FRB Agent – AI Multi‑Chain MEV & Front‑Running Trading

FRBis an AI‑powered, high‑speed trading agent that scans Ethereum, BNB Chain and Polygon mempools in real‑time, capturing maximum extractable value (MEV) opportunities and executing profitable trades in milliseconds.

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How to use these case studies

Each card shows a representative scenario—some simulated, some anonymized real-world deployments. Treat them as blueprints for your own internal memos. Copy the structure, swap in your data, and attach supporting links from /metrics,/telemetry, and /support so executives can audit your claims.

  • Highlight the problem, strategy, chain, and measurable outcome.
  • Document guardrails (refunds, risk limits, node hygiene) next to the result.
  • Link to Case Study Builder to replicate the format.

When you publish an internal showcase, note whether data is simulated or live, who approved it, and where the raw metrics live. Transparency keeps procurement and compliance aligned.

DEX Arbitrage on Polygon

DEX Arbitrage on Polygon

+2.1% net ROI (simulated)

Simulated route across two DEXs showing timing and slippage guards.

Private Backrun on Ethereum

Private Backrun on Ethereum

Low-failure path via private bundle

Illustrates backrun intent execution with bundle submission.

BNB Sandwich Risk Guard

BNB Sandwich Risk Guard

Auto-cancel on adverse price impact

Demonstrates guardrails preventing harmful fills under pressure.

Write your own showcase entry

Borrow the structure of these cards: headline, KPI, short narrative. Include chain, strategy, guardrails, and measured impact. Link to internal dashboards so readers can dig deeper. When you publish internally, attach the case study PDF from Case Study Builder and tag the SMEs who helped.

Outline

  1. Challenge: What problem the desk faced.
  2. Approach: Which FRB features and chains were used.
  3. Outcome: Quantified results (inclusion, refunds, time saved).
  4. Safety: Guardrails, logging, compliance actions.
  5. Next steps: Where the team will expand next.

Publish updates quarterly so marketing, sales, and compliance always have the latest stats. When appropriate, sanitize and reuse on the public site to showcase credibility.

Sample narrative

Challenge: Backrun desk needed to prove refunds stayed under 5% while scaling Base exposure.
Approach: Used FRB Pro on Base, Ops Pulse alerts, /metrics/bnb for context.
Outcome: 78% inclusion over 72 hours, refunds 3.2%, operators logged latency benchmarks.
Safety: Refund guard set to 3/50, public PGAs locked to canary mode, runbook stored in Knowledge Base.

FAQ

Are these numbers audited? Many case studies are simulated or anonymized. Always specify context and point readers to raw telemetry for verification.

Can we embed our own charts? Yes—link or embed images exported from your dashboards as long as you keep sensitive data redacted.

How often should we refresh the showcase? Aim for quarterly. Rolling updates keep your sales/ops teams aligned and give compliance up-to-date references.

Next steps

Keep readers moving through the FRB journey

High bounce rates drop when every page ends with clear actions. Use these quick links to send visitors deeper into the product.

CTA

Install FRB agent

Download the signed Windows build and verify SHA‑256.

CTA

Read Docs Quick Start

Share the 15-minute setup flow with ops and compliance.

CTA

Launch /app dashboard

Pair a node client and monitor Ops Pulse live.

Most-used playbooks

Telemetry & trust anchors