Stop false positives at the source. Turn plain English into production rules.
FisQuant enables fast-growing fintechs and MSBs to write fraud and compliance rules in plain English. The FQ Compiler turns them directly into high performance logic with custom rolling lookbacks, zero engineering delays, and sponsor-ready audit trails.
* Sub-millisecond execution represents internal core engine target design benchmarks.
Decline transaction if 24h velocity exceeds 3x baseline or rolling volume exceeds $3,000 across linked accounts.
Sub-Millisecond Engine*
Deterministic execution on in-flight transaction rails*
Auditable Rule Lineage
Plain English to compiled FQ logic with full version history
Security & Compliance
SOC 2 Type II audit in progress
* Sub-millisecond benchmarks represent internal core engine design targets; actual transit latencies vary based on cloud hosting proximity and clearing rail gateway specifications.
The Interactive “Prompt-to-FQ” Compiler Playground
Write your compliance rules in plain English or select an example below. FisQuant compiles stateful variables and custom rolling lookbacks directly into high performance rules with zero developer tickets.
// FQ Rule Target: rules/velocity_graylist.fq
// Stateful Context: rollingBaseline(lookback: custom_window, metric: "tx_count")
// Target: Hardened FQ Runtime Core (Sub millisecond execution)
tx.event_type == "TRANSFER_CREATED" &&
tx.amount_usd > 3000.00 &&
tx.user.velocity_24h > (state.velocity_baseline * 3.0) &&
fatf.is_graylisted(tx.destination_country)
? rule.escalate("AML_CASE_QUEUE", {
typology: "CROSS_BORDER_VELOCITY",
sla_constraint_ms: 1,
deterministic_proof: "SHA256:8f4b1e9c"
})
: rule.allow()Historical Ledger Backtest*
Evaluated 94,200 txs in 0.14s*
Suppressed 418 false positives* without missing fraud.
The FQ Compiler natively computes custom rolling volumes and velocity curves into deterministic rule definitions over a hardened runtime with zero developer tickets.
* Disclaimer: Simulation metrics, noise suppression percentages, and recall benchmarks are estimates based on synthetic historical transaction ledgers for demonstration purposes. Actual suppression rates vary based on real-world transaction patterns, entity networks, and institution-specific risk appetite.
The 4 Core Architectural Workflows
FisQuant decouples risk operations from engineering sprint cycles. It parses plain English prompts, synthesizes stateful behavioral aggregates such as configurable rolling volumes and multi day velocity curves, and compiles them into a deterministic Abstract Syntax Tree. This delivers sub millisecond execution across real time payment rails with no loops or recursion, so the rule stays outside the model definition.
1. Rule canvas & FQ Compiler
Describe your monitoring logic in natural language. The FQ Compiler synthesizes stateful behavioral aggregates such as configurable rolling volumes and velocity curves, and compiles them down to a hardened FQ runtime, tested against historical data with zero developer tickets.
* Latency targets (<1ms) and runtime throughput represent internal execution design benchmarks on compiled deterministic logic under synthetic testing conditions.
Live ROI and Analyst Hours Reclaimed*
Quantify direct payroll displacement and analyst hours saved by accelerating case reviews with deterministic FQ velocity filters and a unified case management workspace.
Configure Your Compliance Load
Inputs provided by visitor; all calculated values are estimates based on standard fintech operational benchmarks. Includes sandbox setup, historical backtesting, and sponsor bank evidence vault.
Built by Operators & Distributed Systems Specialists
FisQuant pairs proven operational scale with formal computer science rigor, engineered specifically to keep fast-growing fintechs and MSBs compliant without slowing growth.
Angel de Uriarte
Co-Founder & CEO • Operations & Commercial Scale • B.A. UDLAP
Experienced operator with a proven track record scaling high-growth businesses and expanding operations across international markets. Brings extensive leadership across commercial execution, multi-team operations, and product delivery, spearheading go-to-market strategy and customer partnerships at FisQuant.
Cesar Andres Baez
Co-Founder & CTO • Full-Stack Risk Engineering • M.S. UIUC
Distributed systems engineer and Master of Computer Science graduate from the University of Illinois Urbana-Champaign (UIUC). Deep domain experience architecting scalable financial crime infrastructure across transaction monitoring, fraud detection, and sponsor bank audit pipelines. Directly engineers FisQuant's core compiler and deterministic execution engine.
Stop Burning Compliance Payroll on Alert Noise. Deploy FisQuant.
Built for fast-growing fintechs and MSBs eliminating false positives while generating defensible evidence packages for sponsor bank examinations.