Enterprise Knowledge Vault for AI Agents — Reference Architecture

The Problem — As-Is

Three Personas, One Pattern: Knowledge Trapped in People and Silos

Different domains, different systems, same outcome: when your experts need context, they spend hours searching scattered sources and still end up with partial, unverifiable answers.

👩‍⚕️

Dr. Mira Patel

VP Clinical Development

“Why did we switch from PFS to co-primary endpoint?”

📂 Searches SharePoint — finds 140 docs, none tagged

📧 Digs through 2 years of email threads

💬 Tracks down former med director via LinkedIn

📋 Reconstructs rationale from meeting notes fragments

3–5 days

To reconstruct one decision

💼

James Chen

Head of Brand Strategy

“What changed in the competitive landscape since last QBR?”

📊 Pulls IQVIA data from Databricks — unlinked to strategy

📄 Searches vendor reports, 4 different portals

📧 Chases field medical for KOL sentiment (in email)

🎨 Manually stitches together a slide deck

4+ hours

Per quarterly briefing — every quarter

⚙️

Sara Rodriguez

Quality Director, Manufacturing

“Has this bioreactor pH excursion happened before? What was the root cause?”

📚 Searches QMS (TrackWise) — 500+ deviation records

📄 Cross-references batch records in MES

👨‍🔧 Asks 20-year process engineer who “just knows”

📝 Tribal knowledge never written down

2–3 days

Per deviation investigation

What Exists Today

A Pattern That Works — For One Person on One Machine

Local markdown vaults have proven the model: structured files with metadata, bidirectional links, and AI agent access. The format is the strength. But the enterprise layer is entirely missing.

What a local vault provides (1 person, 1 machine)

📝Plain Markdown Files
Human-readable, agent-parseable

📚YAML Frontmatter
Structured metadata per note

🔗Bidirectional [[Wikilinks]]
Knowledge graph via links

🤖Agent Read / Write
File-system access for AI

What the enterprise needs but doesn’t have

☁️

Cloud-Hosted & Multi-User

Shared vaults across teams, sites, and departments

GAP

🔒

Access Control & Permissions

Role-based read/write at file, folder, and tag level

GAP

📝

Version Control & Audit

Immutable history, diff, blame, regulatory retention

GAP

🔌

MCP Server / Standard API

Any agent reads and writes via standard protocol

GAP

🤖

Agent-Maintained Curation

AI proposes structure; humans approve; context compounds

GAP

🛡️

Regulatory Compliance (GxP)

21 CFR Part 11 audit trails, ALCOA+ data integrity, e-signatures

CRITICAL FOR PHARMA

🔬

Knowledge Graph Intelligence

Impact analysis, orphan detection, automated relationship discovery

COMPOUNDING VALUE

🔃

Enterprise System Integration

Bidirectional sync: QMS, LIMS, CRM, data lake — not a silo

ADOPTION BLOCKER

Honest Scoping

What This Architecture Is — and Isn’t

The Enterprise Knowledge Vault fills a specific gap. It does not replace the systems you already run. Understanding the boundaries matters more than the features.

🚫 This Is Not

A replacement for Veeva Vault, Documentum, or your QMS. Those systems are your records of record. They stay. The Knowledge Vault curates the context around those records — the “why,” not the “what.”
A replacement for your data lake or vector database. Your existing RAG pipeline and embeddings infrastructure is valuable. The Knowledge Vault is the curation and governance layer that makes those pipelines more useful.
A general-purpose document management system. This is not where you store every file. It is where curated, structured, linked knowledge lives — the institutional context that makes AI agents intelligent rather than just fast.
Something you build from scratch. The architecture is designed to be assembled from existing infrastructure: S3 or Azure Blob for storage, OpenSearch or Pinecone for vector search, your existing IdP for auth, your existing data lake for source data.

✅ This Is

The curation and governance layer between your systems and your AI agents. It connects what you already have into a structured, permissioned, auditable knowledge layer that any agent can read.
An integration architecture, not a monolith. The value is in the connectors (to QMS, LIMS, CRM, data lake), the curation pipeline, and the agent access protocol — not in reinventing storage or search.
A markdown-native knowledge store with enterprise governance. Plain text files with structured metadata, wikilinks, and version control — extended with RBAC, audit trails, and regulatory compliance for pharma.
An architecture pattern you adapt to your stack. The reference architecture shows what capabilities are needed and how they connect. Implementation uses your existing infrastructure wherever possible.

