finance_ops · finance · workflow
Fintool uses Braintrust to build a scalable LLM evaluation workflow for financial AI insights
Institutional investors face a sheer volume of daily regulatory filings that makes it impossible for humans to review every document, and Fintool needed real-time monitoring to maintain quality and user confidence in its AI-generated financial insights.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Investor configures monitoring
Investors select the companies they want to monitor and configure alerts specifying what type of information they want summarized.
Tools used
BraintrustLLM-as-a-judge scorersLLMClassifier
Outcome
Fintool's evaluation workflow now manages millions of LLM-generated insights, processing 1.5 billion tokens across 70 million data chunks daily while improving accuracy, consistency, and efficiency at scale.
Results
Volume1.5 billion tokens
Grounding & classification
Source type: vendor customer story
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data extractiondocument aiquality inspectionsummarizationknowledge basepolicy documenthuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedfinancial servicesaccuracy improvementthroughput increasevendor customer storycompliance monitoringfinance opsquality assurancehuman review queuemonitor detect alert