MouseCatAgentic AI Infrastructure for Risk Operations
MouseCat enables companies to automate and scale risk investigations. The platform deploys entirely within customer cloud accounts, deeply integrating with the data, vendors, and APIs your team already relies on.
Scale human-quality analysis to every transaction
MouseCat runs human-quality fraud investigations, uncovering high-confidence cases of fraud and false positives flagged by other systems. Companies leverage MouseCat’s investigations to automatically decision or accelerate their analyst reviews.
MouseCat can run on existing queues or in the background, enabling teams to proactively discover fraudulent activity weeks before disputes arrive.
Private, customer-controlled cloud environments
MouseCat deploys entirely within customer-owned cloud environments, enabling companies to retain complete control of their most sensitive data, and for MouseCat to operate over the full enterprise context.
MouseCat’s security posture and data access model enables us to achieve outcomes not possible from any vendor with limited access and data egress requirements.
Agents that improve over time
MouseCat improves over time based on historical case notes, analyst feedback, and ground-truth labels like chargebacks. These labels are used to construct evaluation datasets that MouseCat agents are tested against, enabling iterative improvements and highly optimized performance.
Infrastructure for your entire team
MouseCat’s core components, including prompts, tools, an evaluation suite, and tuning pipelines are deployed within customer environments. Analysts contribute directly to MouseCat through case-level feedback and can even build their own agents using MouseCat’s framework and infrastructure. All MouseCat evidence, reasoning, and outputs are stored and accessible by your team for trend analysis.
Why MouseCat was founded
- 01
Traditional models and rules are falling short
While traditional ML systems excel at learning past fraud trends, they are slow to adapt as fraudsters change their methods. Managing in-house models requires significant investment, and vendor-owned models lack customer-specific context needed to achieve ideal performance.
- 02
Operations teams can't scale headcount 10x
Human investigators excel at discovering and mitigating the patterns models and rules miss but can only review a fraction of cases. Businesses often turn to outsourcing for scale at the expense of quality.
- 03
Productionizing automation efforts is challenging
Companies trying to productionize automated fraud investigation systems internally are facing challenges related to undifferentiated aspects of the problem e.g. evaluation, auto-tuning, versioning, credential management, data integration, etc. And none of the existing vendors help customers solve these gaps.
Frequently Asked Questions
How is MouseCat different from traditional fraud solutions providers?
Many existing vendors now offer copilot agents that operate over the data within their walled garden. While useful, these solutions fail to help enterprises realize the full potential of Agentic AI within their Risk Operations.
MouseCat takes a different approach, designed from day one to deploy within customers' infrastructure stack. Our agents operate over all your company's data, whether internal or sourced from other vendor data providers. We work closely with customers to deploy highly customized agents that improve over time based on your analyst feedback and ground-truth data.
If my team is building our own agents, why would I use MouseCat?
MouseCat operates transparently within customer environments, exposing prompts, tools, skills, evaluation datasets, and tuning pipelines to customers. While MouseCat typically drives the undifferentiated heavy lifting of data integration, evaluation, and tuning, engineers and analysts often contribute directly or build their own agents on top of MouseCat. We also offer Claude Code and Codex integrations that support investigators mining MouseCat case reviews for trends.
Do ML and Data Science teams work with MouseCat?
A fundamental challenge with building ML models for fraud is the significant lag between a transaction and ground-truth labels, resulting in models that adapt slowly when fraud patterns change. ML and DS teams leverage high confidence MouseCat outputs as synthetic labels that improve model performance by capturing emerging trends.
If my organization's fraud losses are within guardrails, why would I invest in MouseCat?
MouseCat agents frequently uncover high-confidence false positives directly impacting top-line revenue. Organizations also achieve significant productivity and efficiency gains by reducing manual, repetitive work and investing in higher-leverage tasks.
Who is the team behind MouseCat?
MouseCat was founded by Nicholas Aldridge and Joseph McAllister.
Nicholas is a core maintainer of the Model Context Protocol (MCP) and a former Principal Engineer at Amazon Web Services, where he launched products like Amazon Bedrock Knowledge Bases and AgentCore. Before MouseCat, Joseph built the ML and Risk infrastructure that powers every risk decision at Coinbase.
MouseCat is backed by Y Combinator and leading investors in the fintech and fraud solutions space. Our angels include executives and founders from Stripe, AWS, Databricks, and Anthropic.