Convert Existing Biomarker Data Into Revenue Capacity

OmicsLayer™ turns disorganized multimodal biomarker data into reusable pharma-ready workflows for cohort selection, responder prediction, patient analysis, and evidence packages.

Integrate now Clients

More revenue from pharma partners

Sell data-backed biomarker analysis, patient cohort selection, trial feasibility evidence, and responder/non-responder stratification packages to pharma partners.

+EUR 150k-500k/year

from 1-2 additional or expanded pharma work packages

Talk to Solutions Architect

Risk reduction / audit readiness

Reduced sponsor-review friction and lower risk of rework during pharma/FDA-facing discussions.

10-25% improvement in cohort-screening efficiency

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Higher project margin

Reduce repeated manual preparation by data science and modality teams; reuse queries, exports, metadata links, and evidence templates.

EUR 40k-120k/ year

in avoided internal effort at a typical fully loaded scientific/data cost

Talk to Solutions Archite

Faster sponsor response

Answer sponsor feasibility questions using searchable cohorts, biomarkers, files, analysis outputs, and provenance instead of manual data assembly.

40-70% faster turnaround

e.g. reduce 5-10 working days to 2-4 days per request

Talk to Solutions Architect

Real implementations of OmicsLayer™ – adapted to each client’s data, infrastructure, and operational needs.

Based on our clients’ experience, OmicsLayer™ saves around €40k–120k/year in avoided manual internal effort.

All Case Studies

Trusted by Industry Leaders:

Secure on-prem/private cloud installation process

Discovery & data audit

1–2 weeks

Workshops with scientific, data, IT, and compliance teams. We define systems, datasets, permissions, and the first high-value use case.

Data & architecture mapping

2–3 weeks

We map data flows, metadata, access logic, pipeline dependencies, and integration points.

Build & integration

4–8 weeks

We connect priority systems, build ingestion/QC workflows, configure the semantic layer, and prepare the AI query interface.

Pilot validation

2–4 weeks

We test the first use case on real data, validate answer quality, review traceability, and tune workflows with domain experts

Scale & optimization

Ongoing

Add new systems, cohorts, data modalities, dashboards, partner workspaces, and additional AI-assisted workflows.

Discovery & data audit

1–2 weeks

Workshops with scientific, data, IT, and compliance teams. We define systems, datasets, permissions, and the first high-value use case.

Data & architecture mapping

2–3 weeks

We map data flows, metadata, access logic, pipeline dependencies, and integration points.

Build & integration

4–8 weeks

We connect priority systems, build ingestion/QC workflows, configure the semantic layer, and prepare the AI query interface.

Pilot validation

2–4 weeks

We test the first use case on real data, validate answer quality, review traceability, and tune workflows with domain experts

Scale & optimization

Ongoing

Add new systems, cohorts, data modalities, dashboards, partner workspaces, and additional AI-assisted workflows.

Who it is for

Built for companies where biomedical data already exists

1 Molecular diagnostics companies

Connect variant, assay, phenotype, and evidence data into faster interpretation workflows.

2 Modality-specific design modules

Unify multi-omics and clinical metadata to support discovery, validation, and translational proof-of-concept.

3 Clinical genomics labs

Reduce manual interpretation and reporting burden with traceable, reviewable AI-assisted workflows.

4 Biobanks and research hospitals

Make biospecimen, cohort, omics, and clinical data queryable under strict governance.

5 Pharma translational medicine teams

Move from scattered datasets to reusable biomarker and patient stratification intelligence.

6 CROs and lab networks

Integrate LIMS/ELN, QC, assay outputs, and reporting workflows into one operational intelligence layer.

1 Molecular diagnostics companies

Connect variant, assay, phenotype, and evidence data into faster interpretation workflows.

2 Modality-specific design modules

Unify multi-omics and clinical metadata to support discovery, validation, and translational proof-of-concept.

3 Clinical genomics labs

Reduce manual interpretation and reporting burden with traceable, reviewable AI-assisted workflows.

4 Biobanks and research hospitals

Make biospecimen, cohort, omics, and clinical data queryable under strict governance.

5 Pharma translational medicine teams

Move from scattered datasets to reusable biomarker and patient stratification intelligence.

6 CROs and lab networks

Integrate LIMS/ELN, QC, assay outputs, and reporting workflows into one operational intelligence layer.

Didn’t find exactly what you were looking for?

Tell us what you’re building – we’ll map out the options, evaluate feasibility, and recommend the optimal technical pathway.

Get a consultation

Get in touch with us. 
We’re here to assist you.

Andrew Markov

Delivery Director

Chat in WhatsApp

Convert Existing Biomarker Data Into Revenue Capacity

More revenue from pharma partners

Sell data-backed biomarker analysis, patient cohort selection, trial feasibility evidence, and responder/non-responder stratification packages to pharma partners.

+EUR 150k-500k/year

Risk reduction / audit readiness

Reduced sponsor-review friction and lower risk of rework during pharma/FDA-facing discussions.

Higher project margin

Reduce repeated manual preparation by data science and modality teams; reuse queries, exports, metadata links, and evidence templates.

EUR 40k-120k/ year

Faster sponsor response

Answer sponsor feasibility questions using searchable cohorts, biomarkers, files, analysis outputs, and provenance instead of manual data assembly.

40-70% faster turnaround

Based on our clients’ experience, OmicsLayer™ saves around €40k–120k/year in avoided manual internal effort.

Secure on-prem/private cloud installation process

Discovery & data audit

1–2 weeks

Data & architecture mapping

2–3 weeks

Build & integration

4–8 weeks

Pilot validation

2–4 weeks

Scale & optimization

Ongoing

Discovery & data audit

1–2 weeks

Data & architecture mapping

2–3 weeks

Build & integration

4–8 weeks

Pilot validation

2–4 weeks

Scale & optimization

Ongoing

Built for companies where biomedical data already exists

1 Molecular diagnostics companies

2 Modality-specific design modules

3 Clinical genomics labs

4 Biobanks and research hospitals

5 Pharma translational medicine teams

6 CROs and lab networks

1 Molecular diagnostics companies

2 Modality-specific design modules

3 Clinical genomics labs

4 Biobanks and research hospitals

5 Pharma translational medicine teams

6 CROs and lab networks

Didn’t find exactly what you were looking for?

Get in touch with us. We’re here to assist you.

Andrew Markov

Your AI Idea, Our Technical Precision

Get in touch with us. 
We’re here to assist you.