MASTER PLAN v2.0

Economic & Workforce Research Center

Centro de Investigación Económica y Laboral

STFM Investor Platform + Capital Markets + Technology + Economic + Workforce Intelligence + Semiannual Educational Planning

THE CLOSED LOOP

Daily data → validated evidence → monthly research → semiannual needs assessment → human academic decision → training program → student outcomes → employer feedback → Research Center

This connects capital, technology, productivity, labor demand, skills and education.

01 · TRUST FOUNDATION

Vision and principles

Human agencyThe platform informs, explains, simulates and proposes; people decide.

Evidence before actionDaily data generates signals; accumulated evidence generates proposals.

One Data FabricMarkets, technology, economics, employment and education share metadata.

Facts vs. interpretationSeparate observations, calculations, AI interpretation and human decisions.

Risk & uncertaintyEvery conclusion includes quality, freshness, limitations and uncertainty.

Portable architectureSites provides the experience; portable services power the system; PythonAnywhere remains a legacy validation baseline.

No fabricated dataMissing data is shown as N/A.

02 · RESPONSIBILITY BY LAYER

Target architecture

ExperienceUI, dashboards, chat and learning - Sites

Data orchestrationConnectors, cache, retries and rate limits - portable services

MastersSecurities, companies, industries, occupations and skills - shared registries

Data FabricNormalized observations with provenance - shared store

IntelligenceMarket, quantitative, risk, technology, economic and workforce engines

ResearchTrend validation and evidence synthesis - Research Center

PlanningSemiannual curriculum proposals - Academic Planner

GovernanceApproval, privacy and audit - human review

Migration rule

Freeze the working app.py. Build new portable services independently; retire legacy components only after quality assurance and observability.

03 · NORMALIZED EVIDENCE

Central Data Fabric

Market observationsymbol, asset type, value, source, observed time, freshness, quality

Technology signaltechnology, use case, industry, maturity, adoption/investment, source, date

Job postingoccupation, employer, industry, location, skills, education, salary, dates, source

Skill observationcanonical skill, aliases, occupation, frequency, geography, period

Training programprogram ID, skills taught, capacity, completions, outcomes

Research findinghypothesis, evidence for/against, confidence, limitations, reviewer

Audit eventactor, action, object, timestamp, before/after, request ID

04 · DATA-SOURCE STRATEGY

Authority, rights and provenance travel with every observation.

Corporate · official filings and regulatorsMacro/labor · statistical agencies and central banksJobs · permitted employer and posting datasetsTechnology/R&D · patents, publications, public R&D and filingsMarket prices · appropriately licensed providersNews · permitted feeds and official releasesEducation outcomes · consented, private and aggregated

05 · SHARED MASTERS

Universal registries

Security MasterUniversal asset search and provider mappings

Company MasterLegal entity, tickers, industry and geography

Industry TaxonomyCommon market, technology and labor mapping

Occupation MasterCanonical occupations and aliases

Skill TaxonomyCanonical skills, aliases and proficiency

Technology TaxonomyVersioned technology families and use cases

Geography MasterCountry, state, metro and local mappings

Program-Skill MapAcademy programs to skills, occupations and industries

06 · INGEST TO AUDIT

Daily Intelligence Pipeline

IngestMarket, company, macro, technology, jobs, skills, news and outcomes

ValidateSchema, timestamp, duplicates, source rights and missing fields

NormalizeSecurities, companies, industries, occupations, skills and geography

EnrichTechnical metrics, trends, job-skill frequencies and linkages

StoreRaw immutable data plus normalized analytical layer

DetectAcceleration, persistence, shortages, emerging technology and investment shifts

RouteInvestor signals to STFM; economic/labor signals to the Research Center

AuditSource, model/transformation version, timestamp and quality

07 · TECHNOLOGY GAP INTELLIGENCE

What remains insufficiently solved?

Unmet economic or human need
Technology that reduces cost, time, risk or resource constraints
Industry adoption and barriers
Capital, R&D, acquisitions and public investment
Complementary occupations and skills
Human welfare, safety, access, health and environmental effects
Evidence supporting and contradicting the hypothesis

08-09 · WORKFORCE INTELLIGENCE

Is demand persistent, broad and insufficiently supplied?

Demand intensity and comparable-period growth
Posting persistence and wage pressure
Recurring canonical skills and employer breadth
Geographic breadth
Workers, unemployment, graduates, certifications and mobility
Training capacity, seats, completions and time-to-skill
Confidence reflects evidence quality, not certainty

10 · FROM SIGNAL TO STRATEGY

Research cadence

DailyDetect changes and anomalies - signals/watchlists

WeeklyCluster related evidence - emerging themes

MonthlyValidate persistence and contradictions - research briefs

QuarterlyCross-sector synthesis - Technology & Workforce Outlook

Every 6 monthsAcademic needs assessment - program recommendations

AnnualEvaluate outcomes and methodology - strategic review

Anti-noise rule

A daily signal never changes curriculum by itself. Semiannual planning requires persistence, comparable evidence, contradictions and human review.

