Gravity engine

@@ -9,10 +9,10 @@ status: active
audience: vendor-public
bcsc_class: public-disclosure-safe
language_protocol: PROSE-TOPIC
last_edited: 2026-05-15
last_edited: 2026-05-22
editor: pointsav-engineering
paired_with: service-content.es.md
short_description: "service-content is the synthesis engine of the PointSav family — the Gravity Engine that reads raw payloads from a Totebox, runs them against an institutional taxonomy, and generates the structured documents an organisation publishes."
short_description: "service-content is the synthesis engine of the PointSav family — the Gravity Engine that reads raw payloads from a Totebox, runs them against an institutional taxonomy, and generates the structured documents an organisation publishes, with a human-in-the-loop boundary on every verified ledger."
cites: []
references:
 - id: 1
@@ -23,39 +23,45 @@ references:
 url: "https://www.ilo.org/public/english/bureau/stat/isco/isco08/"
---

`service-content` is the synthesis engine of the PointSav family. It reads raw payloads from inside a Totebox, runs them against an institutional taxonomy, produces dense Gravity Vectors, and ultimately generates the documents an organisation publishes — wikis, news releases, due-diligence ledgers, internal memos. It is the difference between a file store and an active intelligence engine. This article covers the Four-Pillar Seed Vault, the Gravity Engine pipeline, the Stratified Ontology, and the human-in-the-loop boundary that governs what the engine can write autonomously.
An organization's files hold its knowledge but do not surface it. An email archive, a folder of contracts, a store of PDFs — each is searchable, and none of it knows who relates to whom, which concerns recur, or what the organization's own terms mean.

<!--claim id=gravity-engine confidence=structural cites=[]-->`service-content` — the Gravity Engine — is the synthesis engine of the PointSav family. It reads raw payloads from inside a Totebox, runs them against an institutional taxonomy, produces dense Gravity Vectors, and generates the documents an organisation publishes: wikis, news releases, due-diligence ledgers, internal memos.<!--/claim-->

The engine is built on a Four-Pillar Seed Vault — the institutional grammar — a deterministic ingest-to-routing pipeline, and a five-layer Stratified Ontology. It is the difference between a file store and an active intelligence engine.

For a regulated buyer, one boundary governs all of it. <!--claim id=human-loop-boundary confidence=structural cites=[]-->`service-content` does not publish to a verified ledger autonomously; every verified entry transits a human-in-the-loop verification step first.<!--/claim--> This article covers the Seed Vault, the Gravity Engine pipeline, the Stratified Ontology, and that boundary.

## The Four-Pillar Seed Vault

Every Totebox is provisioned with four JSON ledgers that form the institutional grammar of the system — the Seed Vault. The Seed Vault is built once, locked, and adjusted only by operators, not by AI:
<!--claim id=seed-vault confidence=structural cites=[]-->Every Totebox is provisioned with four JSON ledgers that form the institutional grammar of the system — the Seed Vault. The Seed Vault is built once, locked, and adjusted only by operators, never by AI.<!--/claim-->

| Pillar | What it captures | Example |
|---|---|---|
| Entities | The "who" — every individual, organisation, email, and phone number extracted from payloads | A Sovereign-ID keyed to a specific contact's email hash |
| Entities | The "who" — every individual, organisation, email, and phone number extracted from payloads | A Sovereign-ID keyed to a contact's email hash |
| Archetypes | The "logos" — eleven functional personas (Executive, Guardian, Fiduciary, Architect, Engineer, Artisan, Constructor, Catalyst, Envoy, Steward, Sage) | A façade consultant maps to "The Engineer" |
| Chart of Accounts | The "where" — the rigid hierarchical structure of the organisation (Profile → Domain → Sub-Domain) | Construction → Collaborators → Façade Consultants |
| Domains | The "what" — the localised vocabulary and glossary terms in use | "Direct-Hold Solution" |

A fifth pillar, Themes, captures the multidimensional undercurrents emerging from the corpus — recurring topics and institutional concerns that are flagged and proposed to operators for approval.
A fifth pillar, Themes, captures the recurring topics and institutional concerns emerging from the corpus — flagged and proposed to operators for approval.

## The Gravity Engine pipeline

When a payload arrives (an email, a PDF, a DOCX), the engine performs a tight, deterministic sequence:
When a payload arrives — an email, a PDF, a DOCX — the engine runs a tight, deterministic sequence.

