Every AI writing tool has a hallucination problem. They cite sources that don't exist, invent statistics, and produce confident-sounding claims that fall apart under five minutes of fact-checking. White Paper System solves this with hard evidence enforcement — not prompts, not guardrails. Rules agents cannot break.
Every unsupported claim is tagged [DATA NEEDED]. You fill it with real evidence. Nothing fabricated gets published under your name.
A fabricated statistic in a published white paper is not a minor editing issue. It damages credibility, can trigger legal liability, and is visible to every reader who fact-checks. Most AI writers have no mechanism to prevent it.
They fill the gap. They generate a plausible-sounding statistic, attribute it to a real-sounding source, and move on. You find out when a reader emails you. Or you don't.
The Research-Analyst tags the gap: [DATA NEEDED — specific claim about X]. You see every gap before the paper moves to drafting. You fill them with real sources or remove the claim.
The Research-Analyst ranks every source before building the evidence plan. Higher-tier evidence is prioritized. Lower-tier evidence is flagged for strengthening.
Blogs, anecdotal evidence, and unattributed claims are rejected from the evidence plan entirely.
Without a locked argument, AI writers drift. Section 2 contradicts Section 5. The conclusion oversells what the evidence supports. The CTA misaligns with the problem statement. This is structural, not a prompting problem.
Core argument, target audience, word count target, and CTA are locked in Phase 1. No writing agent touches the draft until Phase 2 locks the section structure and evidence sources.
Specify claims that must appear in the paper (key differentiators, regulatory context) and claims that must not appear (competitor comparisons, unsupported ROI figures). Agents check the lock before every output.
Every agent reads the lock before executing. If a section drifts from the locked argument, the Dev-Editor flags it. The paper finishes coherent — not a collection of loosely related paragraphs.
Start with one paper. No subscription required.
About AI white paper writing and evidence enforcement.
The Research-Analyst agent is explicitly instructed never to fabricate statistics or cite unverifiable sources. Any claim without a source is tagged [DATA NEEDED] in the draft. The Argument Lock specifies required and forbidden claims before writing begins, so no agent can introduce unsupported assertions.
Evidence is ranked: Peer-reviewed research > analyst reports > government data > named case studies > industry reports > news > trade publications > blogs > anecdotal. Higher-tier evidence is prioritized. Lower-tier evidence is flagged so you can seek stronger sources before publication.
A per-paper configuration that locks the core argument, target audience, CTA, required claims, and forbidden claims before writing begins. Every agent checks the lock — if a claim is forbidden, it doesn't get written. Prevents scope drift and contradictions across sections.
Yes. Each project has a research folder for PDFs, reports, interview transcripts, and data files. The Research-Analyst mines these when building the evidence plan and filling data gaps. Your proprietary research becomes the primary evidence base.
Yes. Upload past white papers and the system extracts a style fingerprint: tone, reading level, citation style, header formatting, sentence length, preferred vocabulary, and forbidden terms. Every agent uses it.
Claude Opus 4.6 by Anthropic for all writing, editing, and analysis — Anthropic's most capable model for long-form reasoning and sustained argument construction.
One complete white paper through the full 12-step pipeline for $15. See exactly what evidence enforcement looks like in practice before committing to a subscription.
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