Where This Fits

Your Enterprise Already Has These Systems — None of Them Solve This Problem

Each of these platforms is excellent at what it does. None of them provides a curated, agent-accessible knowledge layer with structured context, provenance, and cross-domain linking. That is the gap.

Veeva Vault

Document Management & QMS

The gold standard for GxP document management in pharma. Manages SOPs, protocols, regulatory submissions, and quality records with full 21 CFR Part 11 compliance.

Gap: Stores documents, not curated knowledge. No wikilinks, no structured context, no agent-native access. You can find the protocol but not why the endpoint was changed.

Palantir Foundry

Enterprise Data & Ontology

Powerful ontology layer for connecting enterprise data. Excels at structured data integration, operational analytics, and decisioning workflows.

Gap: Optimized for structured data and operational workflows, not narrative knowledge. The “why did we choose this dose?” rationale doesn’t fit naturally into an ontology model.

Confluence / Notion

Collaborative Wiki

Widely used for team documentation. Good for project wikis, meeting notes, and general knowledge sharing across departments.

Gap: Not GxP-compliant. No structured metadata schema. No agent-native API. Knowledge rots quickly without curation workflows. Useful for team docs — not for institutional knowledge that outlives teams.

RAG / Vector DB

Retrieval-Augmented Generation

Your existing embeddings pipeline retrieves relevant chunks from documents. Valuable for grounding AI responses in your corpus.

Gap: Retrieves snippets, not curated context. No provenance chain. No knowledge-level versioning. No human curation loop. RAG answers the question “what’s in our documents?” but not “what do we actually know?”

The Enterprise Knowledge Vault Fills the Gap Between These Systems

It pulls context from your QMS, data lake, and CRM. It structures that context as curated, linked, versioned knowledge. It exposes it through agent-native APIs so any AI agent — your chatbot, your compliance checker, your competitive briefing generator — can think with your institution’s full context, not retrieved fragments. Your existing systems stay. This is the layer on top.

Reference Architecture

You Already Have the Foundation — This Is the Next Layer

Your enterprise already runs a data lake, vector databases, and RAG pipelines. Your people already use local markdown vaults. These are valuable — and insufficient. The Enterprise Knowledge Vault doesn’t replace them. It connects and extends them into a governed, curated knowledge layer where agents and humans drink from the same well.

Your Existing Infrastructure

Internal Systems (Keep These)

📄

QMS / VeevaDeviations, SOPs, CAPAs

⚙️

LIMS / MESBatch records, process data

📊

Data Lake / Vector DBYour existing RAG sources

☁️

CRM / ERPKOL interactions, field intel

External Augmentation (Controlled)

