The Grounded
World Model.
Autonomous agents require a deterministic world model of your business. WUF continuously ingests messy, fractured documents across Google Drive, M365, Slack, and Notion—distilling them into atomic, structured canonical facts that eliminate hallucinations at the source.
"Section 4.1: The Enterprise Platform License fee is established at $4,999.00 USD billed monthly in advance, effective from October 1, 2024 through September 30, 2026, encompassing unlimited vector query execution and dedicated tenant isolation."
The Extraction Engine Architecture
How WUF.AI ingests high-velocity enterprise silos and converts chaotic prose into an immutable, structured truth network.
Silo Synchronization
Bi-directional connectors to M365, Google Drive, Slack, and Notion with OAuth / Entra ID enterprise token scoping.
Smart Chunking
Hierarchical parent-child segmenting with halfvec(768) float16 embeddings. Preserves broad LLM context while maximizing retrieval speed.
Canonical Fact Model
Distills text into atomic triplets (Entity, Generic Relation, Normalized Value) with deep typed JSONB attributes.
Strict Provenance
Every single fact is cryptographically linked to byte offsets and paragraph hashes in the original source document.
The Living Knowledge Graph
Explore an active slice of an enterprise truth topology. Select nodes to inspect canonical entity metadata, relational predicates, and cryptographic source evidence.
Hierarchical
Smart Chunking.
Most AI wrappers use naive flat chunking: fixed 500-token blocks that sever sentences in half and lose critical context.
WUF.AI computes dynamic hierarchical parent_child chunk relationships:
Ultra-compact vector embeddings optimized for halfvec(768) similarity search. Queries hit the exact semantic needle in <12ms.
When a child chunk matches, WUF.AI injects the full surrounding parent context to the LLM—guaranteeing complete situational awareness without index bloat.
"...Subject to Exhibit C, all custom deployments retain enterprise access privileges under MSA Tier-3. The SLA provisions defined herein require Tier-1 engineering personnel to respond within 15 minutes of any incident categorization..."
"The SLA provisions require Tier-1 engineering to respond within 15 minutes."
The Golden Relation Rule
Why brittle knowledge graphs fail: developers create thousands of hyper-specific predicates like has_q3_2024_price_in_usd. WUF.AI enforces clean, generic relations while storing fine granularity in typed JSONB attributes.
Creating ad-hoc relation predicates causes relational fragmentation, breaks multi-document joins, and renders Cypher queries impossible.
Generic relations (has_price, governed_by) pair with strict, queryable JSONB attributes.
"currency": "USD",
"amount": 4999.00,
"iso_timestamp": "2024-10-01T00:00:00Z"
Bi-Temporal Facts.
Truth is not static. Yesterday's price is not a contradiction of today's price—it is a historical predecessor.
WUF.AI implements native bi-temporal validity (valid_from, valid_until, and extracted_at). The Knowledge Graph answers questions across past, present, and contracted future states with zero temporal ambiguity.
"What were our SLA commitments to European enterprise customers in Q2 2023?" WUF.AI retrieves point-in-time facts exactly as they stood on that date.
Irrefutable Byte-Range Provenance
A fact without evidence is a hallucination. Every atomic fact extracted by WUF.AI is bound by byte offset to the exact sentence of the source document.
Tiered Storage Policy Engine.
Storing terabytes of raw unstructured text in high-memory database instances causes database costs to skyrocket.
WUF.AI separates vector retrieval from storage: dense halfvec(768) embeddings remain hot in memory for instant queries, while raw document texts are automatically offloaded to cold S3/Blob storage based on your organization's data retention policy.
Vector chunks, canonical entities, relational index, active conflict queues.
Original raw bytes, historical document versions, archival audit diffs.
Knowledge Graph FAQ
Architectural answers for enterprise engineering and compliance teams.
01. How does WUF.AI resolve duplicate entities across different silos?
WUF.AI utilizes a deterministic entity resolution pipeline. When 'Apple', 'Apple Inc.', or 'Apple Computer' are encountered across Jira, Zendesk, or Slack, they are reconciled against canonical enterprise registries (such as your Salesforce CRM or customer master table) and assigned a single immutable Canonical Entity ID with documented aliases.
02. Can the Knowledge Graph be queried using graph languages like Cypher?
Yes. In addition to hybrid vector similarity search (halfvec(768) + full-text BM25), the underlying graph topology supports open graph traversal queries, allowing autonomous agents and engineering teams to query relationship depths (e.g. Entity → governed_by → Compliance_Standard).
03. How are enterprise access controls (RBAC/ABAC) maintained inside the graph?
Every document, chunk, and fact inherits strict ABAC allow/block lists from the source integration (e.g. Google Drive folder permissions or M365 Entra ID group memberships). When a user or agent queries the knowledge graph, queries are strictly gated at the database level—preventing unauthorized information disclosure.
04. What happens when two documents directly contradict one another?
Contradictions are intercepted by the Consistency Engine as a Fact Conflict. Using source authority signals (e.g. Signed Legal Contract > Wiki > Slack message) and timestamps, WUF.AI scores certainty and either auto-proposes a patch or alerts human owners for formal sign-off.
05. What embedding model and vector dimensionality does WUF.AI deploy?
WUF.AI natively supports halfvec(768) float16 embeddings, cutting vector index memory consumption in half while preserving 99.8% precision over standard float32 vectors, enabling sub-15ms semantic retrieval over millions of chunks.
Eliminate Drift.
Own Your Truth.
Stop letting outdated wikis and scattered Slack messages corrupt your enterprise reality. Deploy the WUF.AI Knowledge Graph today.