BRK // For Brokers · Prop Firm Operations

The prop firm economy runs on models. Yours should be one of them.

Evaluation-based prop firms are one of the most AI-native businesses in trading. Challenge design, funded-trader lifecycle, payout risk and fraud detection are all model problems. We build the models — for FTMO-shaped, The5%ers-shaped, FundedNext-shaped firms and everyone in between.

BuyerFounder · COO · Head of Risk · Head of Trading← All broker capabilities
The problem

What breaks first in a fast-growing prop firm.

Prop firms scale fast. Challenge economics — the expected value of a rule-set, a marketing source, a specific challenge type — get away from founder intuition around 2,000 concurrent traders. Funded-trader lifecycle, especially post-pass, is a fundamentally different journey the CRM is not built for. Payout fraud — coordinated groups extracting funded capital across accounts, sophisticated latency arbitrage, martingale exploits — is quietly expensive.

Almost every fast-growing prop firm hits the same three walls in the same order: cohort economics visibility, funded-trader retention, payout fraud losses.

What good looks like

What good prop-firm AI looks like.

Three model systems, one operating layer:

  • Live challenge economics: expected value per rule-set, per cohort, per marketing source, per traffic channel — updated daily, drillable by any dimension.
  • Funded-trader lifecycle: a separate journey that recognises the moment a trader passes and inverts the relationship — from acquisition target to relationship account.
  • Payout risk scoring: every payout request scored for coordination, latency exploits, martingale-fingerprints and duplicate-identity signals, with human review only for borderline cases.
What KJ Capital ships

What KJ Capital ships.

A bespoke prop-firm operations layer, integrated with your challenge platform (BrokerTools, YourPropFirm, Trader Evolution or custom), CRM and payment stack. We deliver:

  • A live challenge economics dashboard with per-cohort EV, drillable to the source.
  • A funded-trader journey orchestration layer that recognises pass/fail events and routes accordingly.
  • A payout risk model trained on your historical payouts and dispute cases.
  • A coordinated-account graph (device, IP, timing, symbol, latency) that surfaces likely rings.
  • An operator dashboard that ties it all together for the founder team.
Typical outcomes

What brokers actually see after go-live.

Fraud losses caught pre-payout
70–90%
Funded-trader 6-month retention lift
10–25%
Challenge EV insight latency
T+1 not T+30
Time from kickoff to production
8 weeks
Frequently asked

What buyers ask us about this build.

Q01Do you work with FTMO / The5%ers / FundedNext-style setups?+
Yes. The mechanics differ but the model shapes are the same. We've architected against every major evaluation model.
Q02What challenge platforms do you integrate with?+
BrokerTools, YourPropFirm, Trader Evolution, Match-Trader, cTrader, DXtrade, MT4/MT5 and custom OMS.
Q03Can this help us design new challenge products?+
Directly. The EV model tells you what the rule-set will actually cost or earn, before you launch it — instead of finding out after 5,000 cohort accounts have been sold.
Q04How does the payout fraud detection integrate with our current review flow?+
It ranks and narrates every borderline case for your reviewers. Auto-approve for clearly clean requests, hard-block for clear fraud, human review with pre-assembled evidence for the middle.
Q05Price?+
Productised Build: £120k, 8 weeks. Continues on the AI Operator retainer once live.
Next step

Two weeks. £15k. A written blueprint and a working proof-of-concept on your data.