Illustrative Example
This example demonstrates the classification and scoring methodology applied to a real signal. Classification and scoring are performed manually during the 12-stage refresh protocol (see Refresh Protocol chapter).
Source Articles Identified
News from defined sources is reviewed for coverage universe relevance:
| NVIDIA Newsroom | NVIDIA and Accenture Expand Partnership to Accelerate Enterprise AI Adoption |
| Reuters | Accenture to train 30,000 consultants on NVIDIA AI Enterprise platform |
| CRN | Channel partners eye new opportunities as NVIDIA-Accenture deal deepens |
| Bloomberg | AI consulting race heats up as Accenture doubles down on NVIDIA |
Classification Output
Each article is classified using the scoring model:
Article: "NVIDIA and Accenture Expand Partnership..."
Source: NVIDIA Newsroom (Tier 1)
Primary Theme: GSI Ecosystem Movements
Secondary Themes: AI Infrastructure & Partnerships
Entities: NVIDIA, Accenture, AI Enterprise, DGX Cloud
Confidence: 5 (Tier 1 source)
Impact: 5 (reshapes ecosystem)
Irreversibility: 4 (multi-year commitment)
Signal Type: Announcement
Signal Score: 45 [5 × (5 + 4)]
Summary: NVIDIA and Accenture expanding partnership to deploy
AI solutions across enterprise clients, including
consultant training and joint go-to-market.
Theme Synthesis Generated
During the refresh cycle, all "GSI Ecosystem Movements" articles are synthesized into key developments, pattern analysis, strategic implications, and watch items (see Synthesis chapter).
Alert Triggered
Because the NVIDIA-Accenture article scored 45 (≥30 threshold), it is flagged as a high-priority alert in The Digest and added to the ALERTS[] array during the refresh cycle.
Architecture Overview
Current Implementation
The platform operates as a self-contained analytical application with manual curation workflow, client-side computation, and integrated data validation:
┌─────────────────────────────────────────────────────────────────────────┐
│ MANUAL CURATION (12-Stage Refresh Protocol) │
│ News Monitoring │ Source Review │ Relevance Assessment │
│ Entity Activation (L1/L2/L3) │ Content Gates │ Progress Markers │
└──────────────────────────────────┬──────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────────────┐
│ CLASSIFICATION & SCORING │
│ Theme Assignment │ Entity Tagging │ Impact Scoring │ Summary │
│ Temporal Decay: Score_adj = Score × e^(-λ × age_days) │
└──────────────────────────────────┬───────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────────────┐
│ JSON DATA LAYER (loader.js 4-phase pipeline) │
│ SIGNALS[] │ THEMES[] │ ALERTS[] │ COMPANIES[] │ TRENDS{} │
│ THEME_SYNTHESES{} │ COVERAGE_STATS{} │ COMPANY_DATA{} │
│ COMPANY_PROFILES{} │ DOSSIERS{} │ REPORT_MANIFEST[] │
│ config/platform.json │ config/reports.json │ data/entities/*.json │
└──────────────────────────────────┬───────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────────────┐
│ ANALYTICAL ENGINE (Client-Side) │
│ Temporal Decay Ranking │ Monte Carlo Simulation (5K iterations) │
│ Convergence Diagnostics │ Heavy Cycle Detection │ Event Impulse │
└──────────────────────────────────┬───────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────────────┐
│ VALIDATION & INTEGRITY │
│ Schema Validation │ Range Checks │ Cross-Reference Integrity │
│ Sensitivity Analysis (±5 offsets) │ Methodology Staleness Audit │
└──────────────────────────────────┬───────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────────────┐
│ CLIENT-SIDE RENDERING │
│ Dynamic HTML Generation │ Chart.js Visualization │ Filter/Sort │
│ Interactive Navigation │ Dynamic Stat Derivation │
└──────────────────────────────────────────────────────────────────────────┘
Key Architectural Properties
| Property | Implementation |
|---|
| SPA + Serverless | Vanilla ES5 single-page application with AWS backend (Cognito, Lambda, DynamoDB, API Gateway). Passwordless auth via magic link tokens. Access gated by auth/gate.js. |
| Data-driven | All content loaded from JSON files via async loader.js pipeline. Views are projections of data, not hardcoded HTML. |
| Analytically active | Temporal decay, Monte Carlo simulation, convergence diagnostics, and sensitivity analysis run client-side on page load. |
| Schema-validated | validateData() checks structure, ranges, and cross-references on page load. Results logged to console; rendering is not gated on validation pass. |
| Methodology-aware | METHODOLOGY_REGISTRY tracks documentation freshness. Dynamic stats derived from live data arrays. |
Target Architecture (Automated Pipeline)
The target architecture replaces manual curation with an automated ingestion and classification pipeline. The analytical engine and rendering layers would carry forward largely unchanged.
┌─────────────────────────────────────────────────────────────────────────┐
│ SOURCE LAYER │
│ RSS Feeds │ News APIs │ Press Releases │ Filings │ Blogs │
└──────────────────────────────────┬──────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────────────┐
│ INGESTION PIPELINE │
│ Fetch → Dedupe → Parse → Normalize → Store │
└──────────────────────────────────┬───────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────────────┐
│ CLASSIFICATION ENGINE │
│ Theme Assignment │ Entity Extraction │ Sentiment │ Relevance │
└──────────────────────────────────┬───────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────────────┐
│ SYNTHESIS LAYER │
│ Per-Theme Aggregation │ Cross-Source Comparison │ Trend Detection │
└──────────────────────────────────┬───────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────────────┐
│ OUTPUT LAYER │
│ Static Site │ Executive Briefings │ Alerts │ Trend Reports │
└──────────────────────────────────────────────────────────────────────────┘
Source Definition Framework
Source Tiers
Not all sources carry equal weight. Tiering helps prioritize signal over noise:
| Tier | Description | Examples | Confidence |
|---|
| 1 | Primary / First-party | Company newsrooms, SEC filings, official blogs, PRNewswire/BusinessWire (official releases) | 5 |
| 2 | Major Analysis | Reuters, Bloomberg, WSJ, FT | 4 |
| 3 | Trade Press | CRN, Channel Futures, SiliconANGLE, The Information | 3 |
| 4 | Research (Public) | Gartner, IDC, Forrester snippets | 2 |
Note: Wire services (PRNewswire, BusinessWire, GlobeNewswire) are classified as Tier 1 when distributing official company announcements. Confidence score range is 2-5; a score of 1 is not used in practice.
