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Knowledge Engine
Find the missing link between ideas.
$59/user/mo
Drop in text. Get a knowledge graph — concepts, connections, and the gaps between them. Not search. Not summaries. Structure you can reason about. Paste a research paper — see clusters like "insulin resistance," "mitochondria," "inflammation" — and the missing link between them.
One pass — no iterative prompts, no tuning, no retries. Drop in text, get structure immediately. No LLM, no API calls, no data sent anywhere. The knowledge graph IS the intelligence. The graph shows what you wrote. The gaps show what you missed.
WHAT YOU GET
One-pass concept extraction from any text
Knowledge graph with weighted connections
Gap detection: find what's missing between concepts
Recall by resonance: query by concept, not keyword
Automatic clustering of related ideas
Tension detection: flags concepts that conflict or don't align across sources
Cross-document concept linking
Exportable graph for visualization and sharing
Hardware-signed knowledge graphs via Secure Enclave
WHAT THIS IS / WHAT THIS ISN'T
WHAT THIS IS
One-pass text-to-knowledge-graph extraction. Feed documents, get concepts, connections, clusters, and tension-detected gaps. No LLM, no API calls, no per-token cost. Runs locally.
WHAT THIS ISN'T
An LLM. It doesn't answer questions in natural language. It extracts STRUCTURE from text — concepts and their relationships. For Q&A, pair it with an LLM. For understanding what your documents contain and what's missing between them, this is it.
YEAH BUT
"Notion AI already does this."
Notion searches your documents. We extract a GRAPH. Concepts, connections, tensions, clusters. The difference: Notion finds what you wrote. We find what you missed.
"What about RAG pipelines?"
RAG retrieves answers. We show what's missing. RAG answers questions from documents. We show you the gaps between documents — the knowledge you don't have yet.
"No LLM means limited?"
No LLM means no per-token cost, no API dependency, no data sent anywhere. One pass, local, instant. The knowledge graph IS the intelligence. You get structure, not stochastic parroting.
VS THE COMPETITION
Glean
$50+/user/mo — 100-seat minimum, $70K POC, 7-12% annual increases. Enterprise search, not graph extraction.
Knowledge Engine: $59/user/mo flat. No minimums. Extracts structure, not just search results.
Notion AI
$18/user/mo for AI — flat search, not graph extraction. Finds what you wrote, not what's missing.
Knowledge Engine: concept graph with gap detection. Finds the knowledge you don't have yet.
Obsidian
Free but manual graph. Steep learning curve. No automatic extraction.
Knowledge Engine: automatic extraction in one pass. Drop in text, get a graph. No manual linking.
TRY IT
3 free analyses. Fixed sample data. Your own data requires a license.
TESTING
Unit tests, adversarial input testing (None, wrong types, NaN, empty data, unicode), real user workflow testing, and cross-product integration testing. Every public function handles every input permutation without crashing. 673 quality tests across all products, zero failures. Self-verifying: the product can audit its own output.
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Get Knowledge Engine — $59/user/mo
After purchase: setup guide
INSTALL
pip install begump
from gump.knowledge import *