Every Argus briefing passes through two independent checks before it reaches Telegram: a deterministic code pass that verifies every number against the day's source data, and a separate AI review of editorial judgment. If either check fails, Argus retries once — and if that still fails, it sends a plain, numbers-verified brief instead of an unverified narrative.
Most AI-written financial content has one failure mode that matters more than any other: a plausible-sounding number that's simply wrong. A briefing can read perfectly and still misquote a price, a percentage move, or an economic forecast. Argus is built around removing that failure mode specifically — not by asking the model to be careful, but by checking its output against source data before anything is sent.
Layer 1 — numbers, checked by code, not by AI. Before any narrative judgment happens, every market-shaped figure in the draft — percentages, price levels, thousands-grouped numbers — is compared against Argus's own fact sheet: the structured data pulled directly from that session's market feeds. If a number in the draft doesn't exist in the fact sheet, it's flagged as a fabrication. This step is deterministic: a number either matches the source data or it doesn't, with no AI judgment involved in the check itself.
Layer 2 — editorial judgment, reviewed by a second AI pass. Once the numbers are already verified, a separate AI review looks at how the story is told: whether the tone matches the scale of the move, whether macro events are described accurately, whether anything reads as overstated. Because the figures are already guaranteed correct at this stage, this pass is judgment-only — it is not re-checking whether numbers are real.
If the numeric check finds a fabricated figure, or the editorial review flags a serious issue, Argus does not publish the draft as-is. It gets exactly one retry: the specific problem is fed back to the model, and a corrected version is generated. If that retry still fails either check, Argus does not fall back to publishing a flawed narrative — it instead sends a plain, code-generated brief built directly from the verified fact sheet: the levels, the moves, the macro events, without any AI-written narrative around them. It reads less like a story and more like a data sheet, but every figure in it is guaranteed to match the source. A human operator is notified whenever this fallback is triggered, so it's never a silent failure.
Anyone can ask an AI model to "double-check its own work" — the model will happily say it did. The difference here is that the number check does not depend on the model grading itself: it's a separate, code-based comparison against source data that either matches or it doesn't. The editorial review is a second, independent pass on top of that, not a substitute for it. That separation — code for facts, AI for narrative judgment, and a safe fallback if either fails — is what keeps a bad number from reaching a subscriber's Telegram.
This mechanism runs on every Argus Market Agent briefing, at every session. It doesn't make Argus infallible — no automated system is — but it means a wrong number isn't a matter of hoping the model got it right.
See it in your own briefings — start free on Telegram →This article describes an internal quality mechanism at a high level and is for informational purposes only. It does not constitute financial, investment, or trading advice. Argus briefings are informational content, not trading signals.