Scaling Financial Research and Analysis with Multi-Model LLM Architecture

rogo · Finance · 2024 · source Agentic: no · promotional bias: high · source-backed · extracted with prompt 0.5.6 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: financial research automation

Rogo needed to automate financial research, diligence, and analysis over large financial datasets while preserving analyst workflows.

Constraints:

• Financial professionals need real-time analysis over proprietary and public financial datasets.
• Different research tasks require different model capabilities.
• Analyst trust depends on saving time without removing collaborative review workflows.

Architecture

Rogo fine-tunes and integrates OpenAI models with financial datasets such as S&P Global, Crunchbase, and FactSet. GPT-4o powers chat Q&A and analysis, o1-mini contextualizes and structures financial data for search, and o1 supports evaluations, synthetic data generation, and advanced reasoning workflows.

Diagram confidence: explicit (topology described by the source).

flowchart LR
  A["Financial datasets"] --> B["Rogo platform"]
  B --> C["GPT-4o analysis layer"]
  B --> D["o1-mini structuring layer"]
  B --> E["o1 reasoning and evaluation layer"]
  B --> F["Analyst workflows"]

Components

Data flow

Financial datasets are integrated into Rogo's platform, OpenAI models are fine-tuned and routed by task, o1-mini structures data for search, GPT-4o handles chat and analysis, o1 supports evaluations and advanced reasoning, and analysts use outputs for meeting prep, company profiling, market maps, due diligence, and collaborative workflows.

Why this architecture

Rogo uses a multi-model architecture because financial research combines search structuring, chat Q&A, diligence, synthetic data, evaluation, and advanced reasoning rather than one homogeneous LLM task.

Models used: GPT-4o · o1-mini · o1 · fine-tuned OpenAI models

Scale

• Rogo serves over 5,000 bankers.
  “Served over 5,000 bankers across publicly-traded investment banks and mega cap private equity firms.”article, stated
• Rogo saves analysts more than 10 hours per week on research tasks.
  “Saved analysts 10+ hours per week on tasks like meeting prep, company profiling, and market research.”article, stated
• The platform can search and analyze over 50 million financial documents.
  “search and analyze over 50 million financial documents.”article, stated
• Rogo grew ARR 27x using OpenAI models.
  “Grown their Annual Recurring Revenue (ARR) 27x, using OpenAI’s models to power their solutions.”article, stated

Alternatives considered (roads not taken)

Manual research workflows

Rejected because: Rogo shifts analysts away from repetitive manual work toward higher-value decisions.

Single model for all financial tasks

Rejected because: Rogo assigns GPT-4o, o1-mini, and o1 to different search, analysis, evaluation, and reasoning jobs.

Patterns

model-routing (model-ops)

Rogo assigns different OpenAI models to chat analysis, search structuring, evaluation, synthetic data, and advanced reasoning.

fine-tuning-adaptation (model-development)

Rogo fine-tunes OpenAI models for financial-work challenges and integrates them with financial datasets.

synthetic-data-generation (evaluation)

Rogo uses o1 for synthetic data generation alongside evaluations and reasoning workflows.

Tradeoffs

Financial research platform design

Chose multi-model OpenAI architecture over single-model assistant.

Rationale: Different financial workflows need analysis, search structuring, evaluation, synthetic data, and reasoning capabilities.

Workflow adoption

Chose collaborative analyst workflows over individual-only research automation.

Rationale: Rogo is used by both junior and senior professionals as a shared resource for research tasks.

Gotchas (what bit them)

Financial work is not one LLM task.

Root cause: Research, data structuring, diligence, search, evaluation, and reasoning need different capabilities.

Resolution: Use a multi-model architecture with task-specific roles for GPT-4o, o1-mini, and o1.

Evaluation

The article reports analyst time saved, banker adoption, ARR growth, and model role separation; o1 is also used for evaluation workflows.

Metrics: analyst hours saved · bankers served · ARR growth · documents searchable · evaluation workflow usage

Outcomes

• Rogo serves over 5,000 bankers.
• Analysts save more than 10 hours per week.
• The platform searches and analyzes over 50 million financial documents.
• ARR grew 27x using OpenAI models.

The source is an OpenAI customer story, so commercial claims are attributed to the article.