Recommended Sources by Category
Silicon Partners: NVIDIA Newsroom, Intel Newsroom, AMD News, Qualcomm News, Arm Newsroom
Hyperscalers: AWS News Blog, Google Cloud Blog, Microsoft Azure Blog
GSI / Consulting: Accenture Newsroom, Deloitte Press, IBM Newsroom, Infosys News
Competitive Set: Dell Technologies Newsroom, HPE Newsroom
General Tech/Business: Reuters Technology, Bloomberg Technology, WSJ Tech, FT Technology
Current Source Management
Sources are tracked as a narrative list within this chapter. No structured SOURCES[] array exists in the current implementation. The footer references "54 news sources," which requires manual verification when sources are added or removed during refreshes.
Target Source Configuration Schema
The automated pipeline will store sources as structured records:
{
"source_id": "nvidia_newsroom",
"source_name": "NVIDIA Newsroom",
"url": "https://nvidianews.nvidia.com/rss",
"tier": 1,
"category": "silicon",
"refresh_interval_minutes": 60,
"relevance_keywords": ["partnership", "alliance", "collaboration"],
"enabled": true
}
Coverage Universe
The monitoring system tracks 69 entities across 10 industry segments. Signals mentioning these entities receive priority classification and scoring.
Hyperscalers
| Company | Notes |
|---|
| Google (incl. Gemini) | Cloud + TPU-based AI infra, foundation models, enterprise AI |
| Meta | Social + consumer AI, custom MTIA inference silicon |
| Microsoft (Azure) | Cloud + Copilot, Maia/Cobalt custom AI silicon |
| Amazon / AWS | Cloud + Bedrock, Trainium/Inferentia AI accelerators |
| Oracle (OCI) | Enterprise-focused AI cloud, database-led workloads |
Global System Integrators (GSIs)
| Company | Notes |
|---|
| Accenture | Large-scale AI transformation, co-innovation with CSPs |
| Deloitte | AI consulting, risk, and enterprise transformation |
| PwC | Assurance, tax, and AI-enabled transformation |
| IBM | Hybrid cloud + AI services (Consulting, watsonx) |
| KPMG | Assurance and advisory with AI focus |
| EY | Tax, assurance, and AI advisory |
| Capgemini | Global delivery + AI/industry solutions |
| TCS | Large-scale IT + AI services |
| Infosys | AI and automation-led managed services |
| Wipro | Cloud + AI and engineering services |
| NTT DATA | Global SI with AI/infra focus |
Silicon – Merchant CPUs/GPUs
| Company | Notes |
|---|
| Intel | CPUs, GPUs, Gaudi AI accelerators |
| NVIDIA | GPUs, networking, full-stack AI platform |
| AMD | GPUs, AI accelerators (Instinct) |
| Qualcomm | Edge/phone AI, NPUs |
| ARM | CPU IP for AI-enabled SoCs |
| Broadcom | Networking and custom ASICs for AI DCs |
Silicon – Hyperscaler Custom
| Company / Chip | Notes |
|---|
| Google – TPU | Custom training/inference ASICs for Google Cloud |
| Amazon – Trainium / Inferentia | Custom training/inference ASICs for AWS |
| Microsoft – Maia / Cobalt | Custom AI accelerator + CPU for Azure |
| Meta – MTIA | Inference accelerators for Meta's workloads |
Silicon – AI Accelerator Startups
| Company | Notes |
|---|
| Cerebras | Wafer-scale engines for large-model training |
| Groq | LPU-based, low-latency inference hardware |
| SambaNova | Dataflow AI accelerators for enterprise/gov |
| Graphcore | IPU accelerators (specialized AI compute) |
| Tenstorrent | RISC-V–based AI compute platform |
Foundry & Memory (AI-Enabling)
| Company | Notes |
|---|
| TSMC | Advanced-node foundry for leading AI chips |
| Samsung Electronics | HBM/memory and foundry for AI silicon |
| Micron | HBM and DRAM for AI workloads |
KSA / Sovereign Actors
| Entity | Notes |
|---|
| PIF | Capital allocator into global AI/infra |
| HUMAIN | KSA-linked AI initiative |
Infrastructure (Colo / Data Center / GPU Cloud)
| Company | Notes |
|---|
| Equinix | Global colocation + interconnect for AI clouds |
| Digital Realty | Data center and interconnect for AI workloads |
| CoreWeave | GPU cloud provider, NVIDIA $2B investment, 5GW AI factory build-out |
| Lambda Labs | GPU cloud for ML training and inference |
| Crusoe Energy | Sustainable AI infrastructure, clean-energy data centers |
AI-Native (Model Labs / Platforms)
| Company | Notes |
|---|
| OpenAI | Foundation models, ChatGPT / platform |
| Anthropic | Claude models, enterprise safety-focused AI |
| xAI | Model lab with X ecosystem integration |
| DeepSeek | Cost-optimized LLMs, alternative stack |
| Mistral | Open-weight and enterprise-focused models |
| Cohere | Enterprise language models and APIs |
| Hugging Face | Model hub, tooling, and ecosystem |
Services / Enterprise Control Planes
| Company | Notes |
|---|
| ServiceNow | Workflow + AI system of action |
| Databricks | Lakehouse + ML/AI platform |
| Snowflake | Data cloud + AI/ML workloads |
| Palantir | Data/decision platform; vertical AI |
| SAP | ERP + AI business processes |
| Oracle (Apps) | SaaS + database + AI embedded in applications |
| Rubrik | Data security/backup as AI-resilient control plane |
| Veeam | Backup/DR for AI-era infra |
| Salesforce | Data Cloud + Einstein "system of intelligence" |
| Celonis | Process mining / execution management decision layer |
| UiPath | Automation and agentic orchestration |
| Blue Yonder | AI-native supply chain and planning |
OEM Competitive Set
| Company | Notes |
|---|
| Dell Technologies | AI Factory, PowerEdge, enterprise infrastructure |
| HPE | GreenLake, AI infrastructure, HPC |
| Supermicro | GPU-optimized servers, liquid cooling |
| Cisco | Networking, data center infrastructure |
Theme Taxonomy Design
Principles
Themes should be:
- Distinct enough for clear classification
- Comprehensive enough to capture all relevant signals
- Aligned to strategic priorities (not generic news categories)
- Stable over time to enable trend analysis
Theme Definitions
- AI Infrastructure & Partnerships
- Training infrastructure deals, inference deployment partnerships, and AI platform integrations. Tracks how silicon vendors and platform providers are partnering to deliver enterprise AI capabilities.
