PLMMarket was created to put people back at the center of AI. Large Language Models are powerful—but answers without authorship can lack accountability and authority. Professional Language Models (PLMs) fix that. Each PLM is a named expert’s structured knowledge—verified, scoped, and continually updated—so every answer has a provenance and a responsible steward.
In short: we don’t just return answers. We return answerability.
We felt the gap: AI could answer fast, but couldn’t point to who stood behind the answer. The world needed a way to connect expert identity and real-world experience to AI’s speed and scale. PLMMarket is that bridge. Our platform helps experts capture their repeatable know‑how as PLMs, so organizations and learners get answers that are grounded, auditable, and owned.
Every PLM is attributed to a creator or organization. Clear scope, clear disclaimers, clear stewardship.
Answers are constrained to the expert’s vetted content. When context is thin, PLMs say so.
Show headings, sources, and update cadence. Treat knowledge like software: versioned and reviewable.
Public, private, or hybrid access—on PLMMarket or embedded across the web.
Our north star is a responsible form of “Collaborative Super‑Intelligence”—where multiple vetted PLMs in a domain collaborate to deliver a higher‑quality, multi‑perspective answer. Instead of a single black‑box model, you get a transparent ensemble of named experts, each contributing context, citations, and guardrails. Inclusion is earned through quality, update cadence, and community trust—so the crowd is wise, not noisy.
Think of it as a panel of specialists, on demand: scoped, scored, and auditable.
Knowledge shouldn’t live only on a profile page. With our lightweight embed, PLMs can be placed on blogs, help centers, product pages, LMS portals—anywhere an iframe fits. You choose the access mode (open, tokenized, or private), and we handle security, rate limits, and theming.
The Embedded PLM for Bradley Jones Founder of PLMMarket - AMA
When key people are busy—or move on—your expertise shouldn’t walk out the door. Company PLMs preserve tribal knowledge as living, queryable assistants. Use domain‑restricted access, role‑based controls, and private/hybrid modes to keep the right insights with the right teams.
Universities can run an isolated PLM network for faculty, labs, and departments—governed by campus policies—while selectively exposing public PLMs that showcase current research, programs, and events. It’s a modern knowledge commons: private where needed, public when useful.
Traditional papers are static; PLMs are living publications. Authors publish methods, datasets, and commentary as a queryable model that the community can test with real questions. Versioning captures the scholarly record; citations and critique become structured feedback loops. Peer review doesn’t disappear—it becomes continuous and interactive.
PLMMarket lets individuals and organizations monetize their expertise through per‑query access and subscriptions. This creates incentives to publish granular, high‑signal knowledge—the kind of detail broad web corpora often miss. As PLMs proliferate, AI systems have better, more attributable sources to reason from. Everyone wins: creators, learners, and the models themselves.
Simple rules we refuse to break—so trust can scale with the tech.
Named owners, clear scope, and the right to say “I don’t know yet.”
Context windows, citations, and version history are first‑class features.
Role‑based access, domain restriction, and policy controls for creators and orgs.
Create your first PLM, embed it where people need answers, and help us build a more accountable, authoritative AI ecosystem.
