Make the marketRenders any source into schema-conforming recordsMakes data comparable

Scribe

Makes any source speak the same language.

StatusBuild
FIG · Mismatched sources pass through alignment into schema-conforming records.

Scribe is the agentic conformance engine. Its single verb is render: take any data source (a scanned document, a sensor feed, a government registry, a legacy database) and turn it into records that conform to the shared schema. It owns reconciliation (resolving who and what a record refers to) and commoditisation (mapping arbitrary sources onto the standard). Every rendered record keeps its provenance and lineage, so a conformed fact never loses the trail back to where it came from.

One engine, two targets. Rendered against the cross-system schema, a source becomes a tradable commodity. Rendered against a Frame app’s configuration, it becomes data that app can ingest. The target is a parameter; the engine is one, and it runs a continuous render / evaluate / learn loop that accumulates reusable mapping knowledge per source.

Scribe is what lets DAAC reach data it did not capture (the registries and platforms that already exist but cannot talk to each other) and make it comparable to the data the ecosystem records first-hand.

Where it sits. Sibling to Spine; neither depends on the other. Consumes the shared conventions as render targets; needs no Frame app to run.

01What it does
01
Render anything
Documents, feeds, and legacy databases become structured, schema-conforming records.
02
Reconcile & commoditise
Entities resolve to one identity; arbitrary sources map onto the shared standard so they can be priced.
03
Improves with use
Human corrections accumulate into a reusable corpus, so accuracy compounds; the improvement stays inspectable, and what Scribe learns from a client stays theirs.
02How it works
FIG · One verb, render, and a loop that learns from every correction
  1. 01Render
    Map + reconcile

    Map a source’s constructs to the target schema; resolve its entities to identity records, provenance intact.

  2. 02Evaluate
    Score the render

    First-pass conformance and per-mapping confidence; low-confidence mappings are flagged, not accepted.

  3. 03Critique
    Refute, don’t rubber-stamp

    An independent critic re-examines each mapping to catch false friends, grounded, yet semantically wrong.

  4. 04Improve
    Capture the why

    Corrections are captured with rationale, context and priors, from a human or a higher-trust agent.

  5. 05Learn
    No model retrain

    Corrections feed the next render; per-source templates compound in prompts, rules and corpus.

corrections compound into the corpus, the corpus is the model

03How it connects
FIG · Point the same Tool at the market’s schema, or at a single app, it’s conformance either way
Feeds inHands onConsoleobservesrenderorganised sourcesAny sourceexternalCrawltooltradable →ingestible →SpinetoolFrame appcapture the fieldScribeRenders any source into schema-conforming recordsMakes data comparable
Scribe, the subject hereCopper edges, what it hands on to the marketDashed leads, tooling that watches, never steers
04What it’s for
FIG · Supply is worthless until it’s comparable, this is what makes it comparable
ScribeReach data you didn’t captureany source, renderedComparable & priceableonto one standardApp ingestionrender to a Frame configIntelligencethe demand-side payoffpriced access →· Risk management· Impact measurement· Due diligence· Opportunity assessment
05What it reports
Indicative telemetry
Nothing measured yet, these are the signals it will answer for
Born wired · awaiting first run
Corrections per datasetindicative

the falsifiable proof it learns, must trend down

First-pass conformanceindicative

share of fields auto-validating before human touch

Calibration errorindicative

do confidence scores match actual correctness

Fabrications caughtindicative

false-friend mappings the critic rejects

Critic-agreement rateindicative

watched for drift toward rubber-stamping

These are the vital signs Scribe is designed to report, not measured results. Until it runs, its Console panel is schema-stubbed and clearly labelled. No panel fakes data.

06Where it does the work