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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
Category

AI and Inference

10 articles
  • AI routing and the linguistic air-lock

    AI routing holds every external-model credential and audit-logs every request at a single boundary. It does not scrub PII from prompts, and Tier C external routing is not live yet.

  • Decode-time constraints

    The constrained-decoding technique, and a clear line between it and what PointSav has built today: an advisory post-generation linter, with the grammar-based mechanism itself planned, not shipped.

  • Doorman protocol

    The Doorman is the sole AI request boundary through which every inference call routes, holding every external-model credential and logging every call to an immutable audit ledger.

  • Elastic Compute #1 nightly LoRA training pipeline

    Nightly two-phase pipeline on Elastic Compute #1 that rebuilds the deployment DataGraph and trains LoRA adapter weights for the workspace language model.

  • Knowledge flow: training loop and ontological DataGraph

    Quality framework for the Totebox knowledge flow, asking whether LoRA adapters measurably improve the model and whether the DataGraph is an accurate ontology.

  • Learning Datagraph — SLM trajectory loop and apprenticeship queue

    Training loop turning operator interactions into training signal — trajectory capture, an apprenticeship queue, and a GLiNER→OLMo distillation pipeline that generates entity-extraction DPO pairs.

  • PointSav-LLM

    The planned vendor-tier specialist AI model for substrate-sovereign SMBs — Tier 3 of the Four-Tier SLM Substrate Ladder, built by continued pretraining of the OLMo 3 32B base model.

  • SLM Rust stack architecture

    The full Rust dependency graph and binary architecture for service-slm, the Doorman service that mediates every inference call in the PointSav platform.

  • Tiered entity extraction architecture

    The entity extraction pipeline runs three tiers per document: Tier 0 fast extractive detection via GLiNER, Tier A generative fallback via OLMo, Tier B GPU enrichment.

  • Zero-container inference

    Tier B GPU deployment pattern using native Linux binaries under systemd on an L4 GPU, with idle detection run from the Doorman server process rather than a timer on the GPU VM itself.

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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