How Anteroom works.

Primary sources, always.

Every regulatory provision, every tracked partnership, every example clause in the pattern library, every kill-list move observed in a deal — anchored to a public primary source. SEC 8-K filings, official company press releases and blog posts, partner program pages, regulator press feeds and formal guidance, Federal Register actions, statutory text. Where the source is itself analytical (an academic paper, a scholarly review), it's attributed to its authors with year and citation.

Anteroom does not generate facts. It indexes, classifies, and structures information that exists in primary sources, then makes the structure visible.

The publish threshold.

A partnership, signal, or claim is published only when one of two conditions holds:

• primary_source — sourced directly from an authoritative first-party document (8-K, official press release, official partner program page, regulator press release, statutory text). One such source is sufficient.
• verified — corroborated by at least two independent secondary sources, with no contradicting primary source.

Single-source secondary coverage is marked single_source and held back from feeds. Rumored items rumored are not published unless attached to a verified anchor — and even then only as context. Of 20 tracked partnerships on the site, 20 are confidence primary_source.

The harvester.

A Python signals worker runs on a cron schedule and pulls items from public sources: regulator press feeds (FTC, NIST, FDA, OCC, CFPB, AEPD), regulator HTML news pages (EDPB, ICO, CNIL, Garante, California AG, New York AG, FINRA, EEOC), and practitioner discussion (r/legaltech, r/privacy, r/Lawyertalk). Each item is deduped by source identifier, classified by a small model against a fixed regime taxonomy, scored by materiality (1-3), and where applicable mapped to a specific corpus provision by stable_provision_id. The classifier's model and prompt version are recorded on every classification row so changes are audit-traceable.

Material items appear at /signals filtered to materiality ≥ 2. Sub-material items remain in the database but are not surfaced — the page is a signal, not a firehose.

The corpus.

56 hand-curated regulatory provisions covering the EU AI Act, GDPR, Colorado AI Act, NYC LL 144, NIST AI RMF, ISO 42001, the FTC Act §5, sectoral overlays (FDA SaMD, OCC SR 11-7, FINRA, EEOC), IL BIPA, the ADA, and cross-cutting regimes. Each provision has a primary-source URL, plain-language summary, regulator guidance, enforcement examples, sector overlays, confidence level, safe-harbor and carve-out tracking, and an embedding for similarity search.

Each row is semver-versioned (corpus_version), reviewer-attributed (reviewer + last_reviewed), and carries a stable identifier (stable_provision_id) that survives amendments. When a provision is amended, the prior state is snapshotted in provision_history and the change appears in the corpus changelog. Saved analyses that referenced the amended provision are flagged on their permalinks and, where subscriptions exist, the diff is emailed.

The patterns library.

3analytical patterns covering the structural shapes AI strategic partnerships take — OEM with third-party AI model, compute-backed strategic investment, hyperscaler-exclusive-then-multi-home, with more patterns added as the library matures. Each pattern is grounded in the alliance research literature (Bamford, Gomes-Casseres & Robinson 2004; Kale & Singh 2009; Doz 1996; Dyer & Singh 1998; Reuer & Ariño 2007; Gulati 1998; Hamel 1991; Brandenburger & Nalebuff 1996) and annotated with example clauses, named kill-list moves with mitigations, and scholarly anchors with year and citation tier.

Example clauses in pattern entries are explicitly hypothetical and illustrative — they show structure and what to negotiate. They are not actual deal terms and should not be used verbatim. Practitioners get the architectural read; deal counsel does the drafting.

The partnership tracker.

20 public AI strategic partnerships tracked from primary sources. Each partnership has an architectural read (structure type, current status, key terms visible publicly), a stable identifier that survives restructuring (stable_partnership_id), an event timeline for structural changes, and tagged pattern instances (14 tagged across the library so far) showing which architectural patterns the deal exhibits and which kill-list moves are visible in its public terms.

Partner-pair identity uses an ordered canonical key so the same partnership is the same row regardless of which side announced it first. Self-partnerships are blocked by constraint. When a partnership restructures, the prior version is superseded and the history remains queryable; the public view filters to active rows only.

What Anteroom is not.

• Not legal advice. Anteroom surfaces obligations and structures a thoughtful counsel would consider. It does not advise on any specific situation. Reading it does not create an attorney-client relationship.
• Not generated commentary. No row in the corpus, no tracked partnership, no pattern entry is machine-written and unverified. The harvester classifies and scores public signals; the curated content is hand-written and reviewer-attributed.
• Not opinion without primary-source grounding. Every analytical claim points back to a public document, an academic citation, or a primary regulator statement. Where analysis is the author's, it's structured to be checkable against the cited sources.
• Not vendor recommendations. Anteroom does not endorse, rank, or recommend specific AI vendors, law firms, or commercial products. The partnership tracker describes public deals analytically; it does not characterize parties as good or bad partners.
• Not a substitute for counsel review. The corpus identifies obligations a thoughtful counsel would consider. It does not run an actual compliance assessment for any specific deployment, deal, or organization.

Open about limits.

Anteroom covers public AI strategic partnerships and the regulatory and architectural patterns that surround them. It does not cover (yet, or in some cases ever): private partnerships, individual deal-by-deal compliance assessments, jurisdiction-specific drafting opinions, vendor-side AI vendor diligence from the customer perspective, partnership economics modeling, M&A or fundraise advisory.

The roadmap is to deepen the patterns library, extend the harvester to partnership-specific source feeds (SEC EDGAR 8-K full-text, partner program pages, official press release feeds from a curated set of AI vendors), and add structural-change detection that flags partnership restructurings as they happen. When a feature ships, it's described here.

About the author.

Anteroom is built by Marcus Harjani, a senior in-house lawyer with roughly a decade of strategic-transactions experience covering OEM, distribution, embedded technology, ISV alliances, and channel architecture, plus a software-development background that predates the legal career. The site is a personal research project, not affiliated with any current or prior employer. Views, framings, and analytical positions on this site are the author's alone. Nothing here represents the position of any current or prior employer, client, or partner.

More at /about. Reach me at hello@anteroom.so.

Corrections.

If you find an error — a wrong primary source URL, a partnership characterized inaccurately, a kill-list move misattributed, a scholarly citation wrong, a corpus provision missing a jurisdiction or amendment — email hello@anteroom.so with the URL and what's wrong. Corrections are made on the curated row and the corpus_version bumps; the prior state is preserved in history. Material corrections surface on the changelog.