Where bench science meets commercial decisions. Independent practice 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, PRINCE2 7 Practitioner
Founder & Principal, Dr. S. Lee Life Sciences
Drafts arrive fast. The send stays yours.
An agentic AI workspace by a life-sciences scientist. Lives on your server, routes through your team's stages, drafts with cited sources. Tailored to your domain's taxonomy and team's language.
Take a tour See practice areasBench-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
Any of these can take the partnership shape that fits. Explore partnership →
Drafts arrive fast. Your intelligence compounds.
Built for life-sciences workflows where technical and business judgment converge: inquiry-to-quote, R&D technical assessment, and beyond.
Stays put. Pulls in what's worth reading. Protected by design.
Catches pattern, threshold, tone. Seeded by day one.
Drafts arrive with reasons. Audit at a glance. You decide.
Fits your approval flow. Tune as you go. Logbook writes itself.
Onboard light. Know-how outlasts churn. Memory holds.
Today a technical question (customer inquiry, internal R&D assessment, feasibility check) routes through a senior expert and waits two or three days for a reply. Sense 2026 turns that wait into minutes: one inquiry stays in one workspace across days, hand-offs, and reviewers. It runs on an ontology your AI agents act on, shaped to your sub-domain's taxonomy and your team's vocabulary. An inquiry past your risk threshold auto-routes for senior review; a missing field trips your block list before a draft leaves. Where general AI hands you a guess, every Sense answer carries its source and reliability tier. Year by year, your intelligence compounds.
What compounds is what you tune. Where many platforms keep configuration on the vendor side, all three layers stay with your team: catalogue, pricing, and approval rules tuneable in-house; reliability cut-offs and methodology toggles dialled inquiry by inquiry; deeper structure (classifier, risk patterns, scoring weights) architected with us via your advisor file. Pricing is flat per organisation, not per seat. Evaluation is free.
Open to teams ready to explore Sense at their own pace. You don't need to fit the prototype. The shared sandbox is a brainstorming canvas, not the final shape of your platform: your team sits around Sense, explores, and tells us what clicks (or doesn't). We build tailored solutions through back-and-forth, then deploy in steps. Bring your hardest workflow when the time feels right, and reach out to tell us where to start.
Different ways to engage the practice areas: senior advisory for commercial or scientific verification/validation, co-building existing or new threads, contract engineering on bounded scopes, or student research on capstone or thesis topics. Different doors, same workshop, same hands.
A senior second read, right-sized to your scope.
Consulting firms, expert networks, and research organizations engaging a senior advisor for commercial or scientific verification/validation on flexibly scoped engagements. The three practice areas travel with the engagement: commercial strategy (Europe–Asia market entry, ICP and stakeholder validation, pricing and positioning input), technical and scientific advisory (across clinical, environmental, food-safety and adjacent life-sciences briefings, method and workflow validation, regulatory landscape), and digital infrastructure (data platform architecture, LIMS and biobank systems, agentic AI adoption).
Scope an advisory engagementA build partner for the thread you're shaping.
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 threadBounded engineering scopes, paid and credited.
Engineer interested in shipping bounded scope across agentic AI tooling (active build: Sense 2026), 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 scopeMentored projects at the AI × life-sciences edge.
Undergrad 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 ideaA scientist who codes and speaks the domain vocabulary. The combination came from academia before (bio-)informatics existed: self-practicing R, Python, and Java at the bench set a through-line of fifteen years across 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 from wet-lab to bioinformatics. Commercial work covers EMEA market entry, partnerships, pricing, forecasting, and sales direction, leading multidisciplinary teams across sales, project management, lab, and bioinformatics. Across both tracks, the combination shaped digital infrastructure (genomic reference database, biobank, LIMS, SAP) and the organization-wide documentation and compliance system under ISO 17025/9001. Operational vocabulary came from holding each role in the chain (bench scientist, bioinformatician, ops executive, commercial lead), forming the translation layer that tailors deployment to each context, not one-size-fits-all, now in agentic AI tooling (Sense 2026).
Independent practice designing agentic AI flows and digital infrastructure for life sciences teams, from lab operations through commercial. Senior advisory engagements span three overlapping areas (commercial strategy and partnerships, technical and scientific advisory including R&D research support, and digital infrastructure tailored to each team's stack), supporting biotech and the broader R&D community scaling across EMEA and Asia.
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.