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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

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TUI as corpus producer

The TUI-as-Corpus-Producer pattern designates the operator terminal interface (slm-cli) as a primary source of high-quality training data for the per-tenant model adapter. Every interaction with the Doorman through this interface is a curated corpus contribution.

Correction (2026-08-02, verified against canonical origin/main): slm-cli is not a real component name — a corpus-wide grep for slm-cli/slm_cli across every .rs/.toml file in canonical returns zero hits. The real service-slm/crates/ set is adapter-hub, slm-core, slm-doorman-server, slm-doorman, slm-mcp-server — no CLI/TUI crate. The real operator-facing SLM surface is app-console-slm (a real Active crate, "SlmCartridge (F9); Doorman health dashboard + entity count," per this archive's own project registry). This invented name is also used, with equal confidence, in Tier 0 customer-side sovereign specialist — a corpus-wide sweep for other slm-cli occurrences is worth doing in a future pass. The underlying corpus-production concept described in this article is plausible; only the component name is fabricated. Flagged, not resolved.

Why terminal interactions are high-quality training data

Three properties distinguish system administration and IT-support interactions from general training data:

Verifiable ground truth. When an operator follows AI advice — running a suggested command, applying a proposed configuration change — the system either recovers or it does not. Other domains such as creative writing or strategic reasoning lack this immediate-feedback property. IT-support has it by default. The operator knows immediately whether the response was correct.

Narrow domain. Archive operations, system conventions, and customer-specific workflow vocabulary form a bounded command set and failure-mode space. Models train more efficiently on bounded domains than on general corpora because the signal-to-noise ratio is higher.

Domain-expert feedback. The operator issuing a verdict is the person who knows whether the response was correct — not a proxy labeler separated from the actual work. Published reinforcement-learning-from-human-feedback literature consistently reports that high-quality verdict-signed interaction tuples train an order of magnitude more efficiently than observation-only tuples. 1

The /feedback mechanism

After every assistant response in the TUI, the operator is offered three explicit verdicts:

Good. The response was correct and useful. The tuple is flagged as a positive direct preference optimisation example.

Refine. The response was close but needed adjustment. The operator provides a correction inline; the tuple captures the response-and-refinement pair as training signal.

Bad. The response was wrong. The tuple is flagged as a negative direct preference optimisation example.

If the operator dismisses without providing a verdict, the tuple is captured as unsigned and contributes to supervised fine-tuning but not to direct preference optimisation.

Adapter quality budget

Published fine-tuning literature suggests 200 to 500 high-quality verdict-signed interactions are sufficient for a first adapter training cycle in a narrow domain. 2 The platform's intended sequence for each tenant is: accumulate signed interactions from dogfood operations, train the first per-tenant adapter via the LoRA training pipeline, apply a validation quality gate, and promote the adapter to the deployment. Each subsequent training cycle incorporates additional interactions, progressively tuning the adapter to the customer's specific environment — their systemd units, their seed taxonomy, their workflow vocabulary.

Per-tenant adapter ownership

The corpus produced by a customer's operators trains that customer's adapter, not a general adapter. Per the Customer-owned graph IP convention, the trained adapter weights are the customer's property. The platform distributes the model architecture and the training pipeline; the customer retains the trained adapter that results.

Verdict capture discipline

Some terminal sessions should not contribute to the training corpus: test sessions initiated with a no-corpus flag, sessions interrupted by unavailable tiers before completion, and sessions using forced-tier debug mode are audit-logged but excluded from normal training data. The boundary between operational corpus and test corpus is enforced at the Doorman's verdict intake endpoint.

See also

  1. Rafailov, R. et al. 'Direct Preference Optimization: Your Language Model is Secretly a Reward Model.' NeurIPS, 2023. https://arxiv.org/abs/2305.18290 ↩

  2. Zhou, C. et al. 'LIMA: Less Is More for Alignment.' NeurIPS, 2023. https://arxiv.org/abs/2305.11206 ↩

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.

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