Built for legal, compliance, regulatory, and healthcare work. Noesis cross-checks four large language models on every answer, audits the result for fabrication and bias, grounds every claim in evidence, and produces a record you could hand to a regulator. One chat surface, eleven verifiable signals on every reply.
A chat interface backed by a multi-model reasoning pipeline. Every answer passes through a seven-layer verifier stack, grouped into three families — trust, evidence, safety.
Claude, ChatGPT, Gemini, and Kimi run in parallel on each turn. Their replies cluster into convergent claims (all agree), contradictions (they disagree), and minority views (one model alone). You see what was unanimous and what wasn’t.
A separate model audits the final answer against four dimensions — active analysis, rational evaluation, objectivity, self-regulation — and flags fabrication when claims aren’t grounded in the conversation or uploaded documents.
Citations are verified against CrossRef and PubMed (academic), CourtListener and EUR-Lex (case law and EU statutes), SEC EDGAR (filings), openFDA and DailyMed (drugs), ClinicalTrials.gov (trials), and an authoritative-domain list. Unverified claims are marked inline. Uploaded PDFs and Word files anchor the answer — claims trace back to the page.
Behind each layer is a specific algorithm with a citation. The chat surface stays simple; the machinery underneath is documented and independently auditable. Noesis also runs a declarative Constitution of 11 principles that the system checks itself against on every answer.
Generative AI is now usable for most tasks. It is not yet trustworthy for the high-stakes ones — legal due diligence, compliance audits, regulatory filings, medical literature review — anywhere being confidently wrong has a price.
A single language model produces a confident answer whether or not the underlying claim is true. There is no second opinion. There is no audit trail. There is no place a regulator, a board, or an opposing counsel can look to see why this answer and not another.
Below each reply, Noesis surfaces a row of signals — small colored chips. Click any chip for the underlying evidence. Live in the chat today:
Shows how many of the four models agreed on the final claim. A unanimous chip reads green; a 2/4 split reads amber and the minority view is one click away.
A separate model scores the answer on four reasoning dimensions, with context awareness so “the document says X” isn’t flagged as fabrication when the document actually said X.
Every DOI, PubMed ID, court case, EU statute, SEC filing, FDA label, clinical-trial ID, and authoritative URL is checked against the actual registry — CrossRef, PubMed, CourtListener, EUR-Lex, SEC EDGAR, openFDA, DailyMed, ClinicalTrials.gov. Verified citations become live links; unverified are flagged inline.
The four per-model drafts are persisted alongside the consensus answer. When you ask “why did Noesis say this?”, you can read what each model said before they merged.
Uploaded images are checked for C2PA Content Credentials — a tamper-proof stamp cameras and editors embed to record who created the image and how. If the stamp is present, Noesis reads it. Every image is also run through an AI-generation-likelihood probe with graded evidence and known-limitations disclosure — no binary verdicts.
Every chat turn writes a tamper-evident audit record (EU AI Act §12) — hashed identifiers, the audit signals above, model versions, cost, duration. Users can access, correct, or delete their data with one click (GDPR §15 / 17 / 20). Automated-decision opt-out honored per-turn (CCPA ADMT). 7-year default retention. Chain-verify and export tools shipped.
Two things Noesis does that a language model can’t.
Every language model forgets you between conversations. Noesis doesn’t. Verified uploads become long-term knowledge in your tenant brain — the next question about the same document doesn’t need a re-upload. Every claim promoted to the brain passes a Praetor + Haiku quality gate. When you don’t want a specific turn to teach the brain, one toggle in the composer footer switches it off. The audit chain still records the turn either way.
Paste any AI answer or draft. Noesis runs the same verifier stack that runs inline on every chat turn — per-claim citation checks against CrossRef, PubMed, CourtListener; the critical-thinking self-audit; a hallucination-risk headline. Ships as a standalone drop-zone at chat.noesisCTI.com/verify for anyone who wants a second opinion without moving into a full chat session.
SaaS is the default. Enterprise Bubble is a sovereign deployment mode where no data leaves your perimeter. Single-LLM Trust Mode runs the full verifier stack against your BYO LLM — no Anthropic, OpenAI, Google, or Kimi API calls. Calibrated probability with live market data (Superforecaster, F3 pipeline) is currently admin-only and moving toward the enterprise tier. Contact for details →
Noesis focuses on four kinds of work where a confident-but-wrong answer has a price — legal, compliance, regulatory, and healthcare. Each has its own primary sources, its own audit requirements, and its own line beyond which a licensed professional must remain in the loop.
Case citations, statute references, and court records are verified against CourtListener (U.S. federal & state opinions) and EUR-Lex (EU treaties, directives, regulations, court judgments). Multi-jurisdiction awareness on cross-border matters. Noesis explains the law — it never tells you whether to file, settle, or plead. A licensed lawyer decides.
Every chat turn writes a tamper-evident audit record (EU AI Act §12): hashed identifiers, audit signals, model versions, cost, duration, timestamp. Users can access, correct, or delete their data with one click (GDPR §15 / 17 / 20). Automated-decision opt-out honored per-turn (CCPA ADMT). 7-year default retention. Chain-verify and forensic-export tools shipped for SOC 2, ISO 27001, and HITRUST reviews.
Regulatory answers ground against primary agency records directly: openFDA, SEC EDGAR, EUR-Lex, ClinicalTrials.gov. High-stakes claims must match at least one primary source before Noesis surfaces them. Coverage extending to EMA, Health Canada, MHRA, NICE, CDC, NLM, TGA, WHO, PMDA, NMPA, ANVISA, MFDS, and 14 more national regulators is on the roadmap.
Drug approvals, indications, adverse events, recalls, and clinical trials trace back to openFDA, DailyMed, and ClinicalTrials.gov. Designed to preserve HCP clinical judgment: every answer exposes sources, per-model drafts, and its critical-thinking audit. Noesis’s internal SaMD non-classification assessment against 21st Century Cures Act §3060 (including the independent-review criterion) concludes that Noesis falls outside §3060’s device classification; formal outside legal counsel opinion pending Series A. No PHI in the default SaaS; PHI workflows deploy in Enterprise Bubble mode under a customer-executed BAA.
Noesis is not a licensed law firm, an FDA-cleared medical device, a registered investment adviser, or a certifying body. It is a decision-support tool for licensed professionals across all four verticals. Formal legal opinions on each classification will be commissioned at Series A or before the first regulated customer in each vertical.
The single most important thing a high-stakes AI must know is when to refuse the question.
On four classes of question — legal verdicts, medical diagnoses, financial advice for an individual, and mental-health crisis — Noesis switches into “informational” mode. It explains the underlying law, the relevant clinical evidence, the standard of care, and recommends qualified counsel. It does not render the verdict.
Noesis is a PlatformAI product.
Leads strategy, distribution, and the regulated-industry partnerships that anchor the product.
Architecture, the reasoning pipeline, the audit stack, and the brain-inspired learning substrate.
Noesis is in private demo, hosted in Falkenstein, Germany. Access is gated while infrastructure, reliability, and deployment controls are finalized. Chat and Verify are live and actively used by the team.
Open the chat and ask Noesis something you’d normally double-check. The audit row will show you exactly what the system did and didn’t verify.