Full Technical Architecture Blueprint (SpaceArch Markets–Ready)
0) Design Principles
- Entity-first, article-second
Content originates as structured entities and relationships.
The article is merely a human-readable view of the knowledge graph. - Dual-layer publishing
- Human-readable layer (HTML / Markdown)
- Machine-readable layer (JSON-LD + APIs + structured feeds)
- Semantic stability
Controlled vocabularies and versioned taxonomies.
No uncontrolled tagging systems. - Provenance & auditability
Each data point must include:
- Source
- Timestamp
- Verification status
- Responsible editor
- Change history
- Interoperability
Schema.org + JSON-LD + RSS/Atom + sitemaps + OpenGraph + APIs.
1) System Layers (High-Level Architecture)
A. Ingestion Layer (Input)
Sources:
- Structured submission forms (companies / institutions / founders)
- Structured interviews (Q&A format)
- Uploaded documents (PDF, pitch deck, legal filings)
- Public web sources (if enabled)
- Institutional data partners
Output:
- Raw claims + metadata (who said what, when, with what evidence)
B. Knowledge Layer (Canonical Truth)
Core: Knowledge Graph + Entity Store
Entities:
- Company
- Person
- Product
- Project
- Institution
- City
- Sector
- Deal
- Patent
- Event
- Regulation
- TradeRoute
Relationships:
- founded_by
- located_in
- exports_to
- partners_with
- funded_by
- member_of
- regulated_by
- competes_with
Features:
- Bitemporal versioning (valid_from / valid_to + recorded_at)
- Confidence scoring
- Source attribution
- Editorial approval states
This is the system’s single source of truth.
C. Publishing Layer (Output)
Three simultaneous outputs:
- Web interface (human UI)
- Machine layer (JSON-LD + structured blocks)
- Feeds and APIs (for AI systems and partners)
D. Intelligence Layer (AI Operations)
- Automatic classification
- Entity linking
- Duplicate detection
- Embedding generation
- Semantic search (vector + keyword hybrid)
- RAG-based structured responses
- Trend detection by sector / city / corridor
2) Core Data Model
2.1 Example Entity: Company (Minimum Schema)
- id (UUID)
- legal_name
- brand_name
- country
- city
- coordinates
- primary_sector (controlled vocabulary)
- secondary_sectors[]
- stage (idea, MVP, seed, growth, mature)
- business_model (B2B, B2C, B2G, marketplace)
- products[]
- tech_stack[]
- certifications[]
- website
- social_profiles[]
- export_markets[]
- investment_readiness_score (0–100)
- corridor_fit_score (Miami / Dubai / MDQ)
- last_verified_at
- sources[] (per field)
- editorial_state (draft, verified, published)
2.2 Claim-Based Storage Model
In addition to the finalized entity profile, the system stores individual claims:
- claim_id
- entity_id
- field
- value
- source
- evidence_url or document_id
- confidence_score
- created_by
- approved_by
- timestamps
Benefits:
- Full auditability
- Error correction
- Transparency
- Reduced hallucination risk
- Structured AI retraining capability
3) Semantic Layer (Ontology + Taxonomy)
Controlled Taxonomies
- Sector taxonomy (2–4 hierarchical levels)
- Technology taxonomy
- Corridor taxonomy (trade, capital, real estate, legal/IP)
- Editorial content taxonomy (profiles, dossiers, reports, regulatory updates)
Versioning
- taxonomy_version (v1.0, v1.1…)
- Controlled migrations
- Deprecation management
4) AI-First Content Types
Instead of generic articles, define canonical structured formats:
- Entity Profile (Company / Person / Institution)
- Ecosystem Node (City overview)
- Sector Dossier
- Trade Corridor Brief
- Investment Readiness Memo
- Regulatory Tracker
- Case Study (timeline + KPIs)
Each type includes:
- Required fields
- JSON schema
- HTML rendering template
- Embedded structured data
5) Publishing Stack (Human + Machine)
Human Layer
- Server-side rendering (SSR) or static generation (SSG)
- Stable URLs:
- /entities/company/<slug>
- /dossiers/sector/<slug>
- /corridors/miami-dubai-mdq/<slug>
- All content rendered directly from the Knowledge Layer
Machine Layer (Embedded in Each Page)
Required:
- JSON-LD (Schema.org compliant)
- Microdata (optional)
- OpenGraph metadata
- Canonical URLs
- lastmod field
- Segmented sitemaps
Minimum JSON-LD for Entity
- @type (Organization, Person, Place, Event)
- name
- url
- sameAs
- location
- description
- additionalProperty (extended attributes)
- mainEntityOfPage
Feeds
Human:
- RSS / Atom
Machine:
- entity_updates.json
- corridor_briefs.ndjson
- sector_dossiers.json
Structured AI feeds allow real-time semantic ingestion.