🌐

Regulatory FeedsFDA, EMA, ICH — GxP provenance

📊

Market & CompetitiveIQVIA, congress, publications

→

Ingest, Conform & Govern

📦

Extract & NormalizePull from internal + external

📝

Structure as MarkdownYAML frontmatter + body

🔗

Enrich & LinkWikilinks, tags, relations

🛡️

Quality Gate (DoD)Completeness, trust score, dedup

🤖

Curation Agent + ReviewPropose → verify → approve

🌐

Source ProvenanceGxP: external origin tracked

→

Knowledge Store

Obsidian covers this

🗃

Markdown Vault.md files + YAML metadata

🔗

Knowledge Graph[[Wikilinks]] + backlinks

Enterprise extensions

🧰

Vector IndexExtends your existing RAG

🔒

RBAC + Audit TrailWho, what, when — immutable

🔃

Knowledge VersioningWhat changed at concept level, not just file diff

→

Domain Vaults

💊

Clinical VaultProtocols, rationale, amendments

📊

Commercial VaultCompetitive intel, brand strategy

⚙️

Manufacturing VaultProcess knowledge, deviations

📑

Regulatory VaultSubmissions, FDA interactions

🔬

Cross-Domain IndexShared entities, linked context

→

Consumption

🤖

AI Agents (MCP + REST)Multi-protocol: MCP, REST, tool-use

💬

Enterprise ChatbotGrounded in curated context

📊

Dashboards & VisualsKnowledge health, coverage

🧠

ML / Advanced AnalyticsTraining, fine-tuning, RAG+

🗃

Local Editors (Obsidian)Cloud sync for offline editing

📄

Compliance Reports21 CFR Part 11 exports

🛡️ Knowledge Governance — Preventing Garbage In / Garbage Out

Ingest Quality Gate
Every entry validated against a Definition of Done: completeness, source trust score, schema conformance. Unverified content stays in .inbox/ until human-approved.

Knowledge-Level Versioning
Not just “file modified” — semantic change tracking: “dose rationale updated based on Phase 2b results.” Reviewers see what changed about what we know, not what changed in the text.

External Augmentation Control
Regulatory feeds (FDA, EMA, ICH) and market data enter through controlled pipelines with provenance tagging. GxP content requires human verification before vault promotion.

Agent Execution Governance
AI curation agents follow defined execution templates with verification checkpoints — every agent action produces a traceable assessment, not just an output.

All consumers drink from the same curated, governed knowledge well — consistent, versioned, verified

Closing the Enterprise Gaps

Eight Capabilities That Make the Pattern Work at Scale

Five are the recognized requirements for any enterprise vault. Three more are critical for regulated industries and adoption at scale — gaps that surface only when you try to ship this in production.

☁️

Cloud-Hosted & Multi-User

Per-vault cloud storage with concurrent editing. Multi-vault isolation by tenant, real-time change sync via event bus. Designed to scale across sites and departments.

All domains

🔒

Access Control & Permissions

Six-tier RBAC: vault admin, curator, contributor, reviewer, reader, agent. File, folder, and tag-level policies. Confidentiality controls per note. Agent writes go to inbox for human review.

Brand teams need this for LOE strategy docs

📝

Version Control & Audit

Every write creates an immutable version. Diff between any two versions. Blame attribution for every line. Regulatory-grade retention (7+ years).

Critical for GxP deviation investigations

🔌

Multi-Protocol Agent Access

Three interfaces: REST API (universal), MCP Server (agent-native), and tool-use for hosted AI. Four core tools: read, search, write, traverse. Protocol-agnostic design — MCP today, whatever wins tomorrow.

The unlock for agent-accessible knowledge

🤖

Agent-Maintained Curation

Five-stage pipeline: Extract → Structure → Contextualize → Propose → Quality Gate. GxP notes go to inbox; non-GxP can auto-commit. Tiered review model to prevent curator bottleneck (see Curation at Scale).

How dose rationale gets captured before anyone rotates

🛡️

Regulatory Compliance (GxP)

21 CFR Part 11 electronic records with e-signatures bound via two-factor auth. ALCOA+ data integrity. GAMP 5 validation lifecycle (IQ/OQ/PQ). Write-once immutable storage with tamper-proof audit trail. Validated change control process for system updates. Not a feature you bolt on — it shapes the entire architecture from day one.

Pre-approval inspections require this

🔬

Knowledge Graph Intelligence

Beyond simple wikilinks: orphan note detection, freshness scoring, and impact analysis (“what else changes if we change this?”). Cross-domain relationship discovery is the hardest problem in this architecture — it requires deep domain ontology modeling and is a multi-phase investment, not a one-sprint feature.

Endpoint change → downstream impacts surfaced

🔃

Enterprise System Integration

Bidirectional connectors to QMS, LIMS, CRM, data lakes, and regulatory systems. The vault can’t be another silo. Knowledge flows in from batch records and out to compliance reports. Adoption dies without this.

Integration is the adoption accelerator

Applied Architecture — Three Domains

Same Personas, Transformed Outcomes

The same three people. The same questions. But now their AI agents have access to curated, structured, permissioned institutional knowledge — not scattered documents.

👩‍⚕️

Dr. Mira Patel

VP Clinical Development

“Why did we switch from PFS to co-primary endpoint?”

