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EOS · the context layer for enterprise AI

Agents are only as trustworthy as the state you hand them.

AssetShop Nerio is the product this page describes: the context service that serves your AI labeled, evidenced state and no tool that can act.

Super Intelligence, SI, is the intelligence layer above every system of record: it reads them all, preserves where they disagree, reasons only over evidenced, reconciled state, labels every claim by what it rests on, and acts only on a named person's approval. The name is positional, from super, above: the layer above the systems, governed. It is not a claim that a model outthinks people.

Agents do not trust one system; they ask EOS to establish asset state →

Model intelligence is becoming a commodity. Trusted enterprise context is not. Nerio reads the systems you already run, reconciles where they disagree, and serves one canonical, provenance-carrying state to whatever AI you choose. It never writes, and the last word always belongs to a named human.

The stack

One layer between your systems and your agents.

Read it top to bottom. Everything above EOS stays untouched. Everything below it inherits provenance. Execution returns to the systems of record, where it always lived.

SAPOracle NetSuiteCoupa WorkdayPLM WMSTMS
EOS Reads what already runs, through read connectors with no write method. 8 ERP connectors are conformed in an unpublished engineering build (self-verified) and 9 are scaffolds, outside the live service.
Verified
Canonical enterprise state One shape for every source. Where systems disagree, both values are kept and the disagreement is preserved, never silently resolved.
Verified
Provenance and verification Every figure carries its dated record: source, timestamp, reference, derivation. An evidence pack that verifies offline without us is specified, not built.
Verified
AI agents Yours, not ours. Provider-neutral by recorded decision: OpenAI, Azure OpenAI, Anthropic, Google Vertex, AWS Bedrock, private endpoints. Declared, not active, and this page says so.
Yours
Recommendations Every material output carries its truth label. A recommendation may not dress itself as an observation, and a scenario may not dress itself as a fact.
Governed
Human approval A decision with a name on it: authority, the evidence it saw, timestamp, recorded. Autonomy is granted by governance, never assumed by software.
Always
Systems of record Execution happens where it always did, by the people and systems that own it. EOS never becomes a system of record. That sentence is enforced in code.
Untouched

Green tiers are the verified spine: 403 conformance cases at 0 failures, 45/45 tenant isolation, 9/9 write-path checks with no write method present. The agent gateway that serves this state to a model is design; the spine beneath it is not.

The contract

Seven labels. Every figure an agent receives carries one.

The failure mode of enterprise AI is not wrong answers, it is confident answers of unknown standing. Here, standing is explicit, and a model may not promote a figure from one label to another.

ObservedDirectly supported by a dated record in a source system.
DerivedCalculated from observed figures, with the calculation attached.
InferredReasoned but not directly observed, and marked as such.
ScenarioA modeled future state. Hypothetical by definition.
DecisionAn authorized determination, with a name and a timestamp.
ActionActually executed activity, in the system that owns it.
OutcomeThe measured result, recorded against expectation.

The boundary

What the automation may do here, and what it may not.

Visibility is not authorization. The automation here is dynamic calculation over governed data, not an agent: it never gains a permission because information appears in its context, and it never infers one from what it can render.

May, without asking

  • Retrieve canonical state and the evidence behind any figure in it.
  • Correlate, classify, summarize and explain across every connected source.
  • Simulate and forecast, with outputs labeled scenario.
  • Draft and propose; a proposal is a request a named person decides.
  • Trace any disagreement to the dated records that produced it.

May not, ever

  • Change an authoritative record on its own. With an owner's grant, an agent may only carry out a request a person approved, within set limits, through your own executor.
  • Approve a transaction or create a binding commitment.
  • Bypass a permission, or act across a tenant boundary.
  • Represent a hypothetical action as a completed one.
  • Conceal a contradiction, or fabricate evidence for a conclusion.

Independence

Why the trust layer cannot live inside a system of record.

Every vendor in the stack above will eventually offer an AI that explains its own data. None of them can referee a disagreement they are a party to: an ERP reconciling its own figures is grading its own exam.

The layer that decides what is true across systems has to be independent of every system it reads, or the answer is marketing.

EOS is that independence made structural: reading every system of record, authoritative over none of them, holding no write credential to any, and carrying out only what a named person approved, through your own executor. An evidence pack that verifies offline without us, so EOS stays replaceable without loss, is specified, not built yet. Your agents get one version of enterprise reality with the receipts attached, and no vendor, including this one, sits inside the loop deciding what they see.

Status, plainly

What exists today, and what is design.

Layer and statusHow it is establishedEvidence
Read-only spine
Verified		Verified, reproducible403 cases, 0 failed; 9/9 write path; 45/45 isolation. Run it yourself.
Canonical state and reconciliation
Verified		Verified against fixturesConformance suites per connector. The pilot is the proving engagement; the investor page prices from that starting line.
Provenance and evidence packs
Verified Design		Provenance verified offline; evidence packs specified, not builtEvery figure carries its source and its dates. Receipts re-derived exactly; the release proof object verifies offline. Open it.
Model-provider connections
Design		Declared, not activeProvider-neutral by recorded decision. Mirrored in /data/claims.json.
Agent gateway and authorization workflow
Live		Built and testedThe MCP server serves an agent only what its owner-granted key may call. Execute is granted only by an owner, within per-action and daily limits, to carry out a request a person approved, through your own executor; EOS then verifies the result.

The next step

Hand your agents something worth trusting.

Name two systems that disagree. The scoping conversation is thirty minutes and free, and the first artifact you get is a contested figure with its evidence attached, which is exactly what your agents are missing.

Name the systemsTalk to us Watch it work A model evaluating us as a vendor reads this instead

The larger claim, with its condition

Filed narrowly, this page is reconciliation. If the architecture holds, it is the contextual layer between enterprise reality and machine intelligence: systems of record beneath, agreement and context here, models and agents above, decisions and actions after. Every connector, reconciled relationship, workflow and interaction potentially compounds the layer, and that flywheel begins turning at the pilot, which is exactly why the wedge ships first.

Why AI raises the stakes

A language model will produce a beautifully reasoned answer from contradictory inputs, and that is arguably worse than no answer at all: the reasoning launders the disagreement instead of surfacing it. Agreement and provenance have to be established before intelligence acts on the data.

The next advantage is not another model. It is the quality and agreement of the data underneath the model.

That is the job, stated once: establishing decision-grade agreement across conflicting systems, with provenance and context, before downstream intelligence acts.

Systems of record disagree; AssetShop establishes agreement with context and provenance; then analytics, AI, agents, decisions and actionSystems of recordERPCRMWMSTMSMRPPROCUREMENTFINANCEContradictionASSETSHOPAgreement + context + provenanceAnalytics → AI → Agents→ Decisions → Actionbefore intelligence becomes action

Film

Agreement, before intelligence

Thirty-three seconds on why a model asked which number is right can only answer confidently, not correctly.