6) API Layer (Commercial Infrastructure)
Public API (Rate Limited)
- /api/entities/search
- /api/entities/<id>
- /api/dossiers/<id>
- /api/feeds/latest
Premium API (Revenue Model)
- /api/corridor/pipeline
- /api/entities/verified
- /api/alerts
- /api/graphs/subgraph
Delivery options:
- REST
- GraphQL (recommended for graph-based queries)
- Webhooks (real-time updates)
- Bulk dataset exports (CSV / JSON)
7) AI Layer (Operational Intelligence)
7.1 Embeddings
Embeddings generated for:
- Entity summaries
- Article narratives
- Claims (evidence-level granularity)
Hybrid search:
Keyword + Vector similarity
7.2 Entity Linking & Deduplication
Matching criteria:
- Legal name
- Domain
- Registration number
- Social profiles
- Geographic location
Duplicate prevention is mission-critical.
7.3 Internal RAG Engine
Use cases:
- Journalists
- Analysts
- Premium subscribers
Every answer must include:
- Source citations
- Confidence score
- Last verification timestamp
8) Editorial Workflow
States:
- Draft (AI-assisted)
- Editorial review
- Verification (evidence validation)
- Publish
- Monitor & update
Roles:
- Researcher
- Editor
- Verifier
- Publisher
- Data steward
9) Governance, Trust & Compliance
Data Quality Controls
- Mandatory required fields
- Automated validation checks
- Link validation
- Taxonomy consistency validation
- “No source, no publish” for critical claims
Legal & Ethical Safeguards
- PII separation
- Interview consent documentation
- Correction/opt-out procedures
- Non-advisory disclaimers
10) Observability & Metrics
Technical KPIs
- Structured data validity rate
- Entity duplication rate
- Time-to-publish
- Crawl success rate
- Update frequency per entity
Business KPIs
- Inquiries per entity
- Corridor conversion rate
- Premium API subscriptions
- Institutional contracts
- Dossier sponsorship revenue
11) Recommended Technology Stack (Scalable & Realistic)
Storage:
- PostgreSQL (entities + claims)
- Object storage (documents/media)
- Vector database (embeddings)
Graph:
- Neo4j or graph model in PostgreSQL
- RDF store optional for Linked Data expansion
Search:
- OpenSearch / Elasticsearch
- Hybrid with vector DB
Publishing:
- Next.js (or equivalent SSR framework)
- Headless CMS with strict schema enforcement
AI Services:
- Structured extraction pipelines
- Validation prompts
- Template-constrained summarization
12) Implementation Roadmap
Phase I – MVP (6–10 Weeks)
- Entity store + claim tracking
- 3 structured content types
- JSON-LD integration
- RSS + AI JSON feed
- Basic workflow
- Hybrid search
Phase II – Monetization
- Premium API
- Webhooks
- Investment readiness scoring
- Corridor pipeline dashboard
- Institutional accounts
Phase III – Expansion
- Multi-city replication
- Automated ingestion partnerships
- Linked Data exports
- B2B agent layer
13) AI-Native Article Template
Each publication includes:
- Executive summary (100–150 words)
- Structured entity block
- Evidence block (sources + timestamps)
- Contextual ecosystem links
- Corridor relevance analysis
- Update log (changelog)
- Embedded JSON-LD
Strategic Clarification
This is not “news for AI.”
It is curated semantic infrastructure.
You are not publishing content.
You are publishing structured economic reality.
That is a fundamentally different market category.