Agent searches the Protocol Intelligence Vault — finds the decision note with full rationale

[[Wikilinks]] traverse to FDA Type B meeting minutes and competitive landscape note

Impact analysis surfaces downstream dependencies: consent language, CRO contracts, enrollment timelines

Mira gets a grounded answer in minutes — with provenance chain and version history

Protocol Intelligence Vault
  protocols/
    ASSET-001-NSCLC-Phase3-v3.2.md         # phase: 3, indication: NSCLC, status: active
  rationale/
    dose-selection-200mg-Q3W.md             # linked to PK/PD model + safety review
    endpoint-switch-PFS-to-OS.md            # FDA feedback + competitive pressure
  amendments/
    amendment-03-futility-interim.md        # why interim moved to 75% enrollment
  regulatory/
    FDA-Type-B-meeting-2025-06.md          # co-primary acceptable per Division
  competitive/
    NSCLC-IO-landscape-2026-Q2.md          # vs KEYTRUDA, TECENTRIQ positioning
  .inbox/
    DSMB-minutes-extraction-2026-Q2.md     # agent-proposed, awaiting curator review

Minutes, Not Days

Target: <15 min vs. 3–5 day baseline

Context retrieval shifts from multi-day reconstruction to structured vault queries — dependent on vault curation maturity

Fewer Avoidable Amendments

Design Risks Surfaced Earlier

Protocol design risks identified through linked impact analysis before they become amendments — the most expensive rework in clinical development

Reduced Knowledge Loss

At Staff Rotation

The “why” behind protocol decisions captured and curated while teams are active — not reconstructed after people leave

💼

James Chen

Head of Brand Strategy

“What changed in the competitive landscape since last QBR?”

Agent queries the Brand Competitive Intelligence Vault for notes modified since last QBR date

Auto-ingested IQVIA market share data already linked to strategy notes and KOL sentiment

RBAC filters out LOE strategy and pricing notes — only role-appropriate content surfaced

James gets a draft competitive briefing with source links — ready for review, not ready to present

Brand Competitive Intelligence Vault
  brand/
    ELIQUIS-position-2026-Q2.md            # TA: cardiology, market_share: 48%
  competitors/
    XARELTO/label-update-2026-03.md        # VTE prophylaxis extension impact
    biosimilar-pipeline.md                  # ANDA timelines, IP litigation status
  payer/
    express-scripts-2026-formulary.md      # preferred over XARELTO in Part D
  kol/
    Dr-Braunwald-sentiment.md              # congress talk, stated preferences
  strategy/
    LOE-strategy-2028.md                   # confidentiality: executive_only
  .inbox/
    iqvia-weekly-market-share-W20.md       # auto-ingested from data lake connector

~30 min Draft

vs. 4+ Hours of Manual Assembly

Competitive briefings auto-assembled from vault changes — still requires human review and editorial judgment before presentation

Fewer Blind Spots

In Competitive Intelligence

Market signals — formulary shifts, label updates, KOL sentiment — captured and linked on arrival, reducing the risk of late discovery

One Curated View

Not 47 Slide Decks

Marketing, medical affairs, market access, field force — all contribute, all consume, one curated view with role-based filtering

⚙️

Sara Rodriguez

Quality Director, Manufacturing

“Has this bioreactor pH excursion happened before? What was the root cause?”

Agent searches the Process Knowledge Vault — finds 3 prior pH excursions across 2 campaigns

Graph traversal links to root causes, CAPAs, and batch records with evidence chain

Tribal knowledge note surfaces: “probe drift after 14-day campaigns — calibrate at day 7”

Sara gets a structured investigation package with linked evidence — ready for review, audit-trail compliant

Process Knowledge & Deviation Intelligence Vault
  processes/upstream/
    bioreactor-pH-control.md               # setpoint: 7.0, range: 6.9-7.1, CPP: yes
  deviations/
    DEV-2025-0847-pH-excursion.md          # root_cause: probe drift after 14-day campaign
  capa/
    CAPA-2025-312-calibration-interval.md  # corrective: 14 → 7 day calibration interval
  tribal-knowledge/
    campaign-length-limits.md               # why 21 days not 28 (2022 deviation cluster)
  tech-transfer/
    chromatography-resin-switch.md          # supply chain risk + comparability data
  .inbox/
    batch-1247-parameter-extraction.md     # auto-extracted from MES on batch close