- Hyperscaler Dynamics
- Cloud provider strategy shifts, on-premises and hybrid positioning, and partner program changes. Monitors AWS, Azure, and Google Cloud competitive positioning and partnership structures.
- GSI Ecosystem Movements
- Practice launches and investments, technology partnerships, and go-to-market alignment across major consulting firms. Tracks Accenture, Deloitte, IBM Consulting, and others.
- Silicon Roadmap & Supply
- Product announcements, supply and capacity signals, and competitive positioning from NVIDIA, Intel, AMD, and other semiconductor vendors.
- Competitive Alliance Activity
- Partnership announcements and alliance strategy moves from Dell, HPE, and other OEMs. Direct competitive intelligence for alliance strategy.
- Channel & Route-to-Market
- Partner program changes, distribution shifts, and as-a-service model evolution. Tracks how technology reaches end customers.
- Regulatory & Policy
- Trade and tariff developments, AI governance initiatives, and antitrust activity. Policy context affecting partnership and market strategy.
- Market Signals
- Demand indicators, pricing dynamics, and segment shifts. Broader market context for strategic planning.
Tuning Over Time
The taxonomy is intentionally stable to support trend analysis, but adjustments are expected as the landscape evolves. Themes with persistently low article counts may be candidates for merging, while themes that consistently exceed coverage capacity may warrant splitting. Any taxonomy changes are tracked in METHODOLOGY_LOG to preserve analytical continuity.
Classification Engine
What Classification Produces
| Field | Description |
|---|
| Primary Theme | Single best-fit theme |
| Secondary Themes | Up to 2 additional relevant themes |
| Entities | Companies, products, people mentioned |
| Confidence | 2-5, derived from source tier (Tier 1=5, Tier 2=4, Tier 3=3, Tier 4=2) |
| Impact | 1-5, magnitude of ecosystem effect |
| Irreversibility | 1-5, duration/permanence of signal |
| Signal Type | Announcement, Analysis, Speculation, or Background |
| Summary | 2-sentence synthesis |
Classification Prompt Template
You are classifying news content for a strategic alliance intelligence system.
ARTICLE:
Title: {title}
Source: {source} (Tier {tier})
Date: {date}
Content: {content}
THEMES:
1. AI Infrastructure & Partnerships
2. Hyperscaler Dynamics
3. GSI Ecosystem Movements
4. Silicon Roadmap & Supply
5. Competitive Alliance Activity
6. Channel & Route-to-Market
7. Regulatory & Policy
8. Market Signals
TASK:
1. Assign PRIMARY THEME (single best fit)
2. Assign up to 2 SECONDARY THEMES if clearly relevant
3. Extract ENTITIES mentioned (companies, products, people)
4. Rate IMPACT (1-5): Magnitude of ecosystem effect
1 = Minor/routine 3 = Notable shift 5 = Ecosystem-reshaping
5. Rate IRREVERSIBILITY (1-5): Duration/permanence
1 = Easily reversed 3 = Medium-term commitment 5 = Long-term/structural
6. Identify SIGNAL TYPE: Announcement | Analysis | Speculation | Background
Note: CONFIDENCE is derived from source tier (Tier 1=5, Tier 2=4, Tier 3=3, Tier 4=2)
Return JSON:
{
"primary_theme": "",
"secondary_themes": [],
"entities": [],
"impact": 0,
"irreversibility": 0,
"signal_type": "",
"summary": ""
}
Signal Scoring Model
The scoring model produces a single composite score that enables ranking and prioritization across all signals.
Formula
Signal Score = Confidence × (Impact + Irreversibility)
Range: 2 to 50
High-priority threshold: ≥30
Scoring Dimensions
| Dimension | Scale | Criteria |
|---|
| Confidence |
1-5 |
Derived from source tier:
Tier 1 = 5, Tier 2 = 4, Tier 3 = 3, Tier 4 = 2 |
| Impact |
1-5 |
1 = Minor/routine announcement
2 = Incremental development
3 = Notable strategic shift
4 = Significant ecosystem effect
5 = Ecosystem-reshaping event |
| Irreversibility |
1-5 |
1 = Easily reversed (pilot, MOU)
2 = Short-term commitment (<1 year)
3 = Medium-term (1-3 years)
4 = Long-term contract (3+ years)
5 = Structural/permanent (M&A, major capex) |
Example Calculation
Signal: "NVIDIA and Accenture Expand Partnership"
Source: NVIDIA Newsroom (Tier 1)
Confidence: 5 (Tier 1 source)
Impact: 5 (reshapes GSI-silicon ecosystem)
Irreversibility: 4 (multi-year training commitment, 30K consultants)
Score = 5 × (5 + 4) = 45
Interpretation: High-priority signal warranting immediate attention.
Score Interpretation
| Score Range | Priority | Color | Action |
|---|
| 40-50 | Critical | Claret (#9F1D35) | Immediate alert, leadership briefing |
| 30-39 | High | Mandarin (#FF8833) | Same-day review, stakeholder notification |
| 20-29 | Medium | Wheat (#F2DFCE) | Include in 48-hour digest |
| 10-19 | Low | Grey (#E6D9CE) | Weekly summary only |
| 2-9 | Background | — | Archive for reference |
Visual Examples
Signal scores appear throughout the platform with consistent color coding:
Score: 48
Score: 45
Score: 38
Score: 32
Score: 28
Score: 22
Score: 15
Temporal Decay Adjustment
For ranking and display ordering, signals are adjusted by a time-decay factor that reduces the weight of older signals while preserving the raw score for reference:
Score_adjusted = Score_raw × e^(-λ × age_days)
Where:
λ (lambda) = decay rate constant (default: 0.03)
age_days = days between signal date and reference date
Half-life at λ = 0.03: ~23 days (signal retains 50% weight)
Quarter-life: ~46 days (signal retains 25% weight)
Temporal decay is configurable via COVERAGE_STATS.temporalDecay:
| Parameter | Default | Description |
|---|
| enabled | true | Toggle decay on/off |
| lambda | 0.03 | Decay rate constant. Higher = faster decay |
| referenceDate | Current date | ISO date string. All ages calculated relative to this |
Design rationale: Raw scores remain the permanent record of a signal's assessed importance. Temporal decay affects only ranking within views (Digest, Notable Coverage). This prevents stale signals from crowding out fresh developments while preserving the analytical judgment embedded in the raw score.
Note: The lambda parameter (0.03) was set by editorial judgment based on the typical news cycle cadence for alliance intelligence. External calibration against outcomes has not been performed.
