The PAICE portfolio, organized by the problem you're trying to solve. Every component is independent, agent-readable, and open or commercially available. Start from the problem, not the product.
Independent scores for how your people, your infrastructure, and your whole organization hold up under AI-assisted work.
Organizations are deploying AI faster than they can observe the human risk introduced into AI-enabled workflows. PAICE measures that risk: behavioral reliability under operational pressure, not what people report.
Who it's for: Regulated organizations where professionals are individually licensed, personally liable, and AI governance isn't optional — GRC, CISO, CAIO, and legal leaders who need evidence of AI behavioral reliability, not training-completion reports.
Visit paice.work →AI agents increasingly mediate how people discover and act on the web, but most sites are invisible or unusable to them. Siteline grades how well a site works for agents across two dimensions (SNAP fundamentals and Agentic Enablement) and shows exactly what to fix.
Who it's for: Site owners, agencies, and engineering teams whose public sites need to be findable, usable, and recommendable by AI agents — not just human visitors.
Visit siteline.to →Organizations adopt AI across people, infrastructure, and regulation but have no single measure of combined readiness. AI Posture produces one governance score, bounded by the weakest vector, so the gap that actually limits you is visible.
Who it's for: Governance, risk, and compliance leaders who need one defensible readiness score across people, infrastructure, and regulation — instead of three disconnected tools that never combine into a posture.
Visit aiposture.org →A connected, machine-readable graph of AI laws, obligations, incidents, and the shared schema underneath them.
AI regulation is changing fast. New laws are introduced, amended, and replaced across dozens of jurisdictions simultaneously. Keeping up is a full-time job -- and most organizations don't have someone dedicated to it.
Who it's for: - **Compliance teams** determining what applies to their AI systems
Visit everyailaw.com →Fact-specific interpretations (no-action letters, private rulings, JIAs, HOA decisions) live in scattered, unstructured records that can't be compared across authorities. PubLedge is an open protocol that records them as hash-pinned markdown bound to a shared ontology.
Who it's for: Regulators, regulated parties, and civic bodies that issue or rely on fact-specific written interpretations and need them recorded in a portable, queryable form.
Visit publedge.org →AI incidents and their legal consequences are scattered across public records with no structured, searchable index. AI Incident Law is an open corpus of public AI-related matters, queryable by humans and agents.
Who it's for: Compliance teams, legal counsel, AI governance leads, and researchers tracking how AI failures turn into legal and regulatory action.
Visit aiincidentlaw.org →Normative content is usually modeled by what it says, not what it requires, so obligations can't be queried consistently across laws, cases, and agreements. Obligation-First is a shared upper schema and JSON-LD context that models normative content by what it requires.
Who it's for: Anyone modeling laws, cases, or agreements for machines — legal-graph builders, compliance-tool developers, and ontologists who need normative content to be queryable across sources.
Visit obligationfirst.org →Open standards and templates for the agent layer: operational limits, provenance, instruction integrity, peer coordination, and structured knowledge.
Services signal limits with status codes (429, 403, 500) that agents can't interpret, so agents retry blindly and the waste compounds. Graceful Boundaries is a specification for communicating operational limits to humans and autonomous agents.
Who it's for: API and service operators, plus the agent builders calling them, who need operational limits expressed in a way autonomous callers can actually act on.
Visit gracefulboundaries.dev →Agent Skills move across local folders, registries, and platform uploads with no portable way to verify version, integrity, or drift. Skill Provenance makes a bundle's identity and integrity travel with it.
Who it's for: Teams that build, distribute, or run Agent Skills across multiple surfaces and need to know a bundle is the version they trust and hasn't silently drifted.
Visit skillprovenance.dev →Multi-agent setups default to a central orchestrator that dictates to subordinate agents, hiding disagreement and decisions. Turnfile is a protocol for peer agents to negotiate and reach auditable consensus with humans on the loop.
Who it's for: Teams building multi-agent systems where LLM agents must coordinate as peers — disagreeing, negotiating, and reaching consensus without a central orchestrator.
Visit turnfile.work →Multi-model tools increasingly surface where models disagree, then the disagreement evaporates into a chat answer; execution logs record what ran, not who dissented or who decided. AIDR captures independent agent positions, preserved dissent, and required human arbitration in one plain-text file that survives tool changes.
Who it's for: Teams making high-consequence decisions with AI agents — architecture calls, release gates, security-relevant changes, public claims — who will later need to reconstruct who objected before acting and who held the authority to resolve it.
Visit aidr.work →Knowledge bases usually live in databases or CMSs that aren't diffable, portable, or agent-readable. Knowledge-as-Code applies software engineering practice (plain text, Git-native, ontology-driven) to produce a searchable HTML site plus JSON API from one source.
Who it's for: Teams that want a knowledge base treated like code — version-controlled, ontology-first, multi-output — without standing up a database or CMS.
Visit knowledge-as-code.com →AI-assisted setup guides are distributed through HTML, rendered Markdown, PDFs,
Who it's for: - AI governance practitioners who need evidence that guidance was reviewable
Visit guidecheck.org →LLM-to-LLM communication defaults to verbose human prose, which wastes tokens and loses precision; Tokenese gives agents a token-native interlingua whose lexicon is admitted only when each symbol survives a reproducible cross-tokenizer audit.
Who it's for: Teams building multi-agent systems who want machine-to-machine messages that are more compressed and more precise than natural language, with every vocabulary symbol verified by a reproducible tokenizer audit rather than asserted.
Visit tokenese.org →