Hours, Not Days

Target: <2 hrs vs. 2–3 day baseline

Deviation root-cause search across years of history — complete with linked evidence, dependent on vault coverage of historical deviations

Fewer Audit Gaps

From Improved Traceability

Investigations pre-linked and auditable at any time — reducing the scramble before PAIs, though full Part 11 compliance requires validated system deployment

Tribal Knowledge Captured

Before Veterans Retire

The undocumented “why” behind process parameters — captured proactively through structured interviews and agent-assisted curation

The Hard Problem

Curation at Scale: How the Inbox Doesn’t Drown the Curators

If every auto-ingested batch record, IQVIA feed, and regulatory update requires human review, curators become the bottleneck. The architecture addresses this with tiered review — not all knowledge carries equal risk.

✅ Tier 1: Auto-Commit

Non-GxP, low-risk knowledge from trusted pipelines with high-confidence extraction. Examples: public regulatory guidance updates, market share data from validated feeds, congress abstract summaries. Agent commits directly; human audit is sampling-based (10–15%).

Example: Weekly IQVIA market share data auto-linked to brand notes. Audited monthly.

🛠️ Tier 2: Light Review

Medium-risk knowledge where the agent structures and proposes, but a curator does a pass/fail review (not a full rewrite). Target SLA: 48 hours. Escalation trigger if inbox exceeds threshold. Examples: competitive intelligence summaries, internal meeting action items, field medical notes.

Example: Agent extracts KOL sentiment from congress report. Curator confirms or flags in <48h.

🛡️ Tier 3: Full GxP Review

High-risk, regulated knowledge requiring full human verification, e-signature, and audit trail. Examples: deviation root causes, protocol rationale, dose selection decisions, CAPA effectiveness. No auto-commit. Full review with subject-matter-expert sign-off.

Example: Agent proposes deviation root cause from batch data. SME reviews, edits, signs.

Volume Management

In a mature deployment, we estimate 70–80% of ingested knowledge falls into Tier 1 (auto-commit with audit sampling), 15–20% into Tier 2 (light review), and 5–10% into Tier 3 (full GxP review). The architecture must be calibrated to each organization's risk tolerance — conservative organizations may start with more Tier 2/3 and shift toward Tier 1 as confidence in extraction quality grows. Backlog dashboards and escalation rules are part of the governance layer, not an afterthought.

Proven Domain Patterns

What We’ve Built — And What It Proves (and Doesn’t)

Two production systems that follow the vault pattern: curated, structured knowledge that AI agents read, search, traverse, and cite. These validate the domain knowledge model. They do not yet prove the full enterprise platform described above — they are the starting point, not the finish line.

Domain Knowledge Vault — Clinical Trial Compliance

ProtoCheck

A curated vault of 500+ regulatory citations across FDA, EMA, ICH, and WHO — structured as rules with YAML metadata, linked to source regulations, and traversed by AI agents that check clinical trial protocols for compliance gaps. Every finding traces back through the vault to its regulatory source.

Markdown-native rules YAML metadata per citation Agent traversal & search Evidence provenance Version-controlled Patent pending

Domain Knowledge Vault — FDA Inspection Intelligence

CiteTrace

63,000+ enriched FDA 483 observation vectors mapped to 503 CFR sections and 96 ICH guidelines. Agents query the vault to surface facility risk scores, root-cause patterns, and regulatory precedent — every answer linked to its source in the FDA’s own corpus. The vault compounds as new 483s are ingested and auto-linked.

