Evidence infrastructure · AI first

The evidence layer for

Skill Core Labs is evidence infrastructure for AI systems, built to generalize across markets, science, art, music, and culture.
AI

Every AI output makes a claim. Reality decides what it was worth.

Skill Core records what an AI system said, what evidence it used, where its authority ended, and what happened after someone acted on it. The same record can score claims in markets, science, art, music, and culture, but AI is where the infrastructure starts.

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ClaimsRecorded
EvidenceBound
AuthorityScoped
OutcomesChecked

Built to generalize

AI is the wedge. Claims are the substrate.

Skill Core starts with AI because AI systems generate claims at machine scale. But the record is more basic than AI: claim, evidence, authority, context, outcome, validation, trust update, and failure semantics. That structure works anywhere a claim later meets reality.

AI
The core market. A model answer, agent action, recommendation, or tool call becomes a claim with evidence, scope, and an outcome trail.
Markets
A signal or trade thesis can be scored against realized P&L, timing, and drawdown.
Science
A hypothesis can be tied to methods, sources, replication attempts, and later evidence.
Art
An attribution or provenance claim can be tracked against records, ownership, and expert review.
Music
A recommendation or classification can be tested against listening behavior, retention, and context.
Culture
A trend call can be checked against adoption, attention, and what actually persisted.

The operating record

AI output becomes an audit-readable event.

Skill Core keeps the chain intact after the answer was given. The system can later ask whether the claim held, which evidence deserved trust, and where the failure actually happened.

  1. 01
    Claim

    The answer, prediction, decision, recommendation, or action the AI system proposed.

    asserted
  2. 02
    Evidence

    The sources, observations, data, tool calls, and reasoning that supported the claim.

    bound
  3. 03
    Authority boundary

    Where the system could act, where review was required, and what context constrained the answer.

    scope
  4. 04
    Outcome stream

    The later events and measurements that make the original output checkable.

    observed
  5. 05
    Trust update

    The evidence-based change in how much this model, source, or workflow deserves weight next time.

    resolved

Products

Evidence infrastructure for AI systems.

Five product surfaces, one record. Each writes to and reads from the same evidence layer, so AI systems can remember what they claimed and learn from what happened.

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01Skill Core MemoryDurable memory of claims, evidence, source quality, and outcomes across sessions.
02Truth EngineScores what to trust from evidence, contradiction handling, and track record.
03Model Reality LedgerValidation of model outputs against realized outcomes and failure semantics.
04Certified Skill CallsTool and skill calls that carry their own evidence, inputs, outputs, and authority scope.
05Agent GovernanceAuthority boundaries, escalation, and review gates for agents that act.

Pilot

Put one AI decision loop on record.

Pick one workflow where an AI system recommends, acts, or scores. We instrument the claim record end to end and hand back an audit trail your team can actually review.

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  1. 01▸Capture AI claims
  2. 02▸Bind evidence and scope
  3. 03▸Track outcomes
  4. 04▸Update trust