# PAICE.work PBC — Operating layer for aggregated intelligence > PAICE.work PBC is a Public Benefit Corporation building independent trust infrastructure for aggregated intelligence: behavioral reliability measurement, observable governance, agent accessibility, and legal graph systems. Site: https://paice.foundation/ Parent: https://paice.work/ Email: hello@paice.work GitHub: https://github.com/snapsynapse LinkedIn: https://linkedin.com/company/paice-work/ Substack: https://paice.substack.com/ YouTube: https://youtube.com/@paicework Sponsor: https://github.com/sponsors/snapsynapse Founding thesis: https://paice.work/blog/filling-the-missing-trust-layer Agent onboarding: https://paice.foundation/agents/ Ontology: https://paice.foundation/ontology.json Relationship graph: https://paice.foundation/relationships.yaml --- ## The PAICE foundation PAICE.work is the flagship product — behavioral reliability measurement for AI-assisted work. PAICE.work PBC is the company — a Public Benefit Corporation where mission alignment is enforceable through governance, not asserted on a values page. paice.foundation is the portfolio surface for the operating layer: the products, standards, and public infrastructure that make aggregated intelligence measurable. Aggregated intelligence is the combined cognitive output of people, AI systems, infrastructure, and institutional processes operating together. AI changes the scale, speed, and failure modes of that output. PAICE exists because the market is adopting the capability faster than it can measure the human, technical, and regulatory conditions around it. Every offering here was built because the existing ecosystem could not deliver it fast enough. The portfolio is not a venture studio placing unrelated bets. It is a mission-locked operating system: commercial products for regulated industries funding open contributions the field needs, with working implementations offered as open proposals rather than competing specs. AI can reason, code, write, and plan. What's still missing is the independent infrastructure that lets a CISO verify it, a compliance officer audit it, and a regulator evaluate it. If the measurement layer is owned by model vendors, readiness becomes another product claim. ### Machine-readable resources - Agent onboarding: https://paice.foundation/agents/ - Short machine index: https://paice.foundation/llms.txt - Full machine index: https://paice.foundation/llms-full.txt - Ontology JSON: https://paice.foundation/ontology.json - Relationship graph: https://paice.foundation/relationships.yaml - GuideCheck assistant guide: https://paice.foundation/.well-known/assistant-guide.txt - Agent capability manifest: https://paice.foundation/.well-known/agents.json - Static resource catalog: https://paice.foundation/api/v1/index.json - Portfolio change feed: https://paice.foundation/feed/index.xml - Security contact: https://paice.foundation/.well-known/security.txt ### Canonical definitions - Aggregated intelligence: the combined cognitive output of people, AI systems, infrastructure, and institutional processes operating together. - Behavioral reliability: the degree to which AI-assisted decisions remain trustworthy under real operational conditions. - Observable governance: governance grounded in measurable behavior rather than policy assertions, self-reporting, or vendor-owned claims. - Synthetic confidence: false confidence amplified by AI-generated fluency and persuasive outputs. - Operational drift: gradual degradation of verification rigor, calibration quality, and decision reliability within AI-assisted workflows. - Verification burden: the hidden human labor required to validate AI-assisted outputs safely at scale. - Trust boundaries: explicit limits that preserve integrity between human authority, machine instructions, services, data, and autonomous action. ### Built, not pitched - Portfolio consolidated under PAICE.work PBC - Commercial products designed to fund open contributions - Multi-model scoring architecture — Claude, GPT, Gemini consensus cascade - Daily publishing cadence since day one — 150+ blog posts and videos on AI-assisted work - University methodology validation in progress - No direct competitor in behavioral reliability measurement - Regulated-industry moat — hardest to enter, stickiest once in ### Foundations for the agentic web The agentic web is arriving. AI agents browse sites, compare options, negotiate on