Hardware co-location methodology

@@ -9,8 +9,8 @@ title: "Hardware co-location methodology"
category: architecture
index_group: location-intelligence-and-domain
language: en
quality: complete
status: stable
quality: stub
status: planned
bcsc_class: public-disclosure-safe
last_edited: 2026-08-03
editor: pointsav-engineering
@@ -18,7 +18,7 @@ paired_with: hardware-co-location-methodology.es.md
cites: []
---

The co-location methodology is the structured approach [[pointsav-overview|PointSav]] uses to score and rank physical co-location opportunities for customer deployments. It combines market data, regulatory context, and infrastructure characteristics drawn from the platform's intelligence pipeline and [[compounding-substrate]] to produce a ranked list of candidate sites for a given deployment requirement.
The co-location methodology is a planned structured approach for scoring and ranking physical co-location opportunities for customer hardware deployments — jurisdiction, network transit, infrastructure fit, and cost, in that priority order. No automated scoring pipeline exists yet; the methodology below states the intended design, not a shipped system. (This is a distinct concept from the platform's real-estate co-location scoring, used for commercial-property site selection — a different domain with its own, separately-implemented methodology.)

## What co-location means in this context

@@ -42,11 +42,9 @@ Co-location scoring operates within the development-regions taxonomy. A developm

This scoped approach reflects the platform's deployment philosophy: sovereignty and regulatory compliance are architectural constraints, not post-selection considerations. Candidate sets are bounded before scoring begins.

## Intelligence pipeline role
## Status

The scoring computation draws on structured market data ingested by the platform's intelligence pipeline. Facility profiles — power ratings, transit providers, compliance certifications, pricing structures — are maintained as structured records in the [[three-ring-architecture]] Ring 1 data layer. The pipeline refreshes these records on a defined schedule; scores reflect the most recently ingested facility state.

Operator review is the final step. The intelligence pipeline produces a ranked candidate list; the operator selects from that list. The methodology does not automate site selection; it compresses the search space and makes the trade-offs explicit before the operator decides.
No facility-scoring pipeline or structured facility-profile data store exists today. The dimensions above describe the intended design; a candidate list is currently assembled and scored by an operator manually, not generated by an automated pipeline.

## See also