Caelveris is building a modular analysis and interpretation platform that transforms WGS and WES data into traceable evidence, prioritized candidates and structured outputs designed for expert review.
In development. Intended for professional and research workflows. Caelveris does not independently provide a clinical diagnosis and is not presented as a clinically validated medical device.
The public website is the first layer. The planned authenticated portal will separate laboratory operations, expert review and administration so each role sees the data and controls needed for its responsibility. These panels are a product-direction preview—not a live clinical system.
Planned capabilities include authorized case intake, controlled upload, run state, QC review, artifact inventory and delivery tracking. Large genomic files will use dedicated object storage and signed transfers rather than the application database.
The expert view is planned around variants, inheritance, phenotype fit, evidence provenance and review notes. Ranking supports navigation; it does not replace classification or specialist judgment.
The governance layer is planned for role assignment, organization boundaries, audit events, retention rules, evidence bundle versions, licensing metadata and operational reporting.
The platform is designed as a reusable analysis core rather than a single FASTQ-to-VCF script. It connects reproducible primary analysis with evidence normalization, phenotype-aware reasoning, candidate prioritization, reporting and auditability. Components remain separable so laboratories can adopt the layer they need and retain control of expert interpretation.
Quality control, GRCh38 alignment, alignment metrics, DeepVariant calling, normalization and VEP annotation organized as modular, testable processes.
Variant, population, gene–disease, phenotype and disease-ontology sources are integrated without collapsing distinct evidence types into misleading binary conclusions.
Candidate ranking, inheritance context, evidence summaries and provenance are delivered as reviewable structured data before any narrative layer is generated.
Each stage is expected to emit explicit artifacts, quality metrics and machine-readable provenance. The long-term objective is to make reruns, comparison, verification and expert sign-off practical across local, cloud and HPC execution environments.
Caelveris is designed around the idea that each evidence source answers a different question. Absence from one source is not automatically negative evidence, and gene-level evidence is not treated as proof of variant pathogenicity.
Candidate prioritization and ACMG/AMP clinical classification are treated as separate workflows with separate outputs and responsibilities.
PanelApp supports panel relevance; GenCC supports gene–disease validity; HPO provides phenotype context; MONDO supports disease normalization; ClinVar and gnomAD answer different variant-level questions.
Missing evidence is represented explicitly rather than silently converted into a negative conclusion.
Any future NLP layer will explain deterministic outputs. It will not independently decide which finding is clinically significant.
Clinical and genetic data require more than computational accuracy. The product roadmap includes role-based access control, encryption, audit logging, retention and deletion policies, licensing controls, data-processing agreements and intended-use governance before real patient data is accepted.
Source name, release, retrieval date, license context and transformation history are intended to accompany evidence records and reports.
Independent technical benchmarking is planned with GIAB HG002 after the first production VCF is frozen, reducing the risk of tuning development against the truth set.
The initial commercial direction is B2B analysis and decision support for authorized organizations—not direct-to-consumer autonomous diagnosis.
The answers below describe the intended direction of the product while it is under development. Final capabilities, validated use and contractual terms will be defined before commercial deployment.
No. The platform is intended to support qualified professionals with structured analysis and evidence. It does not independently diagnose disease or replace clinical responsibility.
No. Ranking helps experts navigate potentially relevant findings. Clinical classification is a separate controlled process requiring explicit criteria, evidence evaluation and professional sign-off.
The production design will keep large genomic objects outside the transactional SQL database, using encrypted object storage, controlled transfers, tenant separation, retention policies and audited access.
Early conversations are welcome. Pilot work will be limited to clearly defined scope, data governance, technical validation and intended-use boundaries.
Caelveris is seeking conversations with laboratories, healthcare professionals, bioinformatics teams and research organizations interested in modular genomics analysis, evidence integration or pilot validation.