63K+ structured vectors 503 CFR sections mapped Agent-accessible API Auto-ingestion pipeline Semantic + graph search Knowledge compounds over time

What These Validate

✓ Markdown-native knowledge stores work for pharma domains
✓ AI agents can traverse and cite structured vault content
✓ Evidence provenance chains are achievable and valuable
✓ Auto-ingestion with quality gates produces reliable knowledge
✓ Expert calibration loops improve curation quality over time

What Remains to Be Proven at Enterprise Scale

○ Multi-tenant cloud hosting with concurrent editing at scale
○ Full 21 CFR Part 11 validated deployment with e-signatures
○ Bidirectional connectors to enterprise systems (Veeva, SAP, etc.)
○ Cross-domain ontology alignment and relationship discovery
○ Curation throughput at enterprise volume (thousands of entries/week)

Implementation Approach

Phased Delivery, Honest Complexity

Phase 0 establishes the vault standards baseline. Then four build phases deliver on those standards, each increasing in complexity. This is a multi-year architecture investment, not a quarterly project. We show estimated timelines because you deserve to plan with real numbers.

Phase 0 Vault Standards Baseline

Before any vault is built, establish the cross-cutting standards every knowledge vault inherits. This is the master pattern index — naming conventions, metadata schemas, governance contracts, compliance mappings, and operational patterns that ensure consistency across Clinical, Commercial, Manufacturing, and every future domain vault.

Pattern Catalog & Templates

Master index of vault design patterns (naming, structure, linking)
Reusable pattern template for new vault operations
YAML frontmatter schema (metadata, tags, provenance fields)
Pattern dependency graph across vaults
Self-improving knowledge taxonomy (grows with every document ingested)

Compliance & Governance Contracts

Regulatory compliance matrix (21 CFR 11, ALCOA+, ICH mapped to patterns)
Multi-dimension quality evaluation (precision, consistency, evidence chain)
Quality gate definitions & curation workflow standards
RBAC model baseline (6-tier roles, vault-level permissions)
Audit trail schema & immutable record conventions

Operational Standards

Knowledge versioning conventions (semantic change, not file diff)
Reproducibility contracts (automated determinism gates on vault operations)
Cross-cutting principles: soft-delete only, record-level versioning
Validation framework baseline (IQ/OQ/PQ for vault operations)
Agent interaction contracts (MCP tool schemas, access policies)

Pre-Build • 4–8 weeks • Standards That Every Vault Inherits

↓ ↓ ↓

Already Built These Practices Are Running in Production Domain Vaults Today

11-Dimension Quality Framework

Automated evaluation covering precision, recall, cross-domain consistency, evidence chain integrity, and confidence calibration — 9 quality gates enforced on every pipeline run.

GxP Reproducibility Gate

Same input produces same output (≥95% overlap verified). Automated 8-gate verification suite with formal 21 CFR Part 11 qualification — patent pending.

Self-Improving Knowledge Taxonomy

4-phase lifecycle: Seed → Expand → Polymorphism → Validate. Structural intelligence that grows with every document ingested, no manual mapping required.

Expert Calibration Loop

Domain expert annotations feed back into quality baselines. 67 knowledge rules calibrated with inter-rater reliability tracking and ground-truth methodology.

Catalog Taxonomy What Lives in Each Vault — Organized for Humans and Agents

Every knowledge entry carries domain, category, and sub-category metadata — enabling both people and AI agents to navigate, discover, and audit vault contents across the enterprise.