Condensed Event Summary Specification
Executive briefings in The Assessment and The Digest follow a structured specification to ensure consistent, high-quality summaries. Summary length adapts based on news cycle intensity.
Heavy News Cycle Detection
A heavy news cycle triggers expanded summaries (5-6 sentences) when ANY threshold is met:
Runtime: These thresholds are evaluated and stored in COVERAGE_STATS.heavyCycle (isHeavy, trigger, thresholds) during each refresh cycle. The heavyCycle object drives summary length decisions in both The Digest and The Assessment.
| Metric | Standard | Heavy Threshold |
|---|
| Articles processed | <100 | ≥100 |
| High-priority signals (≥30) | <15 | ≥15 |
| Critical signals (≥40) | <4 | ≥4 |
| New partnerships announced | <5 | ≥5 |
| Themes with activity spike (>25%) | <3 | ≥3 |
Qualitative Triggers
| Tentpole events | CES, MWC, GTC, re:Invent, Google I/O, Build, Ignite, Dreamforce |
| Policy announcements | Executive orders, regulatory rulings, trade actions |
| Mega-deals | Any partnership >$10B or consortium announcement |
| Market-moving | IPOs, major M&A, leadership changes at coverage entities |
Segment-Specific Triggers
| AI-Native | Model release, funding >$1B, market share shift >5% |
| Hyperscalers | Region launch, pricing change, major customer win/loss |
| GSIs | Business group launch, headcount >10K, new practice |
| Silicon | Architecture announcement, fab deal, supply constraint |
| OEM | Product launch, design win, channel program change |
| Sovereign | National AI strategy, infrastructure >$1B |
| Infrastructure | Data center >500MW, new geography entry |
| Enterprise SaaS | Platform integration, AI feature, major migration |
Summary Length by Cycle
| Cycle Type | Sentences | Words | Entities | Themes |
|---|
| Standard | 3-4 | 50-80 | 6-10 | 3+ |
| Heavy | 5-6 | 80-120 | 8-12 | 4+ |
Structure — Standard Cycle
| Sentence | Function | Pattern |
|---|
| 1 | Lead signal — highest-impact development | [Entity] [action verb] [outcome]. |
| 2 | Secondary signals — 2-3 developments | [Entity-Entity] [deal], [Entity-Entity] [deal] signal [pattern]. |
| 3 | Tertiary signal or emerging trend | [Entity] [action] [implication]. |
| 4 | Watch item (optional) | [Trend] intensifies around [developments]. |
Structure — Heavy Cycle (additional sentences)
| Sentence | Function | Pattern |
|---|
| 5 | Segment impact — ecosystem implications | [Segment] [consequence] as [specific development]. |
| 6 | Competitive positioning | [Entity] response/positioning signals [strategic direction]. |
Style Rules
| No articles | Drop "the," "a," "an" where possible |
| Entity-first | Lead with company/organization names |
| Action verbs | triggers, signals, accelerates, expands, formalizes, unveils, closes |
| Noun phrases | "Siemens-NVIDIA expansion" not "Siemens and NVIDIA expanded" |
| Implication language | "signals," "marks," "indicates" to connect facts to meaning |
| Compressed attribution | "per Menlo Ventures" not "according to a report from" |
Constraints — Standard Cycle
| Length | 3-4 sentences, 50-80 words total |
| Entities | 6-10 mentioned |
| Themes | Minimum 3 of 8 strategic themes |
| Score threshold | Only include signals with score ≥35 |
| Tense | Present for ongoing; past for completed deals |
Content Priority
| 1. Partnership announcements | New alliances, expansions, JVs |
| 2. Infrastructure deals | Capacity, investment, deployment |
| 3. Market position shifts | Share changes, competitive moves |
| 4. Policy/regulatory | Government programs, compliance |
| 5. Product launches | Only if partnership-relevant |
Vocabulary Reference
Action Verbs (by intensity):
| High Impact | Medium Impact | Low Impact |
|---|
| triggers, accelerates, reshapes, dominates, surges | expands, formalizes, closes, unveils, launches | continues, maintains, supports, updates, extends |
Pattern Verbs: signals (future direction), marks (inflection point), reflects (underlying trend), intensifies (escalating), converges (trends meeting)
Deal Type Nouns: expansion, alliance, partnership, JV, MOU, win, closing, deal, agreement, licensing, launch, rollout, availability, deployment
Quality Checklist
| ☐ | 3-4 sentences, 50-80 words |
| ☐ | Minimum 6 entities named |
| ☐ | Minimum 3 themes represented |
| ☐ | Lead signal has highest score (≥45) |
| ☐ | No articles unless necessary for clarity |
| ☐ | Entity-first sentence construction |
| ☐ | At least one implication verb |
| ☐ | Present tense for ongoing, past for completed |
Example Output
NVIDIA's "ChatGPT moment for robotics" declaration triggers
cascade of industrial AI partnerships. Siemens-NVIDIA Industrial
AI Operating System expansion, Anthropic's Allianz enterprise win,
and Deloitte-Pearson workforce development alliance signal
accelerating GSI-hyperscaler convergence. Disney-OpenAI's $1B
Sora licensing deal marks entertainment AI's commercial
inflection point.
Analysis: Lead: NVIDIA Physical AI (Score 50) · Secondary: 3 deals across GSI Ecosystem, AI Infrastructure · Tertiary: Disney-OpenAI market signal · Entities: 8 total · Themes: 3 covered
Synthesis Methodology
Synthesis Levels
| Level | Scope | Frequency | Status |
|---|
| The Digest | Last 24-48 hours, score ≥30 only | Per refresh | Active |
| The Assessment | Rolling 10-15 days, cross-theme | Per refresh | Active |
| Theme Summary | Per-theme, rolling 7 days | Per refresh | Active (via THEME_SYNTHESES) |
| Notable Coverage | Score 20-29, last 48 hours | Per refresh | Active |
| Strategic Assessment | Quarter-over-quarter shifts | Monthly | Planned (not yet implemented) |
Current Synthesis Workflow
In the current manual implementation, synthesis is performed during Stages 4-7 of the refresh protocol. The curator reviews classified signals, identifies cross-theme patterns, and writes narrative synthesis covering key developments, pattern analysis, strategic implications, and watch items. Synthesis output is stored in THEME_SYNTHESES{} with structured fields (meta, keyDevelopments, patternAnalysis, strategicImplications, watchItems, articles).
Target Synthesis Prompt Template
The automated pipeline will use this prompt structure for Claude API-driven synthesis:
You are synthesizing news for strategic intelligence.