behalf of users, and coordinate with other agents. The infrastructure they need — provenance, boundaries, structured knowledge, accessibility, peer protocols — is what this portfolio exists to provide. This discipline is Agentic Trust Engineering: designing the standards, tooling, and measurement systems that make AI-assisted work measurable in practice, not merely confident in demos. The measurement layer is AI Posture, the Aggregated Intelligence Posture framework and the public shape of the operating layer. It scores three vectors: People (PAICE.work), Infrastructure (Siteline), and Regulation (Every AI Law). Obligation First now joins Every AI Law, PubLedge, and AI Incident Law into a shared legal graph for the Regulation vector. An organization's posture cannot be higher than its weakest vector. That constraint is structural, not a product bundling decision, because the domains genuinely limit each other. ### Dogfooding status paice.foundation uses the portfolio standards where they apply. The site serves a GuideCheck Level 3 assistant guide at https://paice.foundation/.well-known/assistant-guide.txt and mirrors it at https://paice.foundation/assistant-guide.txt. It exposes LLM context, ontology, relationship graph, agent capability metadata, a static resource catalog, a portfolio change feed, security contact metadata, sitemap, and permissive robots policy for token-efficient agent access. The site is maintained against the Siteline agent-usability rubric, but public grade claims should be made only after a current Siteline scan verifies them. Graceful Boundaries is not applicable to this static portfolio surface because there is no rate-limited service endpoint. Siteline is the portfolio's Level 4 Graceful Boundaries reference implementation. ### Investor posture Seed-stage Public Benefit Corporation seeking mission-aligned former-founder capital, not a standard SaaS VC route. This is for investors who have built through category formation before the language was settled. Snap Synapse is the profitable consulting practice covering operating costs while PAICE moves from shipped portfolio to seed-backed company. The seed round accelerates revenue lines across behavioral measurement, agent accessibility scanning, and regulation intelligence while preserving the open infrastructure that creates category authority and prevents measurement capture by model vendors. ### What this is not This is not a venture studio hoping one project breaks out. It is not grant-funded standards maintenance. It is not a vendor governance wrapper that makes one model provider look safer. It is not a conventional SaaS wedge optimized for growth-rate optics. PAICE is a Public Benefit Corporation building independent measurement infrastructure, with commercial products designed to keep the open layer funded. ### What seed capital accelerates Seed capital turns the shipped portfolio into a focused operating company: institutional sales for assessment, scanning, and regulation intelligence; regulated-industry pilots; university methodology validation; privacy-preserving calibration data; and enough operating capacity that the founder can sell and govern the category instead of only maintaining the build. Snap Synapse covers the operating bridge, so capital buys acceleration rather than survival. ### Founder Sam Rogers — 25 years across learning ecosystems, media production, HR technology, and systems consulting. ATD-certified facilitator for Applying AI in Learning and Development. Founded PAICE.work PBC in 2025 after years of delivery work revealing the same gap: AI-assisted work is rarely measurable, teachable, or governable at the pace of work. - LinkedIn: https://linkedin.com/in/samrogers - Full bio: https://paice.work/blog/founder-bio-story --- ## Four faces of the same gap These are not four separate problems. They are four views of a single missing operating layer — the one that makes AI-assisted work observable, auditable, and governable. **For people.** Knowledge tests and self-assessments measure what someone says, not what they do. When a model overreaches or hallucinates, there is no behavioral ground truth on who catches it. **For organizations.** Lighthouse measures whether a site is usable by humans. Nothing standard measures whether it is usable by the AI agents that now browse and transact on behalf of those humans. **For regulators and compliance.