Context engineering for enterprise AI
Ariesnet designs and deploys the Context Supply Chain so enterprise AI agents run on governed, validated, operational context, not ad hoc prompts and scattered data.
Engineering software for enterprises since 1997. Systems that had to keep running under real load, real constraints, and real consequences.
Engagements
Most buyers start at the audit because it is the smallest commitment that produces something a CFO can act on.
Advisory
A half-day session for the executive group that has to decide whether to fund agent work — and on what evidence.
engagement
Diagnostics
Two weeks. One question: what is each unit of agent work actually costing you, and which of that spend is buying nothing?
assessment
Diagnostics
Map your context supply chain, score maturity L0–L5, and prioritize fixes so agents stop failing on missing context.
assessment
Why this works
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.
We build on the third source. The 10–100x 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
Results
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.
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.
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 Sources and structure placed at L0 and L1: the knowledge base held superseded policy documents with no supersession marking, so retrieval was returning them as readily as current ones.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 Context Supply Chain Assessment 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
The audit told us which half of the spend was buying nothing. We had been arguing about that number internally for two quarters.
Almost every engagement we open gets described to us as a model problem. Almost every one turns out to be a sources problem wearing a model problem's clothes.
The ladder
| Rung | Engagement | Price | Basis |
|---|---|---|---|
| 1 | Executive Briefing | $7,500 | Half-day engagement |
| 2 | Token Economics Audit | $15,000 | 2-week fixed engagement |
| 3 | Context Supply Chain Assessment Capacity-limited to 3 engagements per quarter | $32,500 | 3 to 4 weeks — Diagnose $15,000 plus Blueprint $17,500 |
| 4 | Implementation | $60,000 – $120,000 | Per quarter — scope fixed at quarter start |
| 5 | Managed Operations | $8,000 – $15,000 | Per month — quarterly rolling commitment |
Reading
Ariesnet originals and syndicated excerpts, with sources marked.
Ariesnet original
The model is rarely the weak link. What reaches it usually is — and unlike a model, that part is engineerable.
Syndicated · aramai.net
The cheapest reliability discipline available to an agent system: check before you assert.
Originally published on aramai.net