🏥 Clinical Vault

Domains: FDA • ICH • EMA • WHO • CDISC

Safety Monitoring — AE reporting, DSMB, dose-limiting toxicity

Study Design — endpoints, stratification, adaptive designs

Regulatory Submissions — IND/NDA filings, agency correspondence

Informed Consent — ICF templates, site-specific amendments

Biostatistics — SAP, interim analysis, multiplicity

Data Management — CRF standards, edit checks, CDISC mappings

By therapeutic area: Oncology • Cardiology • Immunology • CNS • Rare Disease

📈 Commercial Vault

Domains: Market Intel • Medical Affairs • Managed Markets

Competitor Pipelines — phase tracking, readouts, approvals

Market Access — payer landscape, formulary position, HEOR

KOL Intelligence — engagement history, publication impact

Launch Readiness — market shaping, field alignment, channel mix

Pricing & Contracting — gross-to-net, rebate structures, 340B

By therapeutic market: Oncology • Immunology • Rare Disease • Cardiometabolic

⚙️ Manufacturing Vault

Domains: Quality Systems • Process Eng. • GMP • Supply Chain

Deviations & CAPA — root cause, trend analysis, effectiveness

Process Parameters — CPPs, proven acceptable ranges, hold times

Equipment Qualification — IQ/OQ/PQ, calibration, PM schedules

Batch Records — MBR/EBR, yield trends, in-process controls

Supply Chain — vendor qualification, COAs, cold chain

By product line & site: Biologics • Small Molecule • Cell & Gene

🔗 Cross-Functional

Domains: Enterprise-Wide • Shared Standards • Integration

Regulatory Intelligence — guidance tracking across agencies

Decision Patterns — cross-domain playbooks, escalation logic

Validation Standards — CSV frameworks, 21 CFR 11, data integrity

Training & Onboarding — role-specific knowledge paths

Audit Readiness — inspection playbooks, CAPA history, mock audits

Linked across all vaults • Agent-navigable via MCP

Phase 1

Foundation

Cloud vault storage (markdown-native)
Metadata index & full-text search
REST API (CRUD + search + version)
Authentication & 6-tier RBAC
Version control engine

3–5 months • Core Platform

Phase 2

Intelligence

MCP Server + REST fallback (4 core tools)
Semantic search (vector embeddings)
Graph traversal API (wikilinks)
Curation Agent (propose + tiered review)
Change event bus (real-time sync)

4–6 months • Agent Layer

Phase 3

Enterprise

Multi-vault isolation
21 CFR Part 11 audit trail + e-signatures
External connectors (QMS, CRM, LIMS)
Intra-domain knowledge graph
Quality gate pipeline + backlog mgmt

6–9 months • Regulated Deployment

Phase 4

Scale

Local editor sync (Obsidian, VS Code)
Data lake bidirectional export
Vault health analytics & freshness
Cross-domain knowledge graph (hardest)
Validation (IQ/OQ/PQ) + GAMP 5

6–12 months • Full Enterprise Scale

A Note on Honest Complexity

Total estimated timeline: 18–30 months from Phase 0 through Phase 4, depending on enterprise size, regulatory requirements, and the number of domain vaults. This is a significant architecture investment. Phase 3 (GxP compliance) alone requires a validated system lifecycle with IQ/OQ/PQ, SOPs for system use, and a change control process that survives FDA inspection. Cross-domain knowledge graph intelligence (Phase 4) is an open research problem in ontology alignment — we scope it as iterative, not deterministic. We believe the investment is justified by the scale of the knowledge loss problem. But we also believe you deserve honest timelines, not optimistic ones.

Your Best People Rotate Off Programs. Their Knowledge Leaves With Them.

Three Personas, One Pattern: Knowledge Trapped in People and Silos

A Pattern That Works — For One Person on One Machine

Cloud-Hosted & Multi-User

Access Control & Permissions

Version Control & Audit

MCP Server / Standard API

Agent-Maintained Curation

Regulatory Compliance (GxP)

Knowledge Graph Intelligence

Enterprise System Integration

What This Architecture Is — and Isn’t

🚫 This Is Not

✅ This Is

Your Enterprise Already Has These Systems — None of Them Solve This Problem

Veeva Vault

Palantir Foundry

Confluence / Notion

RAG / Vector DB

You Already Have the Foundation — This Is the Next Layer

Eight Capabilities That Make the Pattern Work at Scale

Cloud-Hosted & Multi-User

Access Control & Permissions

Version Control & Audit

Multi-Protocol Agent Access

Agent-Maintained Curation

Regulatory Compliance (GxP)

Knowledge Graph Intelligence

Enterprise System Integration

Same Personas, Transformed Outcomes

The Outcomes This Architecture Enables

Curation at Scale: How the Inbox Doesn’t Drown the Curators

✅ Tier 1: Auto-Commit

🛠️ Tier 2: Light Review

🛡️ Tier 3: Full GxP Review

What We’ve Built — And What It Proves (and Doesn’t)

ProtoCheck

CiteTrace

Phased Delivery, Honest Complexity

Pattern Catalog & Templates

Compliance & Governance Contracts

Operational Standards

Foundation

Intelligence

Enterprise

Scale

A Note on Honest Complexity

The Architecture Pattern Is Defined. The Domain Expertise Is Real. The Hard Pieces Have Started.