Quantifind Tech Stack

AI-powered risk intelligence for financial crime and compliance

Software Development Palo Alto, California 51–200 employees Founded 2009 Privately Held

Quantifind operates a SaaS platform for detecting financial crime—money laundering, fraud, sanctions violations—using machine learning on unstructured data. The tech stack is data-engineering heavy: Spark, Hadoop, Airflow, Python, Scala, with PostgreSQL backing a compliance-grade backend. Notably absent are any adopting/replacing signals, suggesting stable architectural choices. Hiring accelerates across engineering (7 open roles) and data (2), indicating expansion of feature delivery and signal-generation capacity rather than platform overhaul.

Tech Stack 28 technologies

Core StackSalesforce Palo Alto Networks Kubernetes Docker Scala Python Apache Spark Hadoop Jira Confluence WordPress JavaScript SWIFT PostgreSQL Java Go Maven Jenkins Apache Airflow Zendesk React Redux Greenhouse Dell Bash Ubuntu LinkedIn Recruiter Webflow

What Quantifind Is Building

◆Challenges

Legacy technologies demand more human resources
Compliance processes burdened by regulatory responsibilities
Detecting financial crime
Expectation of frictionless transactions
Increasing regulatory responsibilities
Scaling website performance
Improving search visibility
Optimizing conversion
Sanctions screening false positives
Need for faster transactions

▲Active Projects

Apache spark pipeline for risk signal generation
Secure network topologies for saas
Network as code for cloud and data center
Standalone services in scala and python using spark
Build complex machine learning solutions
Proofs of concepts for graphyte platform
Product launches and solution rollouts
Multi-touch email campaigns
Search & ai discoverability
Implement structured data

Hiring Activity

Accelerating15 roles · 10 in 30d

Department

Engineering

Data

Marketing

Sales

Executive

Support

Seniority

Senior

Junior

Staff

Manager

Mid

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About Quantifind

Quantifind helps banks and government agencies identify financial crime and risk networks using an AI platform that analyzes both internal institutional data and public sources. The product targets Know Your Customer (KYC), Anti-Money Laundering (AML), and fraud risk workflows—domains where legacy systems historically demand manual human review as transaction volumes and regulatory scope grow. Founded in 2009 and based in Palo Alto, the company operates as a privately held SaaS vendor with ~100 employees. Core pain points they address include regulatory burden, false-positive screening alerts, and the friction between transaction speed and compliance rigor.

HeadquartersPalo Alto, California

Company Size51–200 employees

Founded2009

Hiring MarketsUnited States

Frequently Asked Questions

What tech stack does Quantifind use?

Python, Scala, Apache Spark, Hadoop, and Apache Airflow for data pipelines; PostgreSQL for persistence; Kubernetes and Docker for deployment; Salesforce, Jira, Confluence, and Zendesk for internal ops.

What is Quantifind working on?

Apache Spark pipelines for risk signal generation, secure SaaS network topologies, machine learning model development, multi-touch email campaigns, and a search/AI discoverability initiative called the Graphyte platform.

How this profile is built

Quantifind's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.