Bidirectional translation of biomedical knowledge from the early research laboratory, through development and manufacturing to clinical & patient-oriented and population-based research

Program and Project scale, AGILE to WATERFALL and all the different connotations between, project scoping and resource planning, kick-off and project delivery, reporting, finalisation and closing

Master Data Management (MDM), Semantics and Data Enrichment can help maximise data value, trust in data and support the FAIRification of data assets and digital transformation.

Security, authentication, bandwidth, latency, data formats, and automation implications of robotics, automation, sensors, and instruments in the scientific lab

Automated HTE & Manufacturing Batch Release, product specification ingestion to LIMS, our experience covers BPMN, RPA, AI, and many low-code-no-code open-source and proprietary tools & platforms.

As science-centric organizations are looking to use their lab data in a more strategic way, centralizing and standardizing the vast dataflow generated by lab instruments becomes key.

As science-centric organizations are looking to use their data for AI/ML and augmented decision support, centralizing and standardizing the vast data flow generated by lab instruments is critical.

Generating, transforming non-graph data, and graph data storage platforms like GRAKN, NEPTUNE, NEO4J etc. alongside ontology definition & management supports interoperable and reusable data.

Making scientific data Findable, Accessible, Interoperable and Reusable is a journey that needs experienced and expert planning, design, execution and governance.

Industry gurus engage in current state and vision setting, transformation coaching, building change momentum, thought leadership, technology options and innovation via POCs & value demonstrators, industry insights & benchmarking

ERP, Registration, Inventory, ELN, LIMS integration, laboratory instruments & robotics, project planning & lab audit tools, integration layer technology, instrument data standards (ADF, AniML, SiLA etc.), tech blueprints

Data modelling, plumbing, wrangling, transformation, management, and enrichment all support Machine Learning (ML) & artificial intelligence (AI) as need good quality data to work well.

Extracting data from and transforming to alternate formats including graph data structures like RDF, special structures like ADF etc to support loading to data lakes, warehouses for reuse and data FAIRification

"Always on", end-user support, proven scale, quality, processes and knowledge of scientific applications have a measurable impact on end-user efficiency, satisfaction and application downtime.

Architecting, development and managed services on the major providers AWS, AZURE, GOOGLE, on-prem systems integrations, DevOPs and TechOps.

Stakeholder and end-user engagement, requirements and vision capture, change readiness assessment, decision making and consensus building, communication planning and program success monitoring.

Automated assay and screening, batch release, workflow orchestration, BPMN, low-code-no-code process automation (RPA) and robotics platforms.

Ontology definition and governance in commercial and open-source platforms alongside deep domain and technical knowledge to support your FAIR and semantic data journey.