Emerging Product

Concept in progress

Immunology Scout

A paper-and-patent scouting product concept for immunology teams, designed to ground questions, surface novelty signals, and support next-step hypotheses.

AI + Science
Research Workflow
Emerging Product

Discuss frontier AI products

Immunology Scout screenshot showing a literature scouting workflow for immunology research.

01 · Product stage: Early-stage concept; The opportunity is real, but still early and intentionally described with restraint.
02 · Core job: Paper + patent scouting; Help teams move from scattered evidence to grounded novelty checks and sharper next-step questions.
03 · Trust requirement: Traceable workflow; Citations, transparency, and human review are non-negotiable in scientific research workflows.

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Product framing

An early-stage research grounding product combining literature and patent signals for trustworthy immunology exploration.

01 · Problem

The friction this product addresses.

01Scientific teams are overwhelmed by papers, pathways, weak signals, and adjacent prior art across fast-moving therapeutic areas.

02The challenge is not just finding information. It is determining what matters, what may already exist, and what is worth pursuing next without losing momentum to manual synthesis and novelty checking.

03Generic AI summarization can compress text, but that alone is insufficient for a trustworthy research workflow. Teams still need cited evidence, patent awareness, and a clear boundary between grounded signals and scientific judgment.

02 · Solution

An intentionally narrow product wedge.

01Immunology Scout is intentionally scoped as a research grounding layer rather than a broad "AI for science" platform.

02The current wedge is paper + patent exploration: combine scientific literature and prior-art signals so teams can see what is known, what is uncertain, and where genuine opportunity may exist.

03Patent-aware exploration helps researchers pressure-test novelty, understand competitive context, and avoid duplicating work that may already be claimed or crowded.

04Outputs are structured to support hypothesis refinement, competitive analysis, and future downstream workflows such as simulation and experimental planning systems, while keeping human scientific reasoning in the loop.

Product experience

What the user actually does.

Start with a focused research question

The workflow begins with a specific immune pathway, marker, therapy area, or scientific uncertainty so the search stays grounded in a real research question.

Review evidence and novelty signals

The product surfaces literature themes, conflicting signals, related patents, and potential whitespace with citations and traceability visible for review.

Shape next-step hypotheses and directions

Outputs are designed for hypothesis refinement, literature triage, scientific discussion, and downstream planning rather than replacing scientific reasoning.

Visual evidence

See the product at work.

Current product visuals and public-safe artifacts from the working experience.

Immunology Scout interface showing a search-and-synthesis workflow. — 01
Immunology Scout retrieval workflow
The interface frames retrieval, synthesis, and scoped query inputs as a research workbench instead of a generic chatbot.

Evaluation & trust

Define quality before claiming confidence.

How quality was defined, tested, and improved in a high-stakes domain.

This was the first project where I built formal evals. Because I was not the domain expert, I worked with a scientific collaborator to define what a useful, trustworthy output should actually contain.

Structured expert feedback became the basis for a lightweight evaluation set: what evidence should be cited, where claims needed tighter boundaries, and what would make the product genuinely useful to a researcher instead of merely polished.

That process surfaced a critical issue early: the system could hallucinate patent references, which is unacceptable in a workflow meant to support novelty checking and competitive understanding.

Once those failures were visible and measurable, iteration became much more disciplined. Later versions enabled tighter automated feedback loops, but the larger lesson was product-oriented: in high-stakes domains, trust comes from repeated loops between domain expertise, evaluation, traceability, and system behavior.

What I learned

The product and leadership lessons.

01
Trust in high-stakes AI products is earned through traceability, humility, and evaluation, not just fluent answers.
02
When the builder is not the subject-matter expert, domain-expert-informed evaluation is essential for defining quality and catching failure modes early.
03
In scientific workflows, compressing synthesis and novelty checking is often more valuable than trying to automate judgment end to end.
04
Strong science product opportunities can start with grounding and signal clarity before expanding into more ambitious downstream systems.
05
Modest public framing matters when the product is promising but still emerging.

Contact

Interested in the build, prototype process, or product logic?

These pages are intentionally structured so the product story is easy to discuss with recruiters, founders, or future teammates.

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