Unify real-time, governed data for financial services
Combine data from analytical warehouse, operational databases, and object storage with compliant federation. Accelerate queries to millisecond latency and deliver AI features with full visibility and audit.
Do more with your data
0x
up to 100x faster queries
0%
up to 80% cost savings on data lakehouse spend
0x
increase in data reliability for critical workloads
Data fragmentation slows modern finance
Financial institutions depend on real-time data for trading, fraud prevention, and customer insights, but legacy ETL, disconnected search, and siloed analytics create delays and compliance risk. Financial services need governed, low-latency access across systems, and AI that operates transparently within those guardrails.
Governed data. Real-time performance.
Federate, accelerate, search, and manage data and AI for every workflow.
Federate and accelerate SQL across sources
Query disparate data sources in one SQL statement. Push down and materialize frequently accessed data locally for sub-second reads.
Real-time fraud detection
Detect emerging fraud patterns in-flight with low-latency access to the most recent transactions. CDC-driven updates keep local tables synced, enabling analytics on live financial data.
Intelligent document and record search
Combine vector semantics with keyword search across unstructured and structured data. Retrieve precise and semantically similar results in a single call.
AI gateway with policy guardrails
Route to hosted or local LLMs, with per-task sandboxes, masking, and full traces. Generate summaries, alerts, and recommendations grounded in governed data.
Built for regulated, low-latency financial workloads
Spice unifies federated access, acceleration, hybrid search, and AI for faster and more secure financial apps. Deployable anywhere with enterprise governance.
Enterprise-Grade Performance
Materialize hot data locally for real-time apps or fraud detection.
ETL-Free Syncs
CDC keeps apps and downstream systems up to date without batch jobs.
Hybrid Search
Vector and BM25 keyword search across research, contracts, and records.
Audit and Observability
Trace queries, prompts, and writebacks end-to-end.
MCP Server and Gateway
Securely extend AI with internal tools such as risk models or fraud APIs executed inside the runtime for compliant, low-latency decisions.
Deployment Flexibility
Run Spice anywhere: as a sidecar, microservice, cluster, or on the managed Spice Cloud Platform.
Deployed in production
Run data-intensive workloads on a high-performance engine trusted by teams building real-time systems at scale.
“Spice opened the door to take these critical control-plane datasets and move them next to our services in the runtime path.”
Peter Janovsky
Software Architect, Twilio
0x
Faster queries
“It just spins up and works, which is really nice. The responsiveness is amazing, which is a huge gain for the customer.”
Darin Douglass
Principal Software Engineer, Barracuda
“Partnering with Spice AI has transformed how NRC Health delivers AI-driven insights. By unifying siloed data across systems, we accelerated AI feature development, reducing time-to-market from months to weeks - and sometimes days. With predictable costs and faster innovation, Spice isn't just solving some of our data and AI challenges - it's helping us redefine personalized healthcare.”
Tim Ottersburg
VP of Technology, NRC Health
“Spice AI grounds AI in our actual data, using SQL queries across many data sources. This brings accuracy to probabilistic AI systems, which are very prone to hallucinations.”
Rachel Wong
CTO, Basis Set
Trusted by global enterprises
Build more robust financial applications
Guides and examples to learn more about building secure, performant applications with Spice.
Hybrid Search Docs
Learn how to combine semantic, full-text, and keyword search with Spice's hybrid search capabilities
Federation and Acceleration Docs
Learn how to get started with Spice federation and acceleration. Query, join, and accelerate data using SQL from multiple sources, including databases, data warehouses, and data lakes
Making Object Storage Operational for Real-Time and AI Workloads
TLDR Introduction Although legacy systems and workflows remain common, many enterprises are re-evaluating their architectures to meet new demands – driven in part, but not exclusively, by AI – that require support for more data-intensive and real-time applications. The underlying storage needs for these novel workloads are generally outside the bounds of a traditional operational […]
FAQs
Common questions about using Spice.ai in financial services
How do financial services companies use Spice.ai?
Financial services companies use Spice.ai to combine data from analytical warehouses, operational databases, and object storage into one governed, low-latency query layer. Common workloads include fraud detection on live transactions, document and record search, and AI features that operate with full visibility and audit.
Can Spice.ai detect fraud on live transaction data?
Yes. Real-time change data capture keeps locally accelerated tables synced with the most recent transactions, so analytics run against live financial data instead of stale batch extracts. This low-latency access to recent transactions supports detecting emerging fraud patterns in-flight.
How does Spice.ai search financial documents and records?
Spice.ai combines vector semantics with BM25 keyword search using hybrid SQL search, returning precise and semantically similar results in a single call. It searches across unstructured and structured data, such as research, contracts, and records.
How does Spice.ai keep AI usage compliant in regulated environments?
Spice.ai routes AI requests through a gateway with policy guardrails, supporting hosted or local LLMs with per-task sandboxes, data masking, and full traces. Queries, prompts, and writebacks are traceable end-to-end, so AI-generated summaries, alerts, and recommendations stay grounded in governed data.
Can AI agents use internal financial tools through Spice.ai?
Yes. The Spice.ai MCP server and gateway lets AI agents securely call internal tools, such as risk models or fraud APIs, executed inside the runtime. This keeps agent-driven decisions low-latency and within existing governance controls.
See Spice in action
Walk through your use case with an engineer and see how Spice handles federation, acceleration, and AI integration for production workloads.
Talk to an engineer