QuantLogix separates computed market signals from AI-written analysis, attaches freshness and provenance to time-sensitive claims, and publishes the evidence needed to audit performance.
1. Sources and evidence
Research prioritizes primary and attributable sources: issuer filings and releases, SEC disclosures, market-data feeds, fund and ownership filings, and identified reporting. Material factual claims should carry a source, an as-of time, or both.
- Public-market facts distinguish live data from delayed or build-time snapshots.
- Private-market estimates identify the round, mark, or evidence basis when available.
- Unverified claims are labeled, excluded, or presented as uncertainty—not converted into fact.
2. Quantitative outputs vs AI
The five-factor signal and its component scores are computed from market and fundamental inputs. An LLM does not invent the signal. AI systems may retrieve evidence, compare scenarios, explain outputs, and draft analysis, but must preserve the underlying numbers and disclose uncertainty.
QL Intelligence uses GLM-5.2 by default and an adaptive router limited to U.S.-hosted inference routes. Model choice can change with task complexity; the quantitative result remains server-authoritative.
3. Freshness and knowability
Time-sensitive pages expose a publication or modification date. Point-in-time claims use the information that was knowable at that time; later outcomes are recorded separately instead of silently rewriting the original call.
Sitemaps carry content-specific last-modified dates, and changed public URLs are submitted through IndexNow to accelerate discovery.
4. Verification and track record
Performance claims should be inspectable, not promotional shorthand. QuantLogix publishes resolved outcomes and methodology on the Verified Track Record, Engine Accuracy, Receipts, and Sealed Calls surfaces.
Historical results are descriptive, not guarantees. Samples, horizons, unresolved calls, and known limitations belong beside any performance summary.
5. Corrections, conflicts, and reader challenges
When a material factual error is confirmed, QuantLogix corrects the affected public page and updates its modification date. A correction should not erase the original timing of a prediction or signal. Readers can report a suspected error with the page URL, disputed statement, and supporting evidence to support@quantlogix.ai.
QuantLogix does not present paid placement as independent research. Educational research is not personalized investment advice, an offer, or a solicitation. See the full disclaimer and company overview.