| 6df9c0262556436d… | The estimated extraction volume for the SALES_POSITIONING set is 100 to 200 CXUs so that the scope of sales and positioning knowledge capture is explicitly bounded. | active | contextual | yes | |
| ec286a0bc086161b… | The Rank 0 CXUs for the SALES_POSITIONING set are the approved PYRANA elevator pitch, core platform differentiators, standard pricing tiers, and target customer profile so that the most important reusable sales knowledge is prioritized for extraction. | active | contextual | yes | |
| 8149581b503a843f… | The extraction sources for the SALES_POSITIONING set include overview decks, strategy frameworks, positioning documents, transcripts, market sizing presentations, integration materials, pitch assets, and platform framework documents so that sales and positioning knowledge can be extracted from a comprehensive collateral library. | active | contextual | yes | |
| 63a6d7c573d04076… | The SALES_POSITIONING set primarily uses prescribed knowledge for approved messaging and derived knowledge for market insights so that both sanctioned positioning and analytical observations can be stored together. | active | contextual | yes | |
| b5bdaa6ae54d4461… | The SALES_POSITIONING set supports CXU claim types of definition, specification, and prescribed so that approved sales knowledge can be expressed as term meanings, structured facts, and normative messaging guidance. | active | contextual | yes | |
| f21b6d5b7c99b81d… | The SALES_POSITIONING set is sub-organized into VALUE_PROPS, PRICING, COMPETITIVE, MARKET, and PITCH_FRAMEWORKS so that sales knowledge can be segmented by messaging, monetization, competition, market sizing, and reusable pitch structure. | active | contextual | yes | |
| ab1548ed7a35193b… | The SALES_POSITIONING set is intended to contain sales materials, value propositions, competitive positioning, pricing strategies, market analysis, and pitch frameworks so that commercial messaging and go-to-market knowledge are centrally organized. | active | contextual | yes | |
| cfbbed32602d37d9… | The estimated extraction volume for the PROJECTS set is 30 to 80 CXUs per project and 200 to 500 CXUs in total so that project knowledge extraction effort can be forecast at both project and aggregate levels. | active | contextual | yes | |
| f880dc31bcc103d2… | The PROJECTS set uses a mix of knowledge types including axiom for project facts, derived for lessons learned, and prescribed for project standards so that both descriptive and normative project knowledge can be represented. | active | contextual | yes | |
| b434aec998b27311… | The PROJECTS set supports CXU claim types of specification, procedure, requirement, and definition so that project knowledge can capture factual scope statements, operating processes, mandatory constraints, and term meanings. | active | contextual | yes | |
| 9d551060f4e6a1a8… | The PROJECTS set includes example sub-sets for Patrick financial analysis, Patrick eCommerce, Patrick AI strategy, Patrick training workshops, J&J SDLC co-pilot, Tolmar document analysis, and Code3 agency operations so that project knowledge can be organized by named engagement. | active | contextual | yes | |
| 476f583985bb7449… | The PROJECTS set is intended to track project-level knowledge about deliverables, timelines, decisions, risks, and outcomes for projects that are scoped to a client but maintained separately so that learning can be reused across projects. | active | contextual | yes | |
| 1d964d1cb11d5e30… | The expected extraction volume for client-level knowledge is estimated at 50 to 150 CXUs per client and 500 to 1000 CXUs in total so that planning for knowledge graph population can be scoped quantitatively. | active | contextual | yes | |
| 5f8a0e8263dae46b… | The client-specific extraction sources for Patrick include presentation decks, statements of work, a master services agreement, spreadsheets, research documents, project documents, risk assessments, planning documents, proposal files, order forms, and meeting notes captured through Granola integration so that client knowledge can be extracted from a broad set of artifacts. | active | contextual | yes | |
| acae08dfb4807f22… | Patrick client extraction should use a defined set of source materials including strategy decks, SOWs, MSAs, spreadsheets, research documents, risk assessments, plans, order forms, and meeting notes so that Patrick knowledge is comprehensively captured. | active | contextual | yes | |
