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Alpine DataWorks
The honey hole

What an intelligence object actually is

We don't sell data. We sell answers with context.

Raw data is crude oil. Every team that touches it pays to refine it. An intelligence object is the refined answer — scored, explained, and ready for one call.

The refinery in three steps

Crude data in. Confident answer out.

The gap between having data and having an answer is where most teams bleed time and budget. Alpine closes it.

Step 01

Raw Data

30+ series across FRED, BLS, BEA, Census — each with its own units, vintage, revision cadence, and seasonal-adjustment flag. Valuable, but not an answer.

FREDBLSBEACensus

Step 02

Enrichment

Normalize units. Align vintages. Compute YoY growth. Decompose into drivers. Validate the composite score against a naive baseline before it ships.

NormalizeScoreValidate

Step 03

Intelligence Object

One scored answer. Score + Drivers + Confidence — plus freshness, coverage, and recommended use. The argument travels with the conclusion.

ScoreDriversConfidenceFreshness
See the difference

30 rows of raw data vs 1 scored answer

Toggle between what you would have to wrangle — and what Alpine gives you instead.

1 call · 1 JSON object · score + drivers + confidence · ready for your agent.

GET /v1/intelligence/adw-002 
{
  "product_id": "ADW-002",
  "entity": "US",
  "score": 68,
  "trend": "stable",
  "confidence": 0.92,
  "top_drivers": [
    {
      "factor": "GDP Growth",
      "contribution": 0.4
    },
    {
      "factor": "Unemployment Rate",
      "contribution": 0.35
    }
  ],
  "prediction_horizon": "3m",
  "recommended_use": "Macro asset allocation",
  "methodology_version": "v3.0",
  "freshness": "2026-06-01T06:00:00Z",
  "coverage": "National",
  "health_score": 68,
  "inflation_pressure": "moderate",
  "labor_market_status": "tight",
  "growth_momentum": "positive",
  "data_lag_days": 2,
  "source_lineage": [
    "FRED",
    "BLS",
    "BEA"
  ],
  "allowed_use": "evaluation, commercial"
}
Why it matters

The answer your stack was always missing

Stop rebuilding the same analysis

Every team that needs a macro health read builds the same ingestion, normalization, and scoring pipeline. That is duplicated cost. An intelligence object buys that back.

One call, full reasoning

The score, its top drivers, confidence, and freshness all travel together. You do not get a number — you get a position with its argument attached, ready for an agent or a CFO.

Typed, versioned, agent-discoverable

A fixed IOM schema means every consumer — app, marketplace, or AI agent — gets the same contract. Version it. Cache it. Register it in your MCP catalog. It just works.

Ready to stop wrangling and start reasoning?

Browse the full product catalog, or read the agent-access docs to make your first call.

See the products Read the docs