THEME: {theme_name}
PERIOD: {start_date} to {end_date}
ARTICLES:
{for each article, sorted by signal_score DESC then recency}
---
Source: {source_name} (Tier {tier})
Date: {published_at}
Title: {title}
Summary: {summary}
Entities: {entities}
---
{end for}
Produce a synthesis with:
1. KEY DEVELOPMENTS (3-5 bullets)
Most significant announcements or shifts
2. PATTERN ANALYSIS
What patterns emerge? Where do sources agree or diverge?
3. STRATEGIC IMPLICATIONS
What does this mean for alliance strategy?
4. WATCH ITEMS
Emerging signals that warrant monitoring
Keep synthesis concise and actionable.
Temporal Analysis
Why Time Matters
The key differentiator from static synthesis is tracking how signals evolve:
| Metric | What It Reveals |
|---|
| Theme volume over time | Rising or falling attention |
| Source concentration | Echo chamber vs. genuine consensus |
| Entity frequency | Who's driving the narrative |
| Signal type mix | Noise (speculation) vs. signal (announcements) |
| Week-over-week delta | Acceleration or deceleration of coverage |
Comparison Windows
CURRENT: Last 7 days
COMPARISON: Prior 7 days
BASELINE: 90-day rolling average
Calculate per theme:
- Volume delta (% change)
- New entities (appeared this period, not prior)
- Dropped entities (appeared prior, not this period)
Alert Triggers
These classification rules are applied during the refresh protocol to determine which signals are promoted to the ALERTS[] array. In the current implementation, alerts are created manually based on these criteria.
| Trigger | Condition | Action |
|---|
| Critical signal | Signal score ≥40 | Immediate alert + leadership briefing |
| High-priority signal | Signal score 30-39 | Same-day review + stakeholder notification |
| Theme volume spike | Volume >2x comparison period | Daily highlight |
| Competitive move | Theme = Competitive Activity + score ≥20 | Immediate alert |
| New entity emergence | Entity not in 90-day baseline + 3+ mentions | Weekly flag |
Refresh Protocol
The content refresh process follows a 12-stage staged execution model governed by four design principles. This is the primary operational procedure for every content update.
Design Principles
| Principle | Name | Rule |
|---|
| P1 | Atomic Stages | Each stage contains one type of work. If sub-steps require fundamentally different effort (e.g. updating narrative text vs. constructing sourced article records), they belong in separate stages. Asymmetric effort within a stage is a skip risk. |
| P2 | Hardest First | When a stage has multiple sub-steps, order by descending effort. The heaviest sub-step runs while context is freshest. Completion momentum on light tasks should carry you out of a stage, not trick you into skipping the hard part. |
| P3 | Content Gates | Every validation step must include at least one assertion that can only pass if the actual work was done, not just if the data structure parses. Structural checks catch broken code. Content checks catch skipped work. |
| P4 | Compaction Resilience | After completing each stage, write a progress marker so that if context is compacted mid-refresh, resume can target the exact next stage. Never rely on context memory alone for tracking which stages are done. |
Stage Execution Sequence
| Stage | Scope | Content Gate |
|---|
| 1 | Web Research (Digest) — 6-8 targeted queries across coverage universe | Signal candidates collected |
| 2 | Update Digest Data Arrays — SIGNALS[], ALERTS[], COVERAGE_STATS periods | Newest SIGNALS[].date ≥ refresh window start |
| 3 | Update Digest Hardcoded HTML — lead section, stats sidebar | Grep confirms new date strings in HTML |
| 4 | Web Research (Assessment Themes) — additional depth searches | Patterns and implications documented |
| 5 | Update Assessment Narratives — THEMES[] metadata + THEME_SYNTHESES{} narrative fields. Does NOT touch articles[]. | All meta.lastUpdate values match refresh date |
| 6 | Update Source Coverage Tables — THEME_SYNTHESES{}.articles[] for all 8 themes. Separate stage because article record construction is different work than narrative writing (P1). | For each theme, newest articles[0].date within refresh window |
| 7 | Update Assessment Hardcoded HTML — lead section, stats sidebar | Grep confirms new dates and stats |
| 8 | Update Ecosystems — COMPANIES[] entries with new data | lastRefresh date matches current refresh date |
| 9 | L2 Entity Propagation — update PARTNERSHIPS[], HEADLINES[], TIMELINE[], PROFILES for qualifying entities. Hardest-first: partnerships before profiles. | Newest headline date within refresh window for each modified entity |
| 10 | Update Media Trends — TRENDS{} weekly data, entities, distributions | weekLabels[] includes current week |
| 11 | Footer + REFRESH_LOG — dates, change log entries | Footer date matches refresh date |
| 12 | Final Validation + Deliver — structural + content validation sweep, methodology audit, present_files | All content-level assertions pass |
Progress Tracking
// PROGRESS: S1 ✓ S2 ✓ S3 ✓ S4 pending
// Update this line after each stage completes.
// On compaction or resume, read to determine where to pick up.
Refresh Log
All refresh activity is tracked in the REFRESH_LOG object:
| Field | Purpose |
|---|
| lastRefresh | ISO timestamp of last completed refresh |
| lastRefreshDisplay | Human-readable date for UI display |
| changes[] | Array of change descriptions from current refresh |
| previousRefreshes[] | Archive of prior refresh summaries |
Entity Activation Protocol
Entities in the Ecosystems tracker move through three activation levels. Each level builds on the previous and requires specific data structures.
