Savvi offers a fully managed, single‑tenant AI platform tailored for banks, credit unions, payment processors, and fintech firms. It ingests data from any source, lets users build and own AI apps and autonomous agents on proprietary data, and provides secure, SOC 2‑Type II‑compliant deployment with sub‑50 ms real‑time inference that integrates directly into tools like Snowflake, Power BI, Excel, or APIs.
Funding
$5.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Financial institutions and fintech companies often lack the resources, infrastructure, and regulatory‑ready tools needed to develop, deploy, and maintain custom AI models, leading to high costs, long time‑to‑value, and compliance risk.
Solution
Savvi provides a fully managed, single‑tenant AI platform built specifically for the fintech and financial services sector. It ingests data from any source, offers hundreds of pre‑built connectors, and lets users create AI Apps and autonomous AI Agents trained on their proprietary data. The platform handles model training, continuous learning, scaling, and real‑time inference (sub‑50 ms) while delivering built‑in SOC 2 Type II compliance, encryption, and granular access controls. Users retain full ownership of models and IP, and can embed AI outputs directly into existing tools such as Snowflake, Power BI, Excel, APIs, or JavaScript, eliminating the need for separate infrastructure or large AI teams.
Target Audience
Primary customers are banks, credit unions, payment processors, and fintech firms that need secure, compliant AI solutions integrated into their existing workflows.
Features
- Single‑tenant, isolated client containers with end‑to‑end encryption (AWS KMS) and SOC 2 Type II certification for regulated environments
- 400+ pre‑built data connectors plus support for CSV, Excel, REST APIs, and custom integrations
- No‑code/low‑code AI App wizard and templates for common fintech use cases (fraud detection, payment optimization, credit risk, churn prediction, etc.)
- Real‑time inference engine with sub‑50 ms response times and auto‑scaling to handle thousands of concurrent decisions
- Continuous‑learning pipelines that automatically retrain and rebalance models based on live outcomes
- Built‑in model transparency, audit trails, and guardrails to ensure explainability and compliance with business rules
- Multi‑channel deployment options: API endpoints, JavaScript tags, and direct integration into Excel or BI tools