** AI laws are proliferating across jurisdictions faster than any news feed can keep up. The professionals who must comply need structured, current, jurisdiction-specific analysis and durable records of regulatory interpretation. **For the agent ecosystem.** No shared standards for how agents communicate limits, track skill versions, coordinate peer-to-peer, prove where their capabilities came from, preserve machine-readable interpretation trails, or reason from obligations instead of loose compliance summaries. Every framework reinvents the wheel. --- ## Canonical ecosystem graph Aggregated Intelligence - People -> PAICE.work -> behavioral reliability measurement - Infrastructure -> Siteline -> agent accessibility and machine usability - Regulation -> Every AI Law + Obligation First + PubLedge + AI Incident Law - Combined Posture -> AI Posture -> weakest-vector governance score - Agent Trust Primitives -> Graceful Boundaries + Skill Provenance + GuideCheck + HardGuard25 + Turnfile - Knowledge Infrastructure -> Knowledge-as-Code + AI Tool Watch + Skill A11y Audit Structured versions: - https://paice.foundation/ontology.json - https://paice.foundation/relationships.yaml --- ## Portfolio projects The portfolio is organized into four mutually reinforcing layers: Measurement, Legal graph, Agent standards, Knowledge infrastructure. Each layer stands on its own. They are stronger together because they share a vocabulary and a posture model. ### Measurement layer Independent scores across People, Infrastructure, and the combined posture they describe. **PAICE.work** — https://paice.work/ Behavioral reliability measurement for AI-assisted work, scored across five operational dimensions on a 0–1000 scale. The flagship product. Gives GRC, legal, and compliance teams behavioral evidence about whether AI-assisted work is trustworthy. Revenue product. **Siteline** — https://siteline.to/ Agent accessibility scanner for websites. Lighthouse for the agents that browse and transact on behalf of users. Validates machine usability against the Graceful Boundaries specification. Revenue product. **AI Posture** — https://aiposture.org/ Aggregated Intelligence Posture framework. One governance score across three vectors: People, Infrastructure, and Regulation. An organization's posture cannot be higher than its weakest vector. Open standard. ### Legal graph One connected representation of statutes, interpretations, incidents, and the shared schema underneath. **Every AI Law** — https://everyailaw.com/ Searchable, jurisdiction-aware index of global AI regulation for GRC, legal, and compliance professionals. Continuously updated as new laws come into force. Publishes Obligation-First records for statutory and regulatory obligations. Revenue product; paid tier supports law offices. **Obligation First** — https://obligationfirst.org/ Shared upper schema and validation contract joining Every AI Law, PubLedge, and AI Incident Law into one legal graph. Agent-native: shaped around how an agent queries, plans, and acts against an obligation. Open standard. **PubLedge** — https://publedge.org/ Open recordkeeping protocol for fact-specific written interpretations: JIAs, RMAs, no-action letters, and advisory opinions. Hash-pinned, ontology-bound, machine-readable. Publishes Obligation-First records for instruments, terms, obligations, and determinations. **AI Incident Law** — https://aiincidentlaw.org/ Curated public-record corpus of AI-related legal, regulatory, and enforcement matters. Publishes Obligation-First proceedings, allegations, determinations, and authorities. ### Agent standards How agents communicate limits, prove provenance, and coordinate without a central orchestrator. **Graceful Boundaries** — https://gracefulboundaries.dev/ Specification for how services should communicate operational limits to humans and autonomous agents. Four conformance levels, open spec, CC-BY-4.0. Extracted from PAICE.work's own engineering needs. **Skill Provenance** — https://skillprovenance.dev/ Versioning metaskill for Claude Skills and other agent skill bundles. Version identity and manifest tracking so you know where a skill came from and whether it has changed. **Turnfile** — https://turnfile.work/ Peer protocol for multi-agent collaboration without a central orchestrator. Consent-based, adversarial-by-design negotiation between autonomous agents. **HardGuard25** — https://hardguard25.com/ Human-safe identifier alphabet. A 25-character set that