Intelligent Analytics
LLM-Powered Analytics Engine
Domain-tuned language models with retrieval-augmented pipelines that turn unstructured financial text, including filings, news, and transcripts, into quantitative signal.
Most of the information that moves markets arrives as text: filings, earnings calls, news, disclosures. The analytics engine reads that text the way an analyst would, and converts qualitative context into signals the rest of the platform can act on.
Overview
Three pieces work together:
- Domain-tuned models: language models adapted to financial corpora so they interpret sentiment, events, and regulatory nuance with precision.
- Model interoperability: the engine connects to both external providers (such as OpenAI and Anthropic) and in-house models, so the best model can be chosen per task.
- Retrieval-augmented pipelines: a vector store supplies the model with the most relevant documents at inference time, grounding its output in evidence.
Domain tuning
General models understand language; markets need models that understand this language. The engine adapts models to financial text and refreshes them on recent market data, keeping interpretation current as conditions evolve. An API gateway load-balances requests across models so the system stays responsive under demand.
Retrieval-augmented generation
Financial documents and market state are embedded and stored in a vector database (such as FAISS or Weaviate). At query time, the most relevant context is retrieved and passed to the model. This sharpens sentiment analysis and signal generation and keeps outputs grounded rather than speculative.
In practice
- Sentiment and signals: unstructured text from news, filings, and social becomes structured, quantitative input.
- Semantic search: natural-language queries over large document sets (10-Ks, earnings transcripts) surface insight on demand.
- Analyst-style reporting: summaries and briefings are generated automatically to support decisions.