors from the clinical advisor network and from beta signups land here first. Closed-beta users get direct access to the engineering team for parser feedback and edge-case escalation. The intent is to debug the parser stack against real-world report variability before the gates open more broadly.
- 02
Public beta · Q1 2027
Self-serve signup opens. Anyone with an institutional email can create an account and start uploading. Vendor support at this stage covers the major US labs (Foundation, Tempus, Caris, Guardant, Natera, NeoGenomics, Strata, Personalis, OmniSeq). Less common vendor formats are added based on user demand.
- 03
General availability · Q2 to Q3 2027
The product graduates from beta with documented uptime, latency, and accuracy targets, and stays free for individual clinical use indefinitely. Sponsorship monetization begins late 2027, once the user base passes the 1,500+ active monthly user threshold that makes educational sponsorship economics work for pharma counterparties.
The roadmap is honest about its dependencies. The biggest variable is parser coverage stability across vendor format changes. If a major vendor reformats during beta, that pushes the roadmap; we will not ship a tool that works inconsistently across last month's and this month's report layouts. If the parser stack stays stable, the timeline holds.
Who's building this
Software engineers, with clinical advisors being recruited.
Engine 4 is part of UNMIRI, a precision oncology infrastructure company founded by Umair Khan, a software architect with 14+ years of engineering experience. UNMIRI was started after a caregiving experience exposed how fragmented the precision oncology software ecosystem is for clinicians who interact with it daily. The full founder story is at /about.
Clinical accuracy is verified through ongoing recruitment of board-certified pathologist advisors. Public introductions are added once each engagement is formalized and the advisor approves being named. The closed-beta cohort overlaps with this recruitment process, so early beta users have a direct line to both the engineering team and the clinical advisors as they come on.
Sample output
Best way to know if this is useful is to see it.
We've built a fully rendered sample for a synthetic NSCLC case with EGFR L858R, TP53 R175H, and PD-L1 <1%, including the contraindication flag for checkpoint-inhibitor monotherapy in EGFR-mutant disease and a pre-matched MARIPOSA-2 trial entry for the resistance pathway. Same output structure the beta tool produces.
View the sample reportBeta signup
Join the beta.
Invitation-based for now. Sign up here and we'll send an invitation when your queue position is up, with the closed-beta cohort first and the public-beta cohort to follow.