Peel · The knowledge
Structured records keep context and typed connectors together, making knowledge inspectable and reusable.
Run every experiment, every literature search, and every model call through one governed console. Peel gives it a knowledge graph where facts carry their evidence; Lois reasons over what is known; and every answer comes back pinned to its source — local by default, yours to inspect, challenge, and build on.
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macOS (universal) · Linux (x86_64 / aarch64) · checksum-verified · no runtime needed
No special equipment and nothing to rip out — we connect the systems your lab already uses. Literature, structures, variants, chemistry, and pathways through the services below; on the WetHands side, your LIMS, electronic lab notebooks, and instrument APIs. One governed console, every answer source-pinned.


AlphaFold DBInterPro

ReactomeQueried live through their public interfaces under the source contract. Names and marks belong to their respective owners; no partnership or endorsement is implied.
Prototype snapshot reported in the public paper; these are scale figures, not bioscience coverage or accuracy scores. Read the source ↗
Six choices. Three turns. 216 distinct paths. This real AAC conversation sample makes context and relationships visible. Click a cell to light up its path and inspect its dataset connectors. Switch topics and explore a different tree.
In patients with coronary artery disease and heart failure, guideline-directed medical therapy should precede revascularization strategies like PCI or CABG.
Drag to orbit · Scroll to zoom · Click to inspect · Or use the keyboard pickers below
This explorer contains conversation knowledge, not biomedical findings. Connectors are dataset relationships; a connector alone does not establish a real-world causal claim.
Peel began with augmentative and alternative communication, where context and dependable expression matter deeply. The same principles speak to a larger scientific need: preserve the basis of a claim, make relationships explicit, and keep open questions visible.
Structured records keep context and typed connectors together, making knowledge inspectable and reusable.
A symbolic engine follows recorded relationships, explains its paths, and can return unknown when evidence is missing.
The surrounding environment connects science tools, experiments, and verification. Each result still has to earn its standing through evidence within a defined scope.
How knowledge, reasoning, and execution fit together.
Explore chapterRecords, typed relationships, reasoning, and expression — four parts with distinct jobs, so you can see where an answer came from and where its authority ends.
Explore chapterWhat supported, conflicting, and unknown mean.
Explore chapterQuery stored knowledge, follow the explanation path, and check known conflicts. When the graph lacks a relationship, Lois returns unknown instead of a guess.
Explore chapterLiterature, molecular data, pathways, computation — and experiments — under one source-pinned contract.
Explore chapterFive layers — literature, molecular data, computation, the source contract, pathways — plus WetHands experiments. Every result names its database, or it is an error.
Explore chapter44 instruments, 19 services, one console.
Explore chapterPubMed to AlphaFold to KEGG on one console, the compute bench, the WetHands lane, and pure C++ on the metal — instruments that were never meant to connect, talking.
Explore chapterLocal models by default, never trained on your data, the record never leaves your disk.
Explore chapterReasoning runs on local Ollama models by default; nothing leaves your devices to train any model; external models are opt-in; and the record — graph, ledger, notebook — is files on your disk that work with the cable pulled.
Explore chapterFrom evidence maps to testable hypotheses.
Explore chapterEvidence maps, pathway review, hypothesis comparison, and continuity across a lab — the proposed research workflow we want to evaluate with partners.
Explore chapterPublic artifacts, prototype scope, and the validation path.
Explore chapterReleased datasets and models, the prototype's real scope, the public papers, and the validation path — what is built, what is next, and what still needs a test.
Explore chapterOur ambition is a research foundation where teams can inspect evidence, compare hypotheses, and retain the reasoning behind a result. Domain partnerships and careful evaluation are how we take that foundation into bioscience.
A real research session, replayed live in your browser — the AI proposes, the floor pulls real data by accession, catches a contradiction, and abstains when nothing resolves. Every line comes back pinned to its source.