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Enterprise intelligence

Enterprise intelligence, built on the Context Supply Chain.

Your schemas, connected into one governed Core Model — and the supply chain around it designed, integrated and operated by Ariesnet, so AI can build across everything you already run.

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Built on CoreModels®, the platform from ARAMAI, Ariesnet's product group, that holds the mappings and governed meaning between your schemas.

The Context Supply Chain
  1. Sources
  2. Structure
  3. Semantics
  4. Validation
  5. Retrieval
  6. Agent Operations
  7. Learning Loop

The platform in the middle

The structure your organization already authored

The context that matters most is the structure your organization already authored: its schemas, data models and canonical definitions. CoreModels® holds the mappings and governed meaning between them. Ariesnet builds and operates the supply chain around it.

Integrated into the systems you already run. Ariesnet integrates the Context Supply Chain into the systems you already operate — your sources, your schemas, your agent platforms — so governed context reaches every agent without replacing anything that works.

One governed Core Model behind one endpoint your MCP tools call on your own key, with connectors assessed against the systems your agents already hit and operations that keep entity definitions from drifting — measured on wrong-tool and wrong-field incidents and eval pass rates.

Engagements

Every engagement starts with an interview

A structured conversation about your schema landscape: what your agents read today, what needs to plug in, and where the disconnects are. It ends in a working map of your context supply chain — the connectors you need and the wiring and remediation it will take to reach one governed Core Model — yours whether or not you go further. The map names the failure classes it surfaces: wrong tool, wrong field, stale definition.

Request an interview See the engagements
  1. Diagnostic Briefing $4,500
  2. Agent Context Cost & Failure Audit $12,000
  3. Core Model Blueprint $32,500
  4. Implementation $75,000
  5. Managed Operations $9,000 – $18,000
  6. What each engagement includes →

Why this works

The graph your enterprise already wrote

Every serious AI context effort eventually needs a structural map — a graph of what your business objects are and how they relate. There are three places that graph can come from.

  1. A model can extract it from your documents. Fast — and a statistical estimate. The graph inherits every error the extractor made, and no amount of downstream reasoning makes it authoritative afterwards.
  2. Specialists can engineer an ontology by hand. Authoritative — and famously slow and expensive.
  3. Or it can be recovered from structure your organization already authored: the schemas, data models and canonical definitions your teams have fought over in meetings and shipped to production. Deliberate, governed, and authoritative by construction, at a fraction of the cost of either alternative.

We build on the third source. The order-of-magnitude indexing overhead you have heard attributed to 'graphs' belongs specifically to text-first extraction — reconstructing by inference structure that was never captured. Where structure already exists, the graph is not an expense to justify. It is an asset being recovered.

It is also why the conformance gate in Stage 4 can be authoritative at all: content is checked against a contract your own organization authored, not against a model's guess.

One honest boundary: this presupposes structure exists. For genuinely unstructured prose, vector retrieval may be entirely adequate — and we will tell you when it is. Discover by similarity; retrieve by structure.

Evidence

What an engagement looks like

Results

Establishing a retrieval baseline before scaling an agent rollout

An assessment engagement that stopped a rollout, measured the actual failure, and restarted it on a defensible baseline.

Client not named — engagement covered by a confidentiality term.

Problem

An internal support agent was performing well in demonstration and poorly in production. The team had assumed a model limitation and had begun scoping an upgrade. No evaluation harness existed, so nobody could say which answers were wrong or why.

Approach

We assessed the seven stages against the live estate. Sources and structure placed at L0 and L1: the knowledge base held superseded policy documents with no supersession marking, so retrieval was returning them as readily as current ones. We built an evaluation set from known-correct answers and measured the baseline before proposing any change.

  • 7 stages

    Assessed against live systems

    Scope of the assessment engagement as delivered.

  • L0–L1

    Sources and structure placement

    Maturity placement recorded during assessment, evidence attached in the report.

  • 3 weeks

    Kickoff to blueprint

    Actual elapsed engagement time.

The finding was not that the model was weak. It was that no stage of the supply chain distinguished a current policy document from a superseded one, so the retrieval layer had no signal to rank on.

In their words

How this gets described to us

Reading

The thinking behind the method

Ariesnet originals and syndicated excerpts, with sources marked.

Syndicated · aramai.net

Look up before you make up

The cheapest reliability discipline available to an agent system: check before you assert.

Originally published on aramai.net

All resources

Next step

Find out what your context is actually costing you.

We work with enterprise AI and data leadership teams.

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