Activation Levels
| Level | Name | Minimum Data | Result |
|---|
| L1 | Card Activation | COMPANIES[] entry: disabled=false, lastRefresh set, type/partnerships/capital/trend updated | Entity card shows as active with refresh badge. No detail page. |
| L2 | View Details | COMPANY_PROFILES[id], [ID]_PARTNERSHIPS[] (min 5), [ID]_HEADLINES[] (min 3), [ID]_TIMELINE[] (min 3 quarters), [ID]_SECTORS[], COMPANY_DATA[id] registered | "View details →" arrow appears. Full detail page with Headlines, Partnerships, Timeline, Sectors tabs. |
| L2+ | Strategic Brief | L2 requirements + COMPANY_PROFILES[id].dossier{} with status, brief, profile fields, sections[]. For live briefs: corresponding <template id="tpl-dossier-[id]"> with full analytical content. | "Strategic Brief" tab appears in detail page tab strip. Live briefs render full inline report (threat assessment, financials, competitive forces, executive tracking, timeline). Planned briefs render stub outline with analysis roadmap. |
| L3 | Media Trends | TRENDS.trendingEntities[] entry, optional volumeByTheme[] and weekOverWeek[] updates | Entity appears in Media Trends trending list and charts. |
L2 Data Structure Requirements
| Array | Record Fields | Minimum Records |
|---|
| [ID]_PARTNERSHIPS[] | id, partner, sector, type, commitment, date, status, headline, summary, terms[] | 5 |
| [ID]_HEADLINES[] | title, value, sector, date, source, url, partnerId, impact | 3 |
| [ID]_TIMELINE[] | quarter, events: [{ date, partners[], detail }] | 3 quarters |
| [ID]_SECTORS[] | id, name, partners, value, focus | Derived from partnerships |
| COMPANY_PROFILES[id] | title, date, metrics[] | 1 entry |
L2+ Dossier Data Structure
| Property | Fields | Notes |
|---|
| COMPANY_PROFILES[id].dossier{} | status ('live'|'planned'), brief, hq, founded, ticker, ceo, marketCap, sections[] | Controls Strategic Brief tab visibility and render mode |
| dossier.sections[] | title, desc | Analysis roadmap items. Rendered as numbered list for planned briefs. |
| <template id="tpl-dossier-[id]"> | Full HTML content (sections, tables, charts, timelines) | Required only for status='live'. Rendered inside .dossier-content wrapper with scoped CSS. |
Activation During Refresh
Entity activation typically occurs at Stage 8 (L1) or Stage 9 (L2 propagation) of the refresh protocol. New L2 activations require dedicated web research for partnership data. L2 propagation (updating existing detail pages with new signals) is triggered when an active entity has ≥1 new SIGNAL or ALERT in the current refresh window.
Activation Checklist
[ ] L1: COMPANIES[] disabled: false, lastRefresh set
[ ] L1: Type, partnerships, capital, trend updated
[ ] L2: COMPANY_PROFILES[id] created
[ ] L2: [ID]_PARTNERSHIPS[] created (min 5 records)
[ ] L2: [ID]_HEADLINES[] created (min 3 records)
[ ] L2: [ID]_TIMELINE[] created (min 3 quarters)
[ ] L2: [ID]_SECTORS[] created
[ ] L2: COMPANY_DATA[id] registered
[ ] L2+: COMPANY_PROFILES[id].dossier{} created (status, brief, profile fields, sections[])
[ ] L2+ (live): <template id="tpl-dossier-[id]"> added with full analytical content
[ ] L2+ (live): Scoped CSS covers all content classes (.dossier-content prefix)
[ ] L3: TRENDS.trendingEntities[] updated (if applicable)
[ ] Validation: JS syntax check passes
[ ] Validation: validateData() reports no new warnings
Theme Volume Chart Methodology
The Theme Volume Chart (TVC) visualizes article volume by strategic theme across two views: a full 26-month timeline and a close-up weekly view. It blends observed data with simulated estimates to present a continuous trend narrative.
Data Sources
| Period | Data Type | Method |
|---|
| Jan 2024 – May 2025 | Estimated | Reverse-trajectory Monte Carlo simulation from Jun 2025 anchor points |
| Jun 2025 – Oct 2025 | Estimated (weekly) | Backward interpolation from Nov anchor points with event-driven impulses |
| Nov 2025 – Feb 2026 | Observed | Actual weekly intake volumes from manual curation |
Monte Carlo Simulation
The full-view monthly trajectory is generated by reverse-trajectory Monte Carlo:
Simulation Parameters:
Iterations: 5,000 per theme
Noise model: Log-normal (σ = 0.12 full view, 0.08 close-up)
Anchor points: Aggregated weekly data (Jun 2025 onward)
Growth rates: Theme-specific, derived from observed data
Event boosts: Multiplicative impulse functions at known events
Reverse trajectory formula:
traj[i] = traj[i+1] / ((1 + growthRate) × eventBoost × noise)
Output per theme:
P25 (lower quartile), Median (P50), P75 (upper quartile)
Confidence bands shown for estimated period only
Event-Driven Impulse Functions
Known market events (earnings, product launches, conferences, regulatory milestones) are modeled as multiplicative boosts applied to the corresponding time period. Each theme defines its own event boost map:
| Event Type | Typical Boost Range | Duration |
|---|
| Major earnings season | 1.2x – 1.5x | 1-2 weeks |
| Tentpole conference (CES, GTC, re:Invent) | 1.3x – 1.8x | 1-2 weeks |
| Major fundraise or M&A | 1.2x – 1.6x | 1 week |
| Regulatory milestone | 1.1x – 1.4x | 1-2 weeks |
Convergence Diagnostics
Simulation quality is verified using a Gelman-Rubin inspired diagnostic:
Method:
Run two independent chains of 2,500 iterations each
with offset PRNG seeds.
Compare median values at the earliest estimated time point
(furthest from anchor — maximum estimation uncertainty).
Divergence = |median_chain1 - median_chain2| / average
Pass criterion: divergence < 5%
Warning: divergence ≥ 5% suggests insufficient iterations
Diagnostics run automatically on page load and log results to the browser console.
View Modes
| View | Resolution | Period | Features |
|---|
| Full View | Monthly | Jan 2024 – Feb 2026 | All 8 themes, P25-P75 confidence bands on estimated region, event annotation markers |
| Close-Up | Weekly | Jun 2025 – Feb 2026 | ~35 weeks, observed region shaded in teal, dashed lines for estimated / solid for observed |
Technical Implementation
Current Stack (Manual Curation)
| Component | Implementation | Purpose |
|---|
| Data Layer | JSON files (async loader.js) | SIGNALS, THEMES, ALERTS, COMPANIES, COVERAGE_STATS, TRENDS, THEME_SYNTHESES, DOSSIERS |
| Auth | AWS Cognito + Lambda | Passwordless magic links, 45-day invite tokens, login tracking |
| Access Control | auth/gate.js | JWT-based client gate; redirects unauthenticated users to splash.html |
| Rendering | Vanilla JavaScript | Dynamic DOM generation from data arrays |
| Visualization | Chart.js 4.x | Theme Volume Chart with Monte Carlo simulation |
| Styling | CSS custom properties | FT-inspired design system, responsive layout |
| Curation | 12-stage refresh protocol | Source monitoring, classification, scoring, content gates |
| Validation | validateData() + runSensitivityAnalysis() | Schema checks, range checks, cross-reference integrity, threshold sensitivity |
| Ranking | Temporal decay function | Scoreadj = Score × e(-λ × age_days) |
| Frontend Fx | Three.js (dot-matrix.js), word-transition.js | Animated dot-grid background, cross-fade word reveals |
| Email | SendGrid | Transactional invite emails, 3-email beta sequence |
| Config | config/platform.json | Coverage stats, signal thresholds, segment maps, data manifest |
| Output | SPA (index.html + JS + JSON) | Static-hostable, serverless backend for auth and tracking |
| Hosting | Static host + AWS | Frontend served statically; backend on API Gateway + Lambda |
Data Architecture (View-to-Source Map)
Each view in the platform is driven by specific data structures. The DATA_MANIFEST object in the codebase documents this mapping programmatically.
