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LLM-Powered Financial Assistant for Structured + Unstructured Insights

LLM-Powered Financial Assistant for Structured + Unstructured Insights

How Reckonsys enabled a global fintech provider to deliver actionable insights by combining real-time financial data with unstructured market intelligence.

LLM-Powered Financial Assistant for Structured Unstructured Insights
client

Investment Solutions Provider – USA

Industry:

Financial Services

Services Offered

LLM-Powered Financial Query Assistant

LangGraph Orchestration & Hybrid RAG

LLM-Based Field Mapping & Persona-Aware Formatting

Retrieval Fusion & Reranking

Background

The client, a leading investment solutions provider in the USA, needed a GenAI assistant to handle both structured and unstructured financial data:

  • Analysts looking for quick KPI insights
  • Executives needing decision-ready summaries
  • Finance teams demanding fused outputs from filings

& reports

Manual searches slowed response times and limited real-time insights.

Challenges

  • Inconsistent policy interpretation across teams created confusion and slowed processes.
  • Delays in legal response times due to lack of alignment and clarity.
  • Manual legal searches made it difficult to quickly identify relevant information.
  • Critical articles or clauses failed to surface in real time, limiting efficiency.

Solutions

Reckonsys built a GenAI-powered financial assistant that combined structured and unstructured data to deliver context-aware insights:


  • LangGraph Orchestration + Hybrid RAG → enabled dynamic data-document fusion for richer insights.
  • Claude & Mistral Models → applied reranking, formula logic, and prompt tuning for accurate responses.
  • LLM-Based Field Mapping → translated natural language into KPI mapping, temporal logic, and formula detection.

1. Parallel Query Flow

  • Structured (MongoDB) and unstructured (Pinecone + Elasticsearch) data executed simultaneously for complete coverage.
  • Reduced delays by running both query types in parallel for faster insights.

2. LLM-Based Field Mapping

  • Natural language mapped into KPIs, temporal logic, and formulas.
  • Enabled accurate detection of complex financial relationships and metrics.

3. Persona-Aware Formatting

  • Role-tuned prompts and templates shaped responses for executives
  • Delivered concise summaries for leaders and detailed outputs for analysts.

4. LangGraph Orchestration

  • Dynamic flow control and routing based on query type and user context.
  • Ensured optimal use of retrieval, ranking, and summarization layers.

5. Retrieval Fusion & Reranking

  • Combined semantic and keyword search with custom scoring for higher relevance.
  • Applied LLM summarization to deliver concise, context-aware outputs.

2x Faster

Decisions

Clear KPIs

Accurate answers

Relevance

Role-based outputs

LLM-powered orchestration enabled faster insights, clearer KPIs, and relevant outputs.

Resources Utilized

Text

Financial reports and filings

Consensus Estimates

Analyst forecasts and market projections

Tabular Data

Performance metrics and financial tables

Charts & Plots

Data visualizations with explanatory notes

Documents

Digitized filings and supporting documents

Knowledge Assets

Structured Guidance

Rule sets and templates ensured a consistent framework for handling queries.

Reference Resources

Reference tables and codebooks provide quick lookup material for accurate responses.

Data Models

Ontologies and taxonomies organize information into structured relationships for better classification.

Results & Benefits

2x Faster

Decisions from fused data and documents

Clear KPI Insights

Answers through dynamic field mapping

High Relevance

Outputs tuned to role and context

Technology Stack

LLMs & Orchestration

Claude 3.7 Sonnet, Mistral Medium, GPT-4 Turbo, and LangGraph were used for orchestration, reranking, and context-aware responses.

Databases & Retrieval

Pinecone, Elasticsearch, MongoDB, Voyage AI, and text-embedding-3-large enabled hybrid RAG with semantic + keyword search.

Development & Infrastructure

Python, Django, Formula Engine, RAGAS, Docker, and Prompt Tuning supported implementation, evaluation, and deployment at scale.

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