The knowledge layer that captures why decisions are made — documents, wikis, notebooks, and 3D annotations with evidence, confidence, and provenance built in.
The knowledge layer that captures why decisions are made — documents, wikis, notebooks, and 3D annotations with evidence, confidence, and provenance built in.
Every claim carries its evidence chain, confidence score, provenance graph, and review state. When someone asks 'why do we believe this?', the answer is one query away.
CRDT-native editing means two engineers write the same paragraph simultaneously — both edits survive. Offline first. Conflict-free merge on reconnect. No locking.
Type @ and reference any entity across your operations platform, object platform, datasets, models, tickets, and documents. Backlinks update automatically.
Run Python, Rust, SQL, and Markdown in reactive cells next to institutional knowledge. Reproducible analysis with evidence attachment and provenance.
Open GLTF, OBJ, STL, STEP, and IGES models in the browser. Annotate parts, take measurements, cross-section, and overlay analytics — all tied to the knowledge graph.
Publish curated knowledge into branded customer portals via the experience platform. 35+ component types for graph viewers, entity cards, wiki embeds, and 3D viewers.
Every organization loses knowledge. The engineer who designed a part leaves. The analyst who wrote the risk assessment moves teams. The compliance rationale behind a policy decision gets buried in email. The data remains — in ERP, PLM, CRM, ticketing systems — but the why disappears. MUKR captures the why.
MUKR is a knowledge context layer built in Rust. It combines documents, wikis, computational notebooks, 3D annotations, and a knowledge graph into one platform where every claim carries its evidence, its confidence, its provenance, and its review state. When a design decision is documented, the rationale, the alternatives considered, the evidence supporting it, and the reviewers who approved it travel together — not as scattered metadata, but as structured knowledge.
A block-based document editor with 35+ block types — paragraphs, headings, lists, media, tables, code, formulas, notebook cells, 3D models, cross-references, and knowledge claims. Drag and drop reordering. Schema-validated blocks so structured content stays structured.
Every block is CRDT-native — two people editing the same paragraph is a normal operation, not a conflict. AI-assisted composition for summarize, expand, improve, translate, and explain. When a block references an entity in your operations platform, the reference is live — change the entity and the document reflects it.
A claim moves through 11 states: Proposed, Evidence-Attached, Under-Review, Validated, Verified, Operationalized, Disputed, Stale, Superseded, Retracted, and Rejected. Each transition requires provenance, calibration, policy approval, and a recorded owner. A claim does not become operational knowledge because someone wrote it — it becomes operational knowledge because it passed review.
When a claim is superseded, the old claim is not deleted — it is marked as superseded with a link to the replacement. When a claim is retracted, downstream claims that depended on it are marked as stale. The history is preserved. The chain is traceable.
CRDT-native means conflict-free. Two people type in the same paragraph — both edits survive. No locking. No 'document is being edited by someone else.' No last-save-wins. The Loro CRDT engine handles merges at the character level. LiveKit data channels deliver updates with p99 under 100 ms.
Presence is built in — cursors, selections, and avatars show who is where in the document. Screen sharing, voice, and video run through the same LiveKit session. A design review happens in the tool, not in a separate video call with a shared screen.
Type @ and reference any entity across your operations platform (parts, BOMs, work orders, quality records), your object platform (datasets, models, code), your ticketing system, your chat messages, and your document library. The reference is live — change the entity and the document reflects it.
Resolution follows a tiered path: local cache, knowledge graph, heuristic matching, object platform, operations platform, and external connectors. Unresolved references return AccessDenied — not NotFound — so that existence is not leaked to unauthorized users.
Write design rationale with evidence, alternatives, and review state tied to parts and BOMs. When you leave, the rationale stays — with its evidence chain intact. Your replacement starts from the knowledge graph, not from a blank page.
Link NCRs, CAPA records, and inspection results to claims with confidence scoring. When an auditor asks for the evidence behind a quality decision, the provenance export generates the package automatically.
Every claim, every decision, every policy interpretation carries its evidence chain. When a regulation changes, every claim that cited the old version is flagged for re-review. Audit-ready without manual reconstruction.
Competing hypotheses coexist with their evidence. Notebook cells with reproducible provenance ensure analyses are traceable. When a peer reviewer asks how a result was produced, the notebook is the evidence.
MUKR is available as a managed SaaS — we run the platform, your team writes the knowledge. Tenant-isolated with cryptographic separation. No cross-tenant leakage. When you want to bring it on-prem, the migration is a data export — not a re-platform.
35+ component types: knowledge graph viewers, entity cards, wiki embeds, 3D model viewers, notebook cells, search bars, hover modals, chart renderers, verification badges, annotation overlays, and more. Each component reads from MUKR knowledge sources and binds to live data.
Components are permission-aware — the same component shows different content to an authenticated customer than to an anonymous visitor. Read-only and authoring modes depend on the user's role and realm.
Where knowledge is captured — documents dominate by volume, but 3D annotations and notebooks carry the deepest evidence. Graph explorers reveal relationships. AI composition synthesizes new knowledge from existing claims.
Every surface feeds the same claim graph. No knowledge is orphaned. No evidence is lost across tool boundaries.
Claims move through 11 states. Most claims reach Verified. Some stay Proposed. Disagreements are preserved, not lost — every dispute is a first-class object with evidence, context, and resolution state.
No claim is deleted. No evidence is discarded. The knowledge graph remembers everything.
Semantics, ontology, epistemology, and heuristics — each layer adds a different kind of intelligence. Semantics defines meaning. Ontology structures relationships. Epistemology tracks evidence and belief. Heuristics applies operational wisdom.
Together, they form a knowledge system that understands not just what things are, but why they are, how we know, and what to do about it.
The engineer who designed the part leaves. The analyst who wrote the risk assessment moves teams. The compliance rationale behind a policy decision gets buried in email. The data remains — in your ERP, your PLM, your CRM, your ticketing system — but the why disappears. You can find the part number. You cannot find why the part exists, which alternatives were rejected, or which defect history changed the decision.
MUKR is built on a different premise. Knowledge is not a storage problem — it is an evidence problem. A claim is not knowledge because someone wrote it. A claim is knowledge because it has evidence, because it passed review, because its confidence is calibrated, because its provenance is traceable, and because its lifecycle state is governed. When an engineer leaves, the knowledge stays — with its evidence, its confidence, and its review chain intact.
That is what MUKR is. Not another wiki. Not another document store. An evidence system for institutional knowledge.
Operator relevance: A reviewer can answer whether a claim is usable, what evidence backs it, what has changed, and where escalation belongs — in one query.