Pillar 4 — Frontier Intelligence

The thesis. Give the private sector the intelligence to accelerate the frontier in Latin America: a terminal of comparable markets and indicators, BI exploration and analysis with deterministic provenance, post-AGI economy and compute-sovereignty lenses — the decision layer that turns verified data into investment and deployment of frontier technology.

It is the fourth pillar of the cycle: it consumes the outputs of the Sovereign Model (2 → 4), and the companies that profit from this intelligence pay the Data Trust back with data and fees (4 → 1).

In the grant frame

In the three-pillar proposal Futuros presents to funders, these surfaces appear as outputs of the Governance Engine (P3), the layer that turns the governed corpus into decisions. The platform canon remains the four-pillar cycle: the mapping between the two frames lives on the four-pillar cycle.

Why provenance makes the intelligence bankable

The pillar's central argument in one line: a cited figure is an investable figure. An analyst taking a number to an investment committee, or a development-bank officer structuring an instrument, cannot defend a figure from a black box. They can defend a figure that traces back in two clicks to its primary source. Deterministic provenance is not a compliance feature — it is what turns intelligence into something you can act on with real money.

That is why the same grounding discipline that serves a regulator (pillar 3) serves an investor (pillar 4): both drink from the same Sovereign Model grounded in the same verified corpus.

And the argument is not asked to be taken on faith: /confianza is the platform's public trust report — provenance coverage, freshness of every source, anomaly load, coverage gaps, expired sources and the corrections log. The claim "a cited figure is an investable figure" is auditable on that page.

The live surfaces

The hub: /inteligencia

/inteligencia — the gateway to the entire pillar: a hub for the BI suite (the analog of Bloomberg's Launchpad) that groups every analytical surface by the job it does — explore · screen and build · monitor · analytical lenses · trust and data. It makes the full terminal legible and discoverable instead of leaving it scattered across loose routes.

Markets terminal

/mercados — the markets screener: dozens of indicators comparable across the 25 countries, with ranking, as-of year, base-100 index, CSV export and watchlists; it integrates IMF WEO projections. The per-country view /mercados/:iso drills into a specific economy. It is the terminal from which an analyst builds a thesis with traceable figures.

The exact semantics of the transformations (all client-side, in src/routes/mercados.tsx):

Transformation	Semantics	Honesty guards
"As of" cut	each cell takes the latest point of the series with year ≤ the chosen year; if the series starts later, the cell stays empty ("—"), never interpolated	opens by default at min(current year − 1, latest observed WEO year) — the terminal never opens on projections
WEO projection boundary	derived from the data itself: horizon = max year of any WEO series; first projected year = horizon − 5; last observed = horizon − 6	self-corrects when a new vintage is re-ingested (horizon 2030 → observed 2024); every WEO cell with year ≥ first projected carries a projection mark, whether the slider moves or not
Rebase to 100	base = the series value at or before the base year (2015 by default); each point = v / base × 100, rounded to one decimal	a country with no base value, or base = 0, is excluded from the trajectory rather than plotted wrong
Ranking and heat	position within the cohort at the as-of year, respecting the indicator's good direction (if "lower is better", lower value = better rank); per-column min–max heat	the ≥ / ≤ filters live in the URL (f=id:op:val), so every filtered screen is shareable and reproducible as-is

BI exploration in the browser

/explorar — the self-service exploration workbench: the analyst crosses whichever metrics they want against whichever countries they want over a static dataset of 200+ metrics × 25 countries, with table, trend and scatter views; normalizations (raw value, base-100 index, z-score, year-over-year change); regional presets; cross-highlighting between views; named saved views; URL-shareable state; and CSV/SVG/PNG export.

The four normalizations have an exact, deterministic definition (src/routes/explorar.tsx) — two analysts with the same URL see the same number:

Mode	Definition	Exclusions and guards	Reference line
Raw	the value in its original unit	—	—
Base-100 index	v / base × 100 to one decimal; base = latest value at or before the chosen base year	a series with no base, or base = 0, is omitted from the chart	y = 100
Z-score	per year, across the countries on screen: (v − mean) / σ (population σ), to two decimals	σ = 0 → divisor 1 (never infinite)	y = 0
Year-over-year (YoY)	% change vs the immediately preceding, consecutive year — a gap in the series cuts the computation, it never skips	no consecutive previous year, or previous value = 0 → empty point	y = 0

And for advanced analysis, a fourth view: a SQL console on DuckDB-WASM, loaded on demand when the tab opens, executed entirely in the browser, no backend — queries never leave the analyst's machine. Serious analysis, provenance intact.

Situation Room

/sala — the personalized, cited situation room for decision-makers: it aggregates (never duplicates) the single most important item from each analytical layer — instability, biggest mover, top anomaly, cross-source contradiction, nearest forecast, next election, rising attention — into one screen ordered by severity, with a link back to each full surface. Purely client-side assembly over already-baked JSON; zero backend, zero LLM until the user chooses to drill down in the chat.

Movements

/movimientos — the movement detector: which indicators moved, by how much and in which direction, to catch significant shifts across countries and dimensions without sweeping tables by hand.

Alerts and embeds

/alertas — the alerts interest list: you register an email (with optional country and topic) and a confirmation arrives; the weekly email digest is in preparation, and the page itself says so. What already operates today: an anomaly digest in JSON at GET /api/alerts-digest, and a Monday cron (0 13 * * 1) that distributes it to the configured webhooks.

