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PointSav Documentation

The engineering library for the PointSav platform — operating systems and services for regulated businesses that own their data, their AI, and their record-keeping outright. Where the monorepo holds the code, this wiki holds the reasoning: architecture, services, security, and the governance commitments that bind future development.

PointSav Digital Systems

Trajectory substrate

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6fd838d6 · PointSav Digital Systems ·

docs(documentation): rename fabricated osc-sn-51-721 citation ID to np-51-201 workspace-wide -- following Command's citations.yaml fix (commit c6be400, real workspace-wide OSC SN 51-721 sweep), this wiki's citation ID and ~59 prose/frontmatter references to the fabricated Ontario-specific staff notice were left dangling; renamed to the real CSA National Policy 51-201 entry Command's fix established, matching the same instrument corporate wiki's dad5b0a fix now cites. Mechanical ID rename + prose phrase swap only, no new claims (EN+ES, 61 files).

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@@ -10,11 +10,11 @@ index_group: core-named-substrates
short_description: "The platform mechanism converting operational work — commits, sessions, feedback — into structured JSONL training tuples feeding a continued-pretraining corpus."
status: active
bcsc_class: public-disclosure-safe
last_edited: 2026-07-18
last_edited: 2026-08-24
editor: pointsav-engineering
cites:
 - ni-51-102
 - osc-sn-51-721
 - np-51-201
 - constitutional-ai-2212-08073
 - federated-lora-2502-05087
 - s-lora-2024
@@ -29,7 +29,7 @@ Three orthogonal corpus types determine the architecture. The constitutional cor

Capture is automatic — no operator decision is required to generate a training tuple. Every JSONL record carries provenance fields (`tuple_type`, `doctrine_version`, `tenant`, `role`, `scope`, `redaction_class`) that let the training pipeline assemble each corpus without trusting prose. Vendor data never co-mingles with customer data at training time; tenant data never crosses tenants — the separation is directory-level and pipeline-level, not policy-level.

An operator's specific correction shapes their own cluster's adapter exclusively — no other tenant benefits or is burdened by it. This per-contributor inverse of aggregate preference learning is structurally inaccessible to platforms whose preference signal is averaged across all users. Per `[ni-51-102]` and `[osc-sn-51-721]`, the continued-pretraining pipeline is described in planned terms; the capture infrastructure and corpus accumulation are operational today.
An operator's specific correction shapes their own cluster's adapter exclusively — no other tenant benefits or is burdened by it. This per-contributor inverse of aggregate preference learning is structurally inaccessible to platforms whose preference signal is averaged across all users. Per `[ni-51-102]` and `[np-51-201]`, the continued-pretraining pipeline is described in planned terms; the capture infrastructure and corpus accumulation are operational today.

## Overview

@@ -107,7 +107,7 @@ This per-contributor inverse of aggregate preference learning is structurally in

## Configuration

The Trajectory Substrate currently operates at L1 (edit-corpus capture, live since v0.1.1). Per `[ni-51-102]` continuous-disclosure language, and in accordance with the forward-looking information principles of `[osc-sn-51-721]`, the statements below describe planned and intended development. Material assumptions: continued-pretraining technology matures on the current trajectory; OLMo 3 base model `[olmo3-allenai]` remains openly licensed; engineering corpus accumulates at the current platform development cadence. Actual outcomes may differ.
The Trajectory Substrate currently operates at L1 (edit-corpus capture, live since v0.1.1). Per `[ni-51-102]` continuous-disclosure language, and in accordance with the forward-looking information principles of `[np-51-201]`, the statements below describe planned and intended development. Material assumptions: continued-pretraining technology matures on the current trajectory; OLMo 3 base model `[olmo3-allenai]` remains openly licensed; engineering corpus accumulates at the current platform development cadence. Actual outcomes may differ.

Planned subsequent tiers:
Important Information

Corporate structure. PointSav Digital Systems ("PointSav") is currently a trade name of Woodfine Capital Projects Inc. ("Woodfine"), planned to become a wholly-owned Woodfine subsidiary upon incorporation. PointSav does not itself offer, sell, or solicit any security. Any securities offering associated with Woodfine's real-property direct-hold solutions is made exclusively by Woodfine, and only by means of the applicable Private Placement Memorandum.

No investment advice. This wiki's content is provided for engineering, operational, research, and development purposes. Nothing on this wiki constitutes investment advice or a solicitation to invest in any Woodfine partnership or direct-hold solution.

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