Where bench science meets commercial decisions. Independent advisory for life sciences teams scaling across EMEA and Asia.
Commercial strategy, scientific advisory, and digital infrastructure, shaped to your stack and your context.
Seungeun Lee, PhD · Founder & Principal, Dr. S. Lee Life Sciences
Active build · spots openQuery to quote in a minute.
An agentic AI workspace by a life-sciences scientist, designed around your domain's taxonomy and team's language. From days to minutes, reading from your existing platforms, ready to tune to your stack.
Sense 2026 is one shape. Other scoped projects and partner roles also open.
Find a fitBench-credible scientific depth from wet-lab to bioinformatics, with database architecture and agentic AI workspace design across technical and commercial operations data platforms. Paired with commercial leadership and Europe–Asia operating fluency, engagements support life-science teams scaling across EMEA and Asia. From discovery through alignment to delivery, on one thread, scoped to deliverables. Fractional advisory or project-based, where it fits.
Tap a dot for details · NDA on request
Alongside the consulting practice, three early-stage threads in life sciences: Sense 2026 (an agentic AI workspace for life-science teams), a mentoring program around strategic skillsets, and skill-set services that pair specialized expertise with hands-on delivery in database architecture, agentic AI tooling, and analytics. Open to partners across any of these, especially collaborators who want to combine their own work with mine.
Query to quote in a minute.
Today, a customer's technical question routes through a senior expert and waits two or three days for a reply. Sense is an agentic AI workspace that brings that down to minutes. Agents handle routing, drafting, and source-checking inside a workspace shaped to your team's flows. Your team asks in their native language; replies come as drafts with cited sources from your own documents, in whichever format the moment calls for (technical report, proposal, email reply, and others). Send after a quick review. Designed around your domain's taxonomy, in the words your team actually uses: each reply quietly trains the workspace, so the next colleague gets the answer instantly.
Your data stays on your server, no outbound data. Sense reads from the platforms you already use, no disruption to current operations. Single self-contained install, free to evaluate, with a tailored advisor file backing the workflows that matter. Ready to build into your stack and tune to your team's flows; data architecture and indexing can be designed alongside when needed.
Open to teams ready to explore Sense at their own rhythm. You start with a shared sandbox so your team has time to sit around Sense together, explore at their own pace, and imagine what their workflow could look like inside it. A short feature request form tells us what works for you, and from there we move directly into customised build and deployment alignment, in steps. No separate pilot scope, no upfront commitment. Bring your team's hardest workflow when the time feels right.
R&D or commercial leaders in biotech, genomics, diagnostics, or pharma who'd rather build something with someone than navigate alone. Two paths fit: shape an existing build (Sense 2026 is the active build, currently open for exploring teams and more topics in the pipeline), or co-create something new that complements the practice, around your own thesis about a tool, infrastructure, program, or service in life sciences. Several shapes are possible, from project collaboration to deeper alignment, with hands-on support for partnership development and project structuring along the way.
Open a co-build threadEngineer interested in shipping bounded scope across agentic AI tooling, database architecture and integration, or operations data platform work (LIMS, SAP, Power BI, SharePoint, or others). Comfortable in Python, R, Docker, or Git workflows. ML or AI background welcome but not required; strong craft in pipelines, schemas, system integration, or curation work is equally valuable. Paid contract engagement, collaboration credit. Domain context (biology, NGS, healthcare data) is a plus. First contracts are forming around active workstreams, with more in the pipeline.
Pitch a project scopeUndergrad or grad student in CS, life sciences, or related fields, looking for hands-on experience at the agentic AI and life sciences intersection. Capstone projects, master's thesis topics, summer internships across clinical, environmental, food-safety, or commercial-operations contexts. We define the scope together; you get structured mentorship and a real project to point at. Initial student threads are forming around current projects, with more topics in the pipeline.
Bring a thesis or capstone ideaOver fifteen years between research benches, regulatory bodies, and commercial leadership. Scientific work spans clinical, environmental, and food-safety domains, with peer-reviewed publications and EU Reference Laboratory studies in genomics and microbiology, from wet-lab to bioinformatics and agentic AI tooling. Commercial work covers EMEA market entry, partnerships, pricing, fiscal planning, revenue forecasting, sales direction, and biobank, LIMS, and SAP data architecture under ISO 17025 & ISO 9001, leading multidisciplinary teams across sales, project management, lab, and bioinformatics.
Domain research, ICP definition, and customer development for an early-stage AI SaaS exploration in life sciences. Mapped senior buyer dynamics and market entry mechanisms across R&D and biotech services globally.
EMEA commercialization of scientific innovations. B2B/B2C partnerships, pricing and branding strategy, fiscal planning, revenue forecasting. ISO-compliant systems and LIMS-based documentation. Reporting infrastructure with SAP and Power BI. Multidisciplinary leadership across sales, project management, lab operations, and bioinformatics.
Genomic surveillance of food-borne pathogenic microbes using Illumina and Nanopore sequencing, with Python, R, Docker, and Git workflows. EU Reference Laboratory inter-laboratory studies and proficiency-test evaluations on Shiga toxin–producing and pathogenic E. coli, and Listeria monocytogenes. Biobank database infrastructure on LIMS, RDBMS, and Jupyter.
Curated metadata to refine the GLOBIO-Aquatic Projection model. Applied R for variable analysis, regression modelling, and dataset alignment.
Built a laboratory from the ground up to optimize in vitro and in silico workflows. Refined NGS data with bioinformatics tools to identify environmental pathogens and improve microbial molecular identification.
Molecular and clinical research on diabetes and endocrine disease. Cell culture, differentiation, and drug treatments. Animal handling for in vivo studies. qPCR, bioluminescent assays, immunoblotting, and protein extraction. Cloning workflows including DNA fragment design, insertion, and digestion.
Undergraduate research placement supporting nutrition and health studies. Handled laboratory equipment, chemicals, and animal models for in vivo nutrition research.
Every channel reaches me directly, with replies usually within two working days. Write or speak in any language. A real clue to a problem usually carries across whatever language it arrives in.
More about meWhether for consulting, partnering on the build, mentoring, or just to say hello, you decide where to start.