Case study · AI agents & RAG
Multi-agent equity research
A multi-agent system for enterprise financial research. Eight specialist agents — from a data-quality auditor to a chief editor — analyze a company from every angle, score it on an explicit weighted rubric and publish the result as a structured report, a dashboard and a document. The model runs locally.
- 8specialist agentsEach with one role and one structured output
- 50+metrics per companyCollected from Finnhub and FMP market-data APIs
- 10×faster first-pass researchProject estimate versus manual collection and analysis
The challenge
Analysts spend most of their time collecting and reconciling data before any judgement happens: fundamentals, price action, peers, news sentiment, risk. Off-the-shelf chat assistants could summarize, but couldn’t be trusted with numbers — and sending proprietary research questions to an external API was not acceptable.
Our approach
We split the analyst’s job into eight roles and gave each its own agent, with a data-quality agent checking inputs before anyone reasons over them and a chief editor synthesizing the final call. Scores combine on an explicit weighting — valuation, quality, momentum, sentiment — so every recommendation is traceable to its parts. Llama 3 runs locally through Ollama, keeping data on-premise.
Workflow
How it works, step by step.
The pipeline in 5 stages, from collect to report. Each stage has a clear input, a clear output and a reason to exist.
- 01
Collect
Market and fundamentals data pulled from the Finnhub and FMP APIs and stored in SQLite.
- 02
Prepare
A data pipeline computes 50+ metrics per company; the Data-QA agent audits them before analysis.
- 03
Analyze
Fundamental, technical, sentiment, peer and risk agents each produce a scored view; a strategist sizes the position.
- 04
Score
A composite score weights valuation 25%, quality 35%, momentum 25% and sentiment 15%.
- 05
Report
The chief-editor agent writes the BUY/HOLD/SELL report as JSON, an HTML dashboard and Markdown.
Results
What it produced.
The eight agents
- Data QAData-quality auditor
- FundamentalValuation & quality
- TechnicalPrice & momentum
- SentimentNews analysis
- Peer / IndustryPeer comparison
- Risk OfficerRisk assessment
- StrategistPosition sizing
- Chief EditorFinal synthesis
Composite scoring · example run
Weighted: 0.25·25 + 0.35·95 + 0.25·75 + 0.15·73 ≈ 69. Every recommendation decomposes into the scores that produced it.
Example run on NVIDIA (NVDA) during development. System output shown for illustration only — not investment advice.
Stack & techniques
- Python
- AutoGen
- Ollama
- Llama 3
- Finnhub API
- FMP API
- SQLite
- React
- Recharts
- Tailwind CSS
Delivered by members of the Saerosense founding team. Client names are withheld unless public — references are available on request.
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