The white-label embeds (/embed/board, /embed/stat, /embed/chart) let you embed boards, stats and charts with your own brand and accent on third-party sites — Futuros intelligence inside the client's workflow, not in a separate tab. Provenance travels with the embed. And there is no need to build the URL by hand: /incrustar is the self-service builder — pick parameter, country, indicator, type and theme, see the live iframe preview and copy the one-line snippet, always with the cited value and its source chip.

Strategic lenses

• /economia-agi — the post-AGI economy lens: the scarce complements in a world of abundant cognitive labor, with six cited dimensions, capital and net-position sliders, and scenarios. It helps an investor reason about where value accrues when intelligence gets cheap.
• /soberania-computo — the compute sovereignty index: readiness in energy, connectivity and capital per country, with the binding constraint identified for each. Where the region can host frontier compute, and what is missing to get there.
• /dependencia — the trade and technology dependency analysis (on UN Comtrade data): what and whom each country depends on, to read geopolitical and supply-chain risk from a non-aligned position.
• /horizonte-2035 — the backcasting module: it extends each country's 10-year historical slope of the composite index (least squares) out to 2035 and compares it against a user-set target. It is explicitly a trend extrapolation, not a forecast — the assumption is declared on the page and the underlying series is shown in full. The natural companion of the chat's forecast operation, applied to the pillar's underlying question: are we on trajectory for 2035?

Under-the-hood mechanisms

Two mechanisms with no route of their own that define what this terminal can promise:

• Deterministic forecast engine (server/chat/compute.ts): the platform's projections are not improvised by an LLM, they are computed by auditable code via OLS trend or CAGR. Every result comes out stamped projection: true and cited to the endpoints of its fit window, and the engine refuses to project with fewer than 3 historical points: it would rather not answer than fabricate a trend. The operation-by-operation detail is in the decision chain, just below.
• MCP institution feed (server/mcp/handler.ts): the governed corpus, exposed to third parties' AI agents (the internal copilot of a development bank or a ministry) through a closed exposure of 15 read-only tools. It is an allowlist, not a denylist: open-web tools are excluded and any new non-corpus tool fails closed by default. The institution asks with its own tools and every answer arrives with its citations. Connection guide: MCP server.

The chat's BI tools: the decision chain

The assistant (Sovereign Model) exposes BI tools that chain the data all the way to the decision, every result tied to its source:

• compute — deterministic computations over the series: change, cagr, rank, gap_to_frontier, correlation, forecast, explain_change. forecast projects by OLS trend (95% band from in-sample residuals, requires ≥3 historical points) or by CAGR (requires positive endpoints); the result comes out marked projection: true and the endpoints of the fit window carry their citation_id — the projection inherits the provenance. explain_change ranks co-movements: the Pearson r of each candidate indicator from the same country against the subject's series, and declares it explicitly as association, not causation.
• recommend_action — the pilot → financing → decision-maker chain (server/chat/tools-action.ts). Given a country (ISO3) and a pillar, it walks the corpus's own graph with deterministic joins — zero LLM in the chain:
  1. Pilot: filters pilots/_index.json by country (primary or alternate) and pillar (primary or tag); reproducible score = 0.6 primary pillar (0.3 tag) + 0.1 primary country + maturity as tiebreaker (scaling > mvp > concept). No match → returns pilot: null with the explicit instruction to state it as a gap, not to invent one.
  2. Financing: the pilot's own funding sources (with % share) plus its instruments, joined by slug against financing-instruments/_index.json — issuer, ticket range, eligibility and application URL.
  3. Decision-makers: personas/_index.json filtered by country and pillar affinity, discarding expired mandates; score = closeness to the decision (layer 1 > 2 > 3) + real-name holder over placeholder + relationship status (warm-intro +1 … hostile −1) + readiness + bonus if the office itself IS the pillar (health minister for salud) + executive bonus. Top 3, each with its own tailored_ask; an empty ask is returned as null so the chat drafts one from the office, never a blank.
• get_discoveries — the proactive radar, with two sources: anomalies from data-health.json (year-over-year jumps and drops, magnitude = |z-score|) and documented contradictions from pulse/contradictions.json (an open one weighs more than a resolved one). Ranked by magnitude, filterable by pillar and country, every finding with its citation_id. It is the tool behind "what should I be watching?".
• Plus the retrieval tools (search_corpus, get_dataset, resolve_metric) that keep every answer grounded.

The full chain, from indicator to decision-maker:

Every arrow is a join over governed corpus assets; citations travel with the result (the pilot's figures carry their citation_id, and capex/opex is declared as a design estimate unless the pilot is field-validated).

From the figure to the decision

The value of pillar 4 is not showing pretty data — it is closing the distance between a verified indicator and an investment or deployment decision. The pilot → financing → decision chain exists so that intelligence does not die in a dashboard, but becomes a bankable project.

The return to the cycle

The companies that profit from this intelligence pay the flywheel back with data and with fees (4 → 1): they fund the operation and bring their own datasets to the Data Trust, recruiting the next cohort of contributors. Frontier intelligence is not the end of the chain — it is what makes the cycle pay for itself.

Related surfaces

• /inteligencia — the hub that organizes the whole suite
• /mercados · /explorar · /movimientos · /sala
• /alertas · /incrustar · /embed/board · /confianza
• /economia-agi · /soberania-computo · /dependencia · /horizonte-2035
• /incertidumbre — V-Dem's democracy indices with their credible interval as a shaded band: real measurement uncertainty, not a point pretending precision. /simulaciones — the frontier-intervention simulator: every persistent regional problem with its modeled pilot, re-runnable by adjusting scope, countries, capital mix and technology path.
• Technical reference for integrators: Public API v1, MCP server, embeds and white-label.

Return to the four-pillar cycle to see how this pillar recruits the flywheel's next turn.