| 7583dfecb6ae3e32… | Client knowledge types should be classified as prescribed for client-specific requirements and preferences so that negotiated constraints and operating expectations are clearly identified. | active | contextual | yes | |
| 455135ae69a7e8cd… | Client knowledge types should be classified as derived for insights from project delivery about what worked and what did not so that experiential lessons are separated from facts and requirements. | active | contextual | yes | |
| 8dd7edc1777aadfb… | Client knowledge types should be classified as axiom for client facts such as industry, size, and headquarters so that foundational client attributes are distinguished from derived insights and prescribed requirements. | active | contextual | yes | |
| daf6df03073f1917… | Client CXUs may use the procedure claim type, exemplified by Patrick SOW renewals following a quarterly review cycle with IT leadership, so that recurring client workflows are represented as procedural knowledge. | active | contextual | yes | |
| 89574ab52362741a… | Client CXUs may use the specification claim type, exemplified by Patrick’s financial analysis solution processing quarterly earnings across more than 50 subsidiaries, so that detailed project scope facts are captured precisely. | active | contextual | yes | |
| d2e39cd51ca17781… | Client CXUs may use the requirement claim type, exemplified by Patrick requiring all data to remain within US Azure regions, so that client-specific constraints are represented as enforceable requirements. | active | contextual | yes | |
| 13c81476833e30c8… | Client CXUs may use the definition claim type, exemplified by a statement that Patrick Industries is a building products and materials company headquartered in Elkhart, Indiana, so that foundational client facts are captured explicitly. | active | contextual | yes | |
| 721c62f6cb9a5be3… | The CLIENTS:LIFE_SCIENCES sub-set is designated as a vertical and contains cross-client life sciences knowledge so that reusable industry knowledge is available beyond any single client account. | active | contextual | yes | |
| 762b1bbd11d175a8… | The CLIENTS:ZENDA sub-set is marked Active/Prospect with domain still listed as TBD so that Zenda can be tracked before its domain taxonomy is finalized. | active | contextual | yes | |
| 4e337e3be3838bb2… | The CLIENTS:PROFITERO sub-set is marked Active/Prospect and covers eCommerce analytics and retail so that Profitero knowledge can be queried separately from other clients. | active | contextual | yes | |
| f2c17716a8716494… | The CLIENTS:CELLDEX sub-set is marked Active/Prospect and covers biotech and therapeutics so that Celldex knowledge is represented in a dedicated client context. | active | contextual | yes | |
| ce0bb42eb5dfa6e7… | The CLIENTS:ASSUREA sub-set is marked Active/Prospect and covers insurance and compliance so that Assurea knowledge can be managed within a dedicated client partition. | active | contextual | yes | |
| 2a901a5c9c822638… | The CLIENTS:LASTMILE sub-set is marked Active/Prospect and covers logistics and delivery so that LastMile-related knowledge is available for client-scoped retrieval. | active | contextual | yes | |
| 69bba4b4b103e55c… | The CLIENTS:HUNTER_ONSITE sub-set is marked Active/Prospect and is associated with the field services domain so that Hunter Onsite knowledge is maintained in a dedicated client scope. | active | contextual | yes | |
| 7830fa132cebead6… | The CLIENTS:EY sub-set is marked Active/Prospect and covers professional services, audit, and consulting so that EY knowledge can support both active and prospective engagement analysis. | active | contextual | yes | |
| 57f9d5b202d93332… | The CLIENTS:TOLMAR sub-set is active and covers document analysis and pharmaceutical manufacturing so that Tolmar-specific knowledge can be queried independently. | active | contextual | yes | |
| 5944d9fa19ca9393… | The CLIENTS:JNJ sub-set is active and focuses on SDLC co-pilot, pharmaceutical, and life sciences domains so that JNJ-related knowledge is grouped under a dedicated client context. | active | contextual | yes | |
| ed3cc8fe6055ccf0… | The CLIENTS:PATRICK sub-set is active as the primary client and covers the domains of financial analysis, eCommerce, AI strategy, and manufacturing so that Patrick knowledge is isolated within a dedicated client scope. | active | contextual | yes | |
