Decoding biology with binary.

One report that reads every layer of a person’s biology together, from blood chemistry to the genome, and says what to do next.

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The data already exists.

Reports from different labs, formats and years. Never read together.

Biochemical analysis

Blood chemistry, from glucose to liver and kidney markers.

Immunology

How the immune system is primed and responding.

Inflammaging

Low-grade inflammation that builds with age.

Gut microbiome

Trillions of microbes shaping metabolism and immunity.

Metabolomics

Small molecules that show metabolism in real time.

Genomics

The full genetic code, including inherited risk.

Molecular genetics

Targeted variants in the genes that matter most.

Transcriptomics

Which genes are switched on, and how strongly.

Nutrigenomics

How genes shape the response to food.

Proteomics

The proteins that carry out the body’s work.

Pharmacogenomics

How genes change the response to medicines.

Checked against the latest research.

Every finding is read against current peer-reviewed literature. The papers passing you are real.

Every layer, read as one.

Five answers. Every line cited.

What you’re going through

Early metabolic strain and low-grade inflammation, while routine blood values stay in range.123

Your risk profile

Type 2 diabetes: above average. Cardiovascular disease: average.456

Actionable points

Fibre-rich diet, 150+ minutes of activity a week, recheck hs-CRP in 6 months.789

Which consultants to see

Endocrinologist and clinical nutritionist.47

What to monitor every year

HbA1c, hs-CRP, proteomic age and gut microbial diversity.21011

References

  1. Inflammaging: a new immune-metabolic viewpoint for age-related diseasesFranceschi et al. Nat Rev Endocrinol, 2018
  2. Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populationsArgentieri et al. Nat Med, 2024
  3. Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomesMei et al. Nat Med, 2024
  4. Genetic drivers of heterogeneity in type 2 diabetes pathophysiologySuzuki et al. Nature, 2024
  5. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutationsKhera et al. Nat Genet, 2018
  6. Mapping the plasma metabolome to human health and disease in 274,241 adultsYou et al. Nat Metab, 2025
  7. Cardiometabolic benefits of a non-industrialized-type diet are linked to gut microbiome modulationLi et al. Cell, 2025
  8. World Health Organization 2020 guidelines on physical activity and sedentary behaviourBull et al. Br J Sports Med, 2020
  9. Antiinflammatory therapy with canakinumab for atherosclerotic diseaseRidker et al. NEJM, 2017
  10. Plasma proteomics links brain and immune system aging with healthspan and longevityOh et al. Nat Med, 2025
  11. A core microbiome signature as an indicator of healthWu et al. Cell, 2024

Illustrative sample

Reviewed and signed
by a clinician.

Before it reaches a patient.

Any lab. Any format.
Your brand.

PDF reportsVCFFASTQCSV

Your Hospital

Jivana AI

Jivana Synthesis

Unified Biological Intelligence

Request a sample report

Today a person is three documents stapled together.

A questionnaire, a blood report and a genomic test, each interpreted on its own. The staple is the only integration. The data already exists. It has never been read together.

What most reports integrate

  1. Questionnaire and history
  2. Blood report
  3. Genomic test

One PDF. Three separate verdicts.

What Jivana reads as one dataset

  1. Biochemical analysis
  2. Immunology
  3. Inflammaging
  4. Gut microbiome
  5. Metabolomics
  6. Genomics
  7. Molecular genetics
  8. Transcriptomics
  9. Nutrigenomics
  10. Proteomics
  11. Pharmacogenomics

Eleven layers. One report. Five answers. Every line sourced.

One report. Five answers.

Every Jivana report has the same five sections, written for the person, and reviewed and signed by a clinician before it reaches them.

Jivana AI Jivana Synthesis report
Prepared for
Ananya Raghunathan, 42, F, 164 cm, 71 kg
Inputs
Eleven layers, from reports and raw files she already had: whole genome, RNA-seq, proteomics, gut microbiome, metabolomics, blood chemistry, history
Sample
JV-2026-0412

Illustrative sample. Not a real patient. Not medical advice.

1. What you are going through

Your fasting glucose is normal, but your transcriptome shows raised expression across insulin-signalling pathways, and your proteomic inflammaging score sits in the top quarter for your age.1,2 Your gut microbiome is low in butyrate producers, which is consistent with both findings.3

Taken together: you are metabolically healthy today, with three independent layers pointing the same way. This is early, and it is the kind of early that responds to change.