| View | Primary Data Sources | Render Functions |
|---|
| The Digest | SIGNALS, ALERTS, COVERAGE_STATS.digestPeriod | renderSignals, renderAlerts, renderTLDR, renderNotableCoverage |
| The Assessment | THEMES, THEME_SYNTHESES | renderThemeCards, renderThemeSynthesis |
| Ecosystems | COMPANIES, COMPANY_DATA, COMPANY_PROFILES | renderSegmentFilters, renderCompanyCards |
| Strategic Brief | COMPANY_PROFILES[id].dossier{}, <template tpl-dossier-[id]> | renderDossier |
| Media Trends | TRENDS | renderVolumeChart, renderTrendingEntities, renderSourceDistribution |
| Processing Stats | COVERAGE_STATS | renderProcessingSummary, updateNavDate |
Data Structure Schema
| Array | Required Fields | Feeds Views |
|---|
| SIGNALS[] | id, score, source, sourceTier, timestamp, displayTime, title, summary, theme, signalType, entities, url | Digest, Processing |
| ALERTS[] | id, type, title, source, theme, timestamp, displayTime, url, signalId | Digest |
| THEMES[] | id, name, priority, articleCount, topScore, trendDirection, trendPercent, updatedAgo, summary, enabled | Assessment, Digest |
| COMPANIES[] | id, name, segment, type, partnerships, capital, sectors, trend, trendText, disabled | Ecosystems |
| COVERAGE_STATS{} | coveragePeriod, digestPeriod, thresholds, heavyCycle | Processing, Digest, Trends |
| COMPANY_PROFILES[id].dossier{} | status, brief, hq, founded, ticker, ceo, marketCap, sections[] | Ecosystems (Strategic Brief tab) |
| <template tpl-dossier-[id]> | Full HTML content (live briefs only) | Ecosystems (Strategic Brief tab) |
Data Validation
The validateData() function runs automatically on page load and performs three categories of checks. Results are logged to the browser console. Validation is currently non-gating: the page renders regardless of warnings. In a future automated pipeline, these checks should become pipeline gates that block publication on failure.
| Check Type | What It Validates | Example |
|---|
| Schema | All required fields present in every record | SIGNALS[i] must have id, score, source, etc. |
| Range | Values within valid bounds | Score 2-50, sourceTier 1-4 |
| Cross-reference | Foreign key integrity between arrays | ALERTS[i].signalId must exist in SIGNALS |
Results are logged to the browser console. Zero warnings indicates a clean dataset.
Sensitivity Analysis
The runSensitivityAnalysis() function tests how signal classification shifts under different threshold assumptions. It evaluates three threshold offsets (-5, 0, +5) and reports how many signals fall into each priority tier at each offset.
Purpose: If small threshold changes dramatically shift signal counts between tiers, the scoring calibration may need adjustment. Stable distributions across offsets indicate robust threshold selection.
Target Stack (Automated Pipeline)
| Component | Tool | Purpose |
|---|
| Scheduler | GitHub Actions | Trigger daily/hourly runs |
| Fetcher | Python + feedparser | Pull RSS content |
| Parser | BeautifulSoup | Extract clean text |
| Storage | SQLite | Store articles + metadata |
| Classification | Claude API (Haiku) | High-volume classification |
| Synthesis | Claude API (Sonnet) | Weekly summaries |
| Output | Jinja2 templates | Generate static HTML |
| Hosting | Static host | Deploy site |
Target Database Schema
CREATE TABLE articles (
id TEXT PRIMARY KEY,
source_id TEXT,
source_tier INTEGER,
fetched_at TIMESTAMP,
published_at TIMESTAMP,
title TEXT,
url TEXT UNIQUE,
content TEXT,
primary_theme TEXT,
secondary_themes JSON,
entities JSON,
confidence INTEGER,
impact INTEGER,
irreversibility INTEGER,
signal_score INTEGER GENERATED ALWAYS AS (confidence * (impact + irreversibility)) STORED,
signal_type TEXT,
summary TEXT
);
CREATE INDEX idx_theme ON articles(primary_theme, published_at);
CREATE INDEX idx_published ON articles(published_at);
CREATE INDEX idx_score ON articles(signal_score DESC);
Estimated Costs
| Component | Monthly Cost |
|---|
| Claude API (classification) | $20-50 |
| Claude API (synthesis) | $30-60 |
| Static hosting | $0-20 |
| Total | $50-130 |
Implementation Roadmap
Complete: Presentation Layer
- Static site architecture with section-based navigation
- Theme index with priority indicators and trend signals
- Theme detail pages with synthesis structure
- Daily digest format with relevance filtering
- Trends dashboard with volume charts and entity tracking
- Theme Volume Chart with Monte Carlo simulation and convergence diagnostics
- Full methodology documentation with dynamic stat derivation
Complete: Information Architecture
- Eight-theme taxonomy aligned to strategic priorities
- Four-tier source classification framework
- Signal type definitions (Announcement, Analysis, Speculation, Background)
- Relevance scoring criteria (1-5 scale) with temporal decay adjustment
- Synthesis templates for theme summaries, including heavy cycle detection
- DATA_MANIFEST documenting view-to-data-source mappings and schema
Complete: Operational Processes
- 12-stage refresh protocol with 4 design principles (Atomic Stages, Hardest First, Content Gates, Compaction Resilience)
- 3-level entity activation protocol (L1 Card, L2 Detail Page, L3 Trends Integration)
- Data validation framework (schema, range, cross-reference integrity checks)
- Sensitivity analysis for threshold parameters (±5 offset testing)
- Methodology Registry for tracking documentation freshness
Complete: Analytical Engine
- Temporal decay scoring for signal freshness ranking
- Monte Carlo simulation engine (5,000 iterations, log-normal noise) for trend estimation
- Convergence diagnostics (2-chain median comparison) for simulation quality
- Event-driven impulse functions for known market catalysts
Complete: Authentication & Access Control
- Passwordless magic link auth via AWS Cognito Custom Auth + Lambda (requestLink.js, verifyToken.js)
- Dual token model: standard 15-minute single-use tokens + 45-day persistent invite tokens (type: invite) for beta users
- Login activity tracking: first_login_at, last_login_at, login_count per token in DynamoDB (EcosystemEdgeMagicLinks)
- Client-side auth gate (auth/gate.js) with JWT validation, token expiry checks, and redirect to splash.html
- Beta user registry (data/beta-users.json) with status and delivery tracking per user
Complete: Email & Notification System
- Outbound email migrated from AWS SES to SendGrid for transactional invite and sequence emails
