Research synthesis
See how 20 papers connect without building the map yourself.
Upload your source material and get a dependency-ordered path through it.
Early access is open — spots are limited. Check availability →
Synthesize any source into structured knowledge.
SILKLEARN synthesizes your sources — not answers questions about them. It maps what depends on what, finds where sources contradict each other, and generates a path you can actually follow.
Any doc format — PDF, Markdown, Notion, Confluence
Contradiction detection runs at ingest, not on request
Structured context your AI can actually use
“I've read this three times and I still can't see how it connects.”
Because sources show conclusions, not the path that makes them make sense.
“I switched domains. There are 50 tabs open and no map.”
Because the prerequisite logic is scattered across sources no one has mapped for your starting point.
“My AI summarizes everything. It never tells me what order to understand things in.”
Because what it receives has no order — and neither does what it gives back.
1Compile
2Review
3Ship
Three steps from any source to a path you can follow.
Step 01
Drop in your docs, exactly as they are.
PDF, Markdown, DOCX, Notion, Confluence. No reformatting needed.
Apply for accessStep 02
See the structure. Confirm it before you follow it.
See every dependency as it's mapped. Inspect connections and source links before you commit to a learning path.
Apply for accessStep 03
Get a path you can follow from the first source you add.
Learning path, synthesis bundle, or AI context — from one source-linked structure.
Apply for accessYour sources were organized for the expert who wrote them, not for where you’re starting.
Research synthesis
See how 20 papers connect without building the map yourself.
Upload your source material and get a dependency-ordered path through it.
Domain switching
Enter a new field knowing what to learn first.
Surface the prerequisite logic before you waste time on the wrong starting point.
Internal AI context
Give your AI structured context, not a RAG guess.
Your AI gets structure it can actually use — ordered by how the knowledge depends on itself.
Conflict detection
Surface what your sources disagree on.
SILKLEARN flags contradictions as it compiles — not after you've already built on a wrong assumption.
Why this works
You can’t navigate what you can’t see.
Map the order, source, and dependencies before you start reading — or you’re guessing at which pieces to trust.
Common questions
Core questions, answered directly.
Anything text-based today: PDFs, docs, Notion pages, web links, code repos. Video, audio, and API feeds are coming. The synthesis engine works on any source you can point it at.
Those tools answer questions about your sources. SILKLEARN synthesizes the structure of your sources. It doesn't wait for you to ask — it runs at ingest, maps what depends on what, and flags where sources contradict each other. The output is a path that persists, not an answer that disappears.
A dependency-ordered path through your sources, a visual graph of what connects to what, and a list of contradictions detected across your material — so you know what to read first, what to question, and what order actually matters.
Anyone synthesizing knowledge from multiple sources — researchers reconciling conflicting papers, developers onboarding to an unfamiliar codebase, consultants distilling client materials, or solo learners who need a path, not a pile. Individual-first by design.
Yes — MCP integration is in progress. Any AI agent will be able to call the synthesis engine directly, get back a structured dependency map, and use it as clean context for downstream reasoning. This is what RaaS (Results-as-a-Service) means: the output works for humans AND agents.
The platform is individual-first — built for the person doing the synthesis, not the org buying seats. Canvas (the visual review layer) supports sharing and collaboration, but the core product is yours to run alone. Enterprise (private cloud deployment) is coming.
Early access
Apply if you’re working from a stack of sources with no clear path through them.
We’re reviewing applications in waves — researchers, developers, students, and domain switchers working from dense sources are the best fit.