Savvi AI helps banks, credit unions and Financial Services companies drive growth, reduce risk, and improve operating performance by deploying governed AI directly into the workflows and systems they already use.
Savvi supports predictive models, AI applications, agents, and intelligent decisioning across areas such as deposits, lending, fraud, customer or member growth, compliance, and operations.
Unlike generic AI tools, Savvi is built for regulated financial services and gives organizations the controls, audit trails, explainability, infrastructure, and expertise to operate AI in production while ensuring that the institution retains ownership of the intelligence they create.
Savvi is used to improve business outcomes across growth, lending, risk, and operations.
Common use cases include:
Savvi focuses on use cases where better intelligence can materially affect revenue, risk, or operating performance.
Savvi can capture the outcomes of predictions, recommendations, and decisions and use that feedback to train models and improve performance.
For example, a deposit-retention model can learn not only which relationships were likely to leave, but which retention actions actually worked.
That feedback loop allows intelligence to improve based on real-world results.
Yes.
Different use cases require different forms of intelligence.
Some are best solved with predictive machine learning, others with large language models, specialized small models, AI agents, or combinations of these technologies.
Savvi allows organizations to use the right model for each problem while maintaining a consistent layer for governance, deployment, monitoring, and control.
Savvi starts with the business outcome.
Depending on the use case, that may include:
AI performance is tied to those business metrics so organizations can determine whether the solution is creating measurable value.
Yes. Use Savvi to build and deploy custom AI Apps and AI Agents with your proprietary data while retaining complete control and IP ownership.
Data scientists and analysts can run multiple modeling experiments in parallel, with integrated monitoring and rollback for fast iteration.
Savvi is designed to run as a single tenant solution in the cloud, ensuring regulatory and SOC 2 Type 2 compliant data, API, and model hosting and isolation. If desired, the Savvi AI Client Containers can be wrapped within your existing VPC or even deployed into your existing cloud-hosted VPC or on-prem solution.
Savvi’s platform includes continuous-learning pipelines that automatically feed real-world outcomes back into each model. Performance metrics are tracked in real-time, and decision thresholds update automatically to handle new patterns—such as fraud tactics, payment-flow changes, and shifting customer behavior—without the need for a large in-house data science team. You get models that adapt as your business and the market evolve, with zero extra DevOps or manual retraining.
Savvi supports auto-scaling in its client container architecture. It's designed for fast real-time decisioning (<40ms response times for real-time decisions in many cases).
Savvi supports over 400+ prebuilt data connections to popular data systems. Also supports CSV/Excel uploads, REST APIs, and JS tag integrations. It has built-in flexibility, whether you're bringing clean structured data or less tidy/unstructured sources.
Savvi provides transparency into any model-driven prediction, classification, or decision (why did the model make a particular decision?), audit trails of decisions, the ability to inspect inputs (features) used, and set business guardrails so machine learning outputs stay within acceptable risk bounds.
Savvi AI is a single tenant solution. All data, models, and APIs are hosted within a secure and isolated client containers. The entire platform meets stringent security requirements, including SOC 2 Type 2, and is frequently pen tested to ensure we meet the highest security standards.
Savvi uses isolated, single-tenant client containers. Your data, models, and APIs are segregated and secure. Data is encrypted at rest (using AWS KMS) and served via HTTPS with secure certificates (2048-bit RSA, SHA256).
Yes, Savvi is SOC 2 Type II certified. Regular penetration testing is part of their security practice.
Savvi AI has multi-tiered risk management built into the platform, from uptime and error monitoring with tools like NewRelic, to client-specific AI Model performance monitoring with the Savvi Health Check system, which provides continuous operational management of AI model performance to catch problems like data drift or model error monitoring.
Each client will have their own set API Key, which are unique to those Savvi Client Containers. Access is controlled—keys are not resettable by outside parties, and must be used via secure channels. Session management uses modern cryptographic verification (JWT tokens).
Savvi supports hosting options with client containers; you can use your own VPC or an on-prem option. Data is stored encrypted and in isolated environments.
Savvi is built explicitly for regulated industries. Its architecture, data handling, auditability, and guardrails are designed to meet regulatory requirements (e.g., financial regulators, privacy, security). SOC 2 Type II certification, isolated client containers, and transparency in model behavior—all help meet compliance obligations. Savvi AI also provides detailed modeling reports that can be used to ensure and meet model governance requirements.