- 3-email beta sequence defined in data/email-sequence.json: Welcome (day 0), Pre-Flight Assignment (day 3), Weekly Friday Dispatch (12:12pm ET through May 16, 2026 launch)
- 5-channel email subscription system with server-side persistence (DynamoDB EcosystemEdgeSubscriptions)
- Mobile push notifications disabled and out of scope for beta program
Complete: Frontend Modules
- js/dot-matrix.js — reusable Three.js animated dot-grid background with shiftColor() and bgShift() API
- js/word-transition.js — reusable cross-fade word reveal utility with anchor, word, color, duration, exitFade, fadeTarget, and onComplete options
- 4-phase async data loader (loader.js) replacing inline data arrays with JSON file fetches
Complete: Strategic Intelligence Briefs (v32)
- L2+ activation level: dossier{} object in COMPANY_PROFILES with status, brief, profile fields, sections[]
- Dynamic "Strategic Brief" tab injection in entity detail pages (visible only when dossier data exists)
- Live brief rendering via <template> elements with scoped CSS (.dossier-content prefix)
- Planned brief rendering with stub outlines: strategic brief, profile grid, status banner, numbered analysis roadmap
- Intel dossier live with full analytical content (Executive Summary, Threat Assessment, Financials, Competitive Forces, Executive Departures, Timeline)
- NVIDIA and Cerebras dossiers registered as planned with section roadmaps
- Silicon Partners companion file (silicon-partners.html) with 15-entity index covering merchant silicon, custom silicon, foundry, and memory segments
In Progress: Entity Coverage
- 17 of 72 entities at Level 2 detail depth (partnerships, headlines, timeline, sectors)
- 3 of 17 L2 entities at Level 2+ with Strategic Brief tab (Intel live, NVIDIA and Cerebras planned)
- Remaining 55 entities at Level 1 card-level data only
- Priority: high-signal entities activated first based on news coverage density
Next: Data Pipeline (Automated)
- Configure RSS feeds and API connections for defined sources
- Build ingestion scripts with deduplication logic
- Implement classification prompts via Claude API
- Create SQLite database with defined schema
- Test end-to-end fetch → classify → store flow
Next: Automation
- Schedule daily ingestion runs
- Automate theme synthesis generation
- Configure alert triggers for high-relevance signals
- Build static site generation from database
Next: Deployment
- Select hosting environment
- Configure CI/CD pipeline for automated publishing
- Set up monitoring and error alerting
- Establish backup and recovery procedures
Useful Database Queries
These queries are designed for the target SQLite database schema (see Implementation chapter, Target Database Schema). In the current implementation, equivalent analysis is performed through JavaScript functions operating on the in-memory data arrays.
Theme volume by week
SELECT primary_theme, COUNT(*) as articles
FROM articles
WHERE published_at > datetime('now', '-7 days')
GROUP BY primary_theme
ORDER BY articles DESC;
Trending entities
SELECT json_each.value as entity, COUNT(*) as mentions
FROM articles, json_each(articles.entities)
WHERE published_at > datetime('now', '-7 days')
GROUP BY entity
HAVING mentions >= 3
ORDER BY mentions DESC;
Week-over-week comparison
WITH current AS (
SELECT primary_theme, COUNT(*) as cnt
FROM articles WHERE published_at > datetime('now', '-7 days')
GROUP BY primary_theme
),
prior AS (
SELECT primary_theme, COUNT(*) as cnt
FROM articles
WHERE published_at BETWEEN datetime('now', '-14 days')
AND datetime('now', '-7 days')
GROUP BY primary_theme
)
SELECT c.primary_theme,
c.cnt as this_week,
p.cnt as last_week,
ROUND((c.cnt - p.cnt) * 100.0 / p.cnt, 1) as pct_change
FROM current c
LEFT JOIN prior p ON c.primary_theme = p.primary_theme;
Limitations
Classification & Analysis
- Classification accuracy depends on source content quality and may misassign edge cases between overlapping themes
- Synthesis reflects patterns in source coverage, not ground truth market dynamics
- Paywalled content from premium sources (The Information, Gartner full reports, IDC trackers) is not included
- Entity extraction may miss new companies or products not in the coverage universe
Operational
- Manual curation workflow limits refresh frequency to 2-3 updates per week at current operational tempo
- Real-time alerts have 15-60 minute latency depending on source refresh intervals
- Entity detail pages (Level 2) are available for approximately 24% of tracked entities; the remainder show card-level data only
- Strategic Briefs (L2+) are available for 3 of 17 L2 entities (1 live, 2 planned); remaining L2 entities show standard detail tabs only
- The "54 news sources" figure (42 active in current period) is configured in config/platform.json; no structured SOURCES[] array backs this count
- Mobile push notifications are disabled and out of scope for the beta program
- Beta invite tokens (45-day TTL) are reusable but not rotated automatically; expired tokens require manual re-issuance
Analytical Model
- Theme Volume Chart data for Jan 2024 through May 2025 is synthetically reconstructed via Monte Carlo simulation, not observed
- Temporal decay parameter (λ = 0.03) was set by editorial judgment; external calibration against outcomes has not been performed
- Sensitivity analysis runs at fixed ±5 threshold offsets; broader parameter sweeps are not automated
- Monte Carlo convergence diagnostics use 2-chain Gelman-Rubin principle with 2,500 iterations per chain; edge cases with high event-boost variance may require more iterations
- Live dossier content is point-in-time analytical work; it does not auto-refresh from SIGNALS[] or ALERTS[] data and requires manual updates when material changes occur
Methodology Maintenance
- Coverage universe counts appear as static text in multiple HTML locations; dynamic derivation covers some but not all instances
- Methodology changes are tracked in METHODOLOGY_LOG; retroactive auditing begins at v31 (Feb 2026)
- Dossier content is loaded from data/dossiers/ JSON files; new dossier content classes must be covered in the .dossier-content CSS scope
- Platform config (config/platform.json) centralizes thresholds and segment maps but is not auto-synced to methodology text; manual verification required on config changes