AssetShop · EOS
Product

EOS · the context layer for enterprise AI

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

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

Model intelligence is becoming a commodity. Trusted enterprise context is not. EOS 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. No write method exists to disable. 8 connectors conformed, 9 declared, and each carries its own conformance suite.
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. Exported evidence packs verify offline, without us, forever.
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, alternatives considered, 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

Nine 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.
AssumedIntroduced for analysis, listed so it can be argued with.
ScenarioA modeled future state. Hypothetical by definition.
RecommendationA proposed action from the model, awaiting authority.
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 recommend, with outputs labeled recommendation.
  • Trace any disagreement to the dated records that produced it.

May not, ever

  • Change an authoritative record. No write method exists to call.
  • 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: read-only toward everything, authoritative over nothing, replaceable without loss because the evidence packs verify offline without us. 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.

LayerStateEvidence
Read-only spineVerified, independently executed403 cases, 0 failed; 9/9 write path; 45/45 isolation. Run it yourself.
Canonical state and reconciliationVerified against fixturesConformance suites per connector. The pilot is the proving engagement; the investor page prices from that starting line.
Provenance and evidence packsVerifiedReceipts re-derived exactly; packs verify offline. Open one.
Model-provider connectionsDeclared, not activeProvider-neutral by recorded decision. Mirrored in /facts.json.
Agent gateway and authorization workflowDesignSpecified against the labels and boundary above. Ships with the pilot engagement.

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.

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.

The signature diagram: systems of record, contradiction, AssetShop agreement plus context plus provenance, then analytics, AI, agents, decisions, action

Film

Agreement, before intelligence

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