1. **Ingest.** `service-email` (or the [[app-console-input|Input Machine]]) writes the raw payload to the Totebox's WORM vault. Nothing is mutated yet.
2. **Entity extraction.** `service-extraction` runs Aho-Corasick over the raw text and pulls out every name, email, phone number, and organisation. [^1] Each gets a deterministic Sovereign-ID and begins in `Discovery` status.
3. **Gravity calculation.** `service-content` loads the four pillars, fuses them into a single search automaton, and extracts only sentences containing structural keywords. A 5 MB payload becomes a 50-word Gravity Vector.
1. **Ingest.** `service-email`, or the [[app-console-input|Input Machine]], writes the raw payload to the Totebox's WORM vault. Nothing is mutated yet.
2. **Entity extraction.** `service-extraction` runs Aho-Corasick string matching [^1] over the raw text and pulls out every name, email, phone number, and organisation. Each receives a deterministic Sovereign-ID and begins in `Discovery` status.
3. **Gravity calculation.** `service-content` loads the four pillars, fuses them into a single search automaton, and extracts only the sentences containing structural keywords. <!--claim id=gravity-vector confidence=structural cites=[]-->A 5 MB payload becomes a 50-word Gravity Vector.<!--/claim-->
4. **Verification.** The vector and the entity bundle pass to `service-slm`, which evaluates whether the payload aligns with the institution's taxonomy. The output is a single token — `VALID` or `REJECT`.
5. **Routing.** Verified payloads go to deep storage; rejected payloads go to quarantine. Verified entities have their `Discovery` status upgraded and are socketed to the Chart of Accounts.

## The Stratified Ontology

`service-content` organises information in a five-layer stack, with three derivative engines on top:
`service-content` organises information in a five-layer stack, with three derivative engines on top.

| Layer | What it holds |
|---|---|
| L0 — Base Geometry | Raw files (`/source`, `/assets`, `/ledger`) — immutable, local |
| L1/L2 — Raw Graph | Blind 100% extraction into a JSONL knowledge graph |
| L1/L2 — Raw Graph | Blind full extraction into a JSONL knowledge graph |
| L3 — Global Anchors | Universal standards (IFRS/GAAP for finance, O*NET/ISCO for personnel) [^2] |
| L4 — Sovereign Taxonomy | The institution's bespoke operational reality — Domains, Glossaries, Topics |
| L5 — Verification Crust | The human-in-the-loop truth ledger; outputs from operator-verified content only |
@@ -68,11 +74,13 @@ When a payload arrives (an email, a PDF, a DOCX), the engine performs a tight, d

## Self-healing

The Gravity Engine is self-healing in a specific, narrow sense: its own outputs feed back into its inputs. A successfully synthesised memo becomes new content the engine indexes the next time it runs. Over months, the Domain glossaries grow, the Themes track real institutional concerns, and the engine's syntheses converge on the institution's actual voice. This is not autonomous model fine-tuning — the improvement is corpus-level: the engine has more verified material to draw from.
The Gravity Engine is self-healing in a specific, narrow sense: its own outputs feed back into its inputs. A successfully synthesised memo becomes new content the engine indexes on its next run. Over months, the Domain glossaries grow, the Themes track real institutional concerns, and the engine's syntheses converge on the institution's actual voice. This is not autonomous model fine-tuning — the improvement is corpus-level: the engine has more verified material to draw from.

## The human-in-the-loop boundary

`service-content` does not publish to verified ledgers autonomously. Every L5 entry must transit a human-in-the-loop verification step — either through the [[app-console-input|Input Machine]] (F12) or through the content review surface — before it can be written to a verified ledger. This satisfies [[architecture-decisions|SYS-ADR-07]] (no structured data through AI) and [[architecture-decisions|SYS-ADR-19]] (no automated AI publishing to verified ledgers).
<!--claim id=adr-compliance confidence=structural cites=[]-->Every L5 entry must transit a human-in-the-loop verification step — through the [[app-console-input|Input Machine]] (F12) or the content review surface — before it can be written to a verified ledger. This satisfies SYS-ADR-07 (no structured data through AI) and SYS-ADR-19 (no automated AI publishing to verified ledgers).<!--/claim-->

The boundary is what makes the engine usable in a regulated setting. The engine accelerates synthesis; a human, not the engine, commits the result to a ledger of record.

## See also