Tenet.

The problem

Between 70% and 90% of retail traders lose money, and for most the gap to consistency is discipline, not strategy. Traders keep written rules but break them under pressure — revenge trades after losses, oversized positions, FOMO entries. A decade of journaling products, from Tradervue to TradeZella, analyzes mistakes after the fact or offers journal-side lockouts a trader can bypass by going straight to the broker.

No tool intervenes at the moment of decision. In the PRD's pain analysis, "no intervention at the moment of decision" is the single dominant pain by a 2× margin — the trader has no accountability partner in the seconds before they place a trade against their own rules.

The solution

Tenet is an AI trading discipline assistant that intervenes *before* a trade is placed, using the trader's own rule book as the standard. Traders set up a structured rulebook once, then run a pre-trade check by speaking or typing their plan — pair, direction, levels, size, reasoning — optionally with a chart screenshot.

Tenet parses the plan, questions vague or incomplete setups, and returns one of three verdicts — GO AHEAD, PROCEED WITH CAUTION, or NO GO — with a per-rule checklist showing what passed and what failed. Crucially, Tenet never blocks the broker. It holds up a mirror; the trader keeps the final decision, and a NO-GO override requires typing "ACCEPT VIOLATION" first.

How it works

Trader speech is transcribed by a speech-to-text layer (Whisper), joined with any attached chart images, and sent to Claude Opus 4.8 via a single Messages API call that handles vision, reasoning, and structured verdict output together. The system prompt runs a two-phase Socratic flow: a completeness check (entry, exit, risk, reasoning) followed by a compliance check against the rulebook, with a decision tree where the lowest verdict wins.

Opus 4.8 was selected after two rounds of evaluation across 120 outputs for its verdict calibration and chart-reading depth; GPT-5.4 is retained behind a provider-abstraction layer as a documented cost-down fallback. The model never fabricates rules or numeric values — it evaluates only against what the trader actually wrote. Per-evaluation cost runs roughly $0.30–$0.50.

Who it's for

Tenet is B2C first, aimed at the serious retail trader with six months to several years of experience across equities, F&O, FX, commodities, or crypto who has identified discipline as their gap. The primary persona is "Alex Carter," a 28–35-year-old prop-funded FX trader navigating the FTMO / MyFundedFX / FundedNext challenge journey — someone who has failed one to three prior challenges and treats trading as a side pursuit they intend to scale full-time.

The user is also the buyer, shortening the sales cycle. A B2B SKU licensing the platform to proprietary trading firms as compliance and trader-development infrastructure is on the 18–24-month roadmap.

Why it matters

The trading journal, analytics, and mentorship niche is projected to grow at roughly 12–15% CAGR, but the more compelling point is penetration: top players serve under 1% of the ~50M actively engaged retail traders globally. Willingness to pay is proven at $50–$300/month, and prop-firm challenge fees alone exceed $1B annually.

Tenet's headline outcome metric is drawdown reduction — fewer days a trader breaches their own stated risk limit — and prop-firm payout success. By enforcing a trader's own rules pre-trade and building a personalized behavioral data moat with every evaluation, it targets the consistency gap that a decade of post-trade tools has left unsolved.

At a glance

Project

Tenet

Built by

Rahul Kandalam

One-liner

Tenet is an AI trading discipline assistant that intervenes before a trader places a trade, using the trader's own rule book as the standard.