FinancialGPT

Capital markets operate at extreme data velocity, but advisory systems failed to keep pace. Investors faced fragmented data from 35+ heterogeneous sources with varying latency and schema standards, shallow robo-advisor outputs that masked underlying assumptions, and latency bottlenecks preventing actionable intraday insights. Crypto flash crashes and equities earnings cycles exposed a persistent gap between raw data abundance and decision-ready intelligence.

Syntera Tech built an end-to-end AI architecture integrating 35+ market feeds into a unified schema with 5–10s latency guarantees, a library of 200+ multi-interval technical indicators, and an ensemble forecasting stack (gradient boosting, RNNs, transformer-based time series models). Historical and predictive outputs were encoded into dense vectors in a high-performance vector DB, with a domain-adapted fine-tuned LLM and multi-similarity ensemble retriever (cosine, dot-product, BM25, hybrid embeddings) enabling sub-100ms RAG retrieval. The system was deployed on AWS with event-driven microservices and delivered across web (Laravel + Vue.js) and mobile (React Native) interfaces.

The platform delivers sub-10 second insights across 35+ live feeds, enabling intraday and algorithmic strategy compatibility. Predictive accuracy improved 12–15% over baseline ML methods on 1-day and 1-week horizons. Sub-100ms vector retrieval latency was sustained even under concurrent loads exceeding 50,000 parallel sessions, with 99.5% node uptime achieved. Explainability-first design with full data lineage (indicator → forecast → reasoning) supports institutional and compliance adoption.

• RAG with a multi-similarity ensemble retriever (cosine, dot-product, BM25, hybrid) significantly improves contextual precision for financial LLM queries compared to single-method retrieval.
• Dual-storage architecture (PostgreSQL for transactional queries + MongoDB for historical analytics) is essential for serving both real-time and backfill visualization workloads at scale.
• Explainability is a first-class requirement in regulated financial environments — pairing narrative LLM outputs with auditable data lineage enables institutional adoption that opaque models cannot achieve.