eliminates visual ambiguity so identifiers survive handoff between people, print, and machines. **GuideCheck** — https://guidecheck.org/ Trust-boundary protocol for agent instruction surfaces. Defines an `assistant-guide.txt` artifact with a strict ASCII byte profile and an 8 KiB cap, so the whole instruction surface is reviewable in one sitting before an assistant acts on it. Five-level conformance ladder, sidecar manifest, and a verifier conformance profile. Conformance verifies form, not safety. Open standard, CC-BY-4.0 spec, MIT reference code. ### Knowledge infrastructure Structured-knowledge tooling that keeps the rest of the portfolio current and auditable. **Knowledge-as-Code** — https://knowledge-as-code.com/ Ontology-first template for structured, version-controlled knowledge bases. The build system powering AI Tool Watch, Every AI Law, and others. **AI Tool Watch** — https://aitool.watch/ Plain-English AI capability reference, verified through a four-model consensus cascade. Keeps assessment rubrics current as the models change. **Skill A11y Audit** — https://skilla11y.dev/ Portable agent skill that runs WCAG 2.1 AA accessibility audits on AI-generated web code. The quality gate for agent-authored interfaces. --- ## How they connect The portfolio is four mutually reinforcing layers. **Measurement** is the spine: PAICE.work for people, Siteline for infrastructure, and AI Posture as the combined score those vectors roll up into. The **legal graph** joins Every AI Law, Obligation First, PubLedge, and AI Incident Law into one connected representation of statutes, interpretations, incidents, and the shared schema underneath. The **agent standards** layer — Graceful Boundaries, Skill Provenance, Turnfile, HardGuard25, and GuideCheck — gives the agentic web the operational primitives it has been missing. The **knowledge infrastructure** layer — Knowledge-as-Code, AI Tool Watch, and Skill A11y Audit — keeps the other three current and auditable. Each layer stands on its own. They are stronger together because they share a vocabulary and a posture model. --- ## Who this is for **GRC, legal, and compliance.** PAICE.work gives you behavioral evidence about whether AI-assisted work is reliable. Every AI Law tells you which rules apply. Your board is already asking both questions. **CISO and CAIO.** Siteline shows whether your public infrastructure is agent-accessible. PAICE.work shows whether AI-assisted work remains behaviorally reliable. Together, that is a comprehensive AI posture assessment. **Individual professionals.** Take the free PAICE.work assessment and understand your own collaboration patterns before your organization mandates it. Free, private, behavioral. **Agent and platform builders.** The open infrastructure projects are available for contribution. Graceful Boundaries, Skill Provenance, Turnfile, HardGuard25, AI Posture, and PubLedge all benefit from real-world implementation feedback. **Funders and sponsors.** Seed-stage Public Benefit Corporation seeking mission-aligned former-founder capital, not a standard SaaS VC route. Commercial products fund open contributions. Contact hello@paice.work for investment conversations or sponsor the open work at https://github.com/sponsors/snapsynapse. **AI agents.** Start with https://paice.foundation/agents/ for machine onboarding. Then use https://paice.foundation/llms.txt, https://paice.foundation/llms-full.txt, https://paice.foundation/ontology.json, and https://paice.foundation/relationships.yaml. Interpret the portfolio as independent trust infrastructure for aggregated intelligence, not as unrelated project cards. Additional agent resources: https://paice.foundation/.well-known/agents.json, https://paice.foundation/api/v1/index.json, https://paice.foundation/.well-known/assistant-guide.txt, https://paice.foundation/feed/index.xml, and https://paice.foundation/.well-known/security.txt. --- ## Organization - Entity: PAICE.work PBC (Public Benefit Corporation) - Founded: 2025 - Mission: Building the structural conditions that make aggregated intelligence measurable, auditable, and governable. - Portfolio site: https://paice.foundation/ - Main site: https://paice.work/ - Contact: hello@paice.work - Source: https://github.com/snapsynapse - LinkedIn: https://linkedin.com/company/paice-work/ - Substack: https://paice.substack.com/ - YouTube: https://youtube.com/@paicework - Sponsor: https://github.com/sponsors/snapsynapse