11 · HUMAN DECISION OPTIONS

Semiannual Academic Planning

CREATEPersistent high-confidence gap plus insufficient training coverage

EXPANDExisting program fits demand but capacity is insufficient

MODIFYMarket skill mix changed

MAINTAINDemand and outcomes remain aligned

PILOTPromising evidence remains immature

REDUCE / RETIRESustained demand deterioration or obsolete content, after review

MORE RESEARCHConflicting or low-quality evidence

12 · TRAINING PROGRAM DESIGN

Translate evidence into capability.

Target occupations and geographies
Core, complementary and emerging skills
Skill → module → assessment → proficiency map
Course, certificate, microcredential, lab or partnership format
Time-to-skill aligned to urgency and complexity
Faculty, laboratories, software and equipment
Employer/subject-matter review before launch
Completion, competency, placement, progression and feedback criteria

13 · OUTCOMES FEEDBACK LOOP

Measure whether training works.

Enrollment and completion
Skill assessment and competency gain
Placement and time to employment
Wage or role progression when ethically and legally collected
Employer feedback on skill fit
Curriculum drift between taught and demanded skills

14 · INTELLIGENCE PRODUCTS

Applications

Markets / Stock AnalyzerCapital-market and company intelligence

Research WorkspaceCompany, industry, technology and workforce context

Portfolio / RiskExposure to sector, technology and macro transitions

Investor AIGrounded financial explanation

Economic DashboardGrowth, productivity, investment, inflation and trade

Technology ObservatoryEmerging technology, R&D, adoption and investment

Labor ObservatoryOccupations, postings, wages, skills and locations

Skills GraphOccupation-skill-technology-industry relationships

Gap ExplorerTechnology, workforce and education gaps

Academic PlannerCREATE / EXPAND / MODIFY / MAINTAIN / PILOT / REDUCE

Research AIEvidence synthesis with uncertainty

15 · GOVERNANCE, PRIVACY AND TRUST

Evidence is useful only when it is governed.

Grounded AI; no invented observationsNo causal claim from correlation aloneEvidence against hypotheses is preservedModels, taxonomies and methods are versionedHigh-impact academic proposals require human reviewPersonal data is minimized; analytics are aggregatedLicensing, attribution, redistribution and rate limits are recordedResearch, curriculum, administration and model actions are audited

16 · APIs AND DATA PRODUCTS

One evidence layer, multiple responsible consumers.

/markets/overview/assets/{symbol}/technology/signals/labor/demand/skills/trends/research/findings/gaps/workforce/planning/semiannual/programs/outcomes/ai/context

17 · KPIs AND RELEASE GATES

No release without measurable trust.

Data · coverage, freshness, missing-data rate, rightsReliability · API success, latency, error budgetMarkets · universal symbol resolution and isolationLabor · deduplication, mappings and geographyResearch · evidence, contradictions and reviewer traceabilityPlanning · evidence, confidence and alternativesEducation · completion, skill gain, placement and feedbackAI · grounded-answer rate, source coverage and error reviewTrust · privacy, licensing, audit and role tests

18 · PHASED DELIVERY

Roadmap

0 - FreezeBackup and stable baseline - no regressions

1 - Data FabricSchemas, source registry and masters - provenance complete

2 - Labor / TechnologyIngestion and taxonomies - measured coverage and quality

3 - Research CenterObservatories and research store - auditable findings

4 - Gap ModelsTechnology, workforce and education gaps - backtests and reviewer QA

5 - Academic PlannerSemiannual workflow - human approval controls

6 - AI ApplicationsInvestor AI and Research AI - grounded answers

7 - PilotSelected industries and regions - outcome instrumentation

8 - ScaleBroader coverage - operational readiness

19 · IMMEDIATE ACTION PLAN

Twelve controlled actions establish the first operating cycle.

01Freeze the working PythonAnywhere app.py and preserve the Gate 1B PASS baseline.
02Create the source registry and legal/licensing inventory.
03Define the Security, Company, Industry, Occupation, Skill, Technology and Geography masters.
04Define normalized schemas and raw/normalized storage.
05Build daily labor and technology ingestion pilots.
06Build deduplication and entity/skill normalization.
07Launch the Technology and Labor Observatories.
08Develop gap methodology with backtests and human review.
09Build the Semiannual Academic Planner.
10Connect Research AI and Investor AI to the same evidence layer.
11Pilot selected sectors and geographies.
12Run the first six-month Technology & Workforce Needs Assessment.

20 · FINAL OPERATING MODEL

Discipline > Prediction | Risk Management > Conviction | Evidence > Assumption | Education → Analysis → Human Decision

We rise together. Nobody is left behind.

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