  1. Published transcriptomic signatures of insulin resistance.
  2. Peer-reviewed inflammaging proteomic clocks.
  3. Butyrate-producer abundance and metabolic outcomes, cohort studies.
  4. Polygenic and clinical risk models, age-matched population cohorts.
Reviewed and signed
Clinician
Date

Every line traces back to the literature.

The model reads all eleven layers against published evidence and current clinical guidelines. It writes nothing it cannot cite.

Eleven data layers flow into the Jivana model, which reads the literature and writes the five report sections. Biochemical analysis Immunology Inflammaging Gut microbiome Metabolomics Genomics Molecular genetics Transcriptomics Nutrigenomics Proteomics Pharmacogenomics Jivana model reads the published literature and current guidelines What you are going through Your risk profile Actionable points Which consultants to see What to monitor every year

Cited

Every statement in the report carries the source it came from. If there is no source, the sentence is not written.

Cohort-honest

Every probability names the population it was measured in, next to your own number, so you can see the distance.

Signable

Every threshold follows a current clinical guideline. A clinician can review the report, edit it and put their name to it.

  1. Raw reads
  2. Sequencing
  3. FASTQ
  4. Quality control
  5. Alignment
  6. Variant calling
  7. Annotation
  8. Interpretation
  9. Signed report

From FASTQ to a signature.

The Genomics Suite packages every analysis we run, from a single PCR read to a whole genome, into one report a clinician can sign. This is the pipeline, end to end. In development.

Inputs: PCR, Sanger, targeted NGS, whole genome, RNA-seq.

Sequencing by synthesis.

On the flow cell, millions of DNA clusters light up one base per cycle and are imaged in four colours. Whatever the assay, this is where letters become data.

Illumina-class short reads; long reads and Sanger traces enter the same pipeline.

FASTQ. Every letter with a confidence.

Tens of millions of reads leave the sequencer as text, each base tagged with the probability that it is wrong. That probability is the first thing we read.

Phred quality, adapter content, duplication rate.

Quality control.

Reads that cannot be trusted never reach the genome. Adapters are trimmed, low-quality tails cut, contamination flagged and coverage checked before anything is called.

Run-level and sample-level metrics on every dataset.

Alignment.

Each read finds its address on the reference genome and stacks into coverage. Duplicates are marked and base qualities recalibrated.

GRCh38 reference, duplicate marking, recalibration.

Variant calling.

Where the reads disagree with the reference, consistently and confidently, a variant is called: single letters, insertions, deletions and larger structural changes.

SNVs, indels, copy number and structural variants.

Annotation.

Every variant is written against the literature: how common it is in populations like yours, what the clinical databases say, and what it does to the drugs you take.

gnomAD, ClinVar, ACMG criteria, CPIC guidelines.

Interpretation.

A variant is never read alone. It is read against every other layer of the person’s data, so the report says what it means for this body, now.

Illustrative reading: the genotype, low CYP2C19 expression and a current clopidogrel prescription point the same way. Recommendation: an alternative antiplatelet, reviewed with cardiology.

A report a clinician can sign.

Findings, evidence and recommendations in a document your doctor reviews, edits and puts their name to.

Request a sample report

CYP2C19*2 rs4244285 · c.681G>A · heterozygousIllustrative variant 1.2 kb deletion CYP2C19*2 rs4244285 · heterozygous Whole genome CYP2C19 *1/*2 carrier Transcriptome CYP2C19 expression low Inflammaging proteomics hs-CRP 2.4 mg/L, raised Gut microbiome no interacting taxa flagged Blood parameters platelets normal History on clopidogrel since 2025

Built for the people who have to act on it.

For clinicians

Receive one synthesis instead of a stack of PDFs. Review it, edit any line, and sign it. Nothing reaches a patient until you have.

For people who already have data

A genome from one company, a blood panel from another, a microbiome kit from a third. Send them in. We read them together.

For labs and hospitals

Any lab. Any format. Your brand. PDF reports, VCF, FASTQ or CSV go in; a white-labelled report your clinicians sign comes out. We read results. We do not run tests.

Request a sample report.

Tell us which data you already have. We will reply with a sample report built the way yours would be.

We reply from a named person, not a list.

Which data do you already have?

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