| bac7e31498c351ec… | The CLIENTS set stores client-specific knowledge such as relationship history, project context, organizational structure, key contacts, domain terminology, and negotiated terms so that each client’s working context is retrievable. | active | contextual | yes | |
| b2ea43f5e7cff380… | The expected extraction volume for the COMPANY_POLICIES set is 50 to 100 CXUs, indicating the anticipated scope of policy knowledge to be captured from the listed sources. | active | contextual | yes | |
| 98d5441ed4a6564d… | Rank 0 CXUs for the COMPANY_POLICIES set must include core information security requirements, AI model usage guardrails, and BYOD device requirements so that the highest-priority policy knowledge is captured first. | active | contextual | yes | |
| 27ec037be59efba4… | The COMPANY_POLICIES set should be populated from four named policy documents, including information security, BYOD, AI model security, and comprehensive security policy sources, so that policy CXUs are grounded in authoritative internal materials. | active | contextual | yes | |
| ffac3c3f17ca8eeb… | Knowledge extracted from the COMPANY_POLICIES set should be classified almost exclusively as prescribed knowledge because the source documents define official internal policy requirements. | active | contextual | yes | |
| 7153463fe24de4ed… | CXUs extracted from the COMPANY_POLICIES set should be expressed primarily as requirement, procedure, and prescribed claim forms because the set is intended to capture policy-driven operational rules. | active | contextual | yes | |
| d9172bbe83a38207… | For the Patrick client, extraction sources include strategy decks, SOWs, an MSA, spreadsheets, research documents, security assessments, planning documents, order forms, and meeting notes via Granola integration so that Patrick knowledge is assembled from both formal and operational artifacts. | active | contextual | yes | |
| 97f22f61b53a7aea… | Client knowledge types are categorized as axiom for client facts, derived for project-delivery insights, and prescribed for client-specific requirements and preferences so that extracted client CXUs distinguish facts, lessons, and mandates. | active | contextual | yes | |
| 374a499685c4f2c5… | Client CXUs may be expressed as definition, requirement, specification, procedure, and relationship claim types, as illustrated by the Patrick examples, so that multiple forms of client knowledge can be represented. | active | contextual | yes | |
| aac6cee9adef33c2… | The CLIENTS:LIFE_SCIENCES sub-set is defined as a vertical domain for cross-client life sciences knowledge so that reusable industry knowledge can be shared across multiple client accounts. | active | contextual | yes | |
| 94ce4ceb07f486e9… | The CLIENTS:ZENDA sub-set is classified as active or prospect with key domains still marked TBD so that Zenda knowledge can be created before domain scoping is finalized. | active | contextual | yes | |
| 95bddc7546f94037… | The CLIENTS:PROFITERO sub-set is classified as active or prospect and focused on eCommerce analytics and retail so that Profitero knowledge is grouped around commerce intelligence use cases. | active | contextual | yes | |
| d400041af1e7e1a4… | The CLIENTS:CELLDEX sub-set is classified as active or prospect and focused on biotech and therapeutics so that Celldex knowledge is organized within life sciences innovation work. | active | contextual | yes | |
| c84795d4f1216d7a… | The CLIENTS:ASSUREA sub-set is classified as active or prospect and focused on insurance and compliance so that Assurea knowledge is grouped under regulated risk-management topics. | active | contextual | yes | |
| b5e1522eb44efccf… | The CLIENTS:LASTMILE sub-set is classified as active or prospect and focused on logistics and delivery so that Lastmile knowledge is organized around transportation operations. | active | contextual | yes | |
| 4fc48ea62ad0abd6… | The CLIENTS:HUNTER_ONSITE sub-set is classified as active or prospect and focused on field services so that Hunter Onsite knowledge is tracked as a developing client domain. | active | contextual | yes | |
| 66742a1c54d2af8b… | The CLIENTS:EY sub-set is classified as active or prospect and covers professional services, audit, and consulting so that EY knowledge can support both current and pipeline work. | active | contextual | yes | |