Fabriq — spend to outcome
A three-step left-to-right flow. Step one: metadata-only input from AI tools - cost, usage, identity, and timestamps, never content. Step two: mapped to org context such as roles, teams, and cost centers. Step three: confidence-rated signals answering the board's AI-spend questions. Below, confidence is shown as a first-class output: GREEN, AMBER, and RED grades rendered as labeled ink badges with solid, dashed, and hairline borders, each paired with what the grade means and what's missing.
FABRIQ · AI CAPITAL INTELLIGENCE
From AI spend to board-credible answers
Metadata in, org context applied, confidence-rated signals out — and every signal says how sure it is, and what's missing.
STEP 1 · INPUT
AI-tool metadata
Cost. Usage volume. Identity.
Timestamps. The accounting
trail of every AI tool —
never the content itself.
METADATA ONLY
metadata only
STEP 2 · CONTEXT
Mapped to the org
Spend and usage are crossed
with who and where: roles,
teams, cost centers — the
workforce structure encoded
in the data layer, identity
matched across systems.
STEP 3 · OUTPUT
Confidence-rated signals
The board's AI-spend questions,
answered: who is spending what,
where it lands in the workforce,
what's governed and what isn't.
Every signal carries its grade
— and what's missing.
CONFIDENCE IS THE PRODUCT · EVERY SIGNAL IS GRADED
GREEN
Stand on it
Sources agree, coverage is fresh
and complete. Board-ready as is.
MISSING: NOTHING MATERIAL
AMBER
Use with the caveat
Directionally solid, but a source is
stale or coverage is partial.
MISSING: NAMED — E.G. A STALE FEED
RED
Don't lean on it yet
Too thin to defend. The signal still
shows — flagged, never dressed up.
MISSING: THE GAP ITSELF IS THE ANSWER
"It never reads what your people typed — and it always says how sure it is."
Metadata-only by architecture. Confidence-rated by design. The grade — and the gap — ship with every answer.
METADATA ONLY · TENANT-ISOLATED DATA LAYER · 19 COMMITTED MIGRATIONS
Fabriq
Spend to outcome, with the confidence attached. Fabriq takes the metadata of AI-tool use - cost, usage, identity, timestamps, never the content - and maps it to org context: roles, teams, cost centers. Out come confidence-rated signals that answer the board's AI-spend questions, each carrying its grade (GREEN - stand on it; AMBER - use with the caveat; RED - don't lean on it yet) and a plain statement of what's missing. The grade and the gap are the product, not a footnote.