Financial Services

Semantic SEC Filing Research for a Financial Research Firm

Semantic SEC Filing Research for a Financial Research Firm

3.4x

3.4x

Faster search turnaround versus the legacy keyword system

91

91

System Usability Scale score achieved (out of 100)

About

The firm is a boutique investment research firm whose analysts needed plain-English access to thousands of SEC filings instead of building arcane Boolean search strings by hand. We built a fine-tuned semantic search layer with a full compliance audit trail.

Industry

Financial Services

Company size

100 – 500 employees

Founded

2014

The Company

An analyst team spending more time querying than analysing

The firm provides fundamental investment research grounded in public SEC filings, covering a wide universe of listed companies for institutional clients. Analysts needed reliable, fast access to thousands of 10-K, 10-Q, 8-K, and proxy filings for comparative analysis.

The firm's existing search tooling relied on 256-character Boolean query strings against the legacy EDGAR interface — powerful in principle, but slow and error-prone to construct correctly for a nuanced comparative question.

The challenge

Arcane query syntax was costing three staff-hours a day

Constructing a Boolean search precise enough to surface the right filings — without either missing relevant documents or drowning in irrelevant ones — consumed roughly three staff-hours across the analyst team every day, time that came directly out of the interpretive work that was the firm's actual value-add.

Compliance also required an audit trail of what was searched and what was found, which the legacy Boolean interface did not support in any structured, exportable way — creating a second layer of manual record-keeping on top of the search itself.

The Solution

A QLoRA-tuned semantic search layer with a built-in audit trail

We fine-tuned a MiniLM retrieval model using QLoRA on a corpus of SEC filings and deployed it behind a Streamlit interface, letting analysts search in plain English rather than Boolean syntax. Every query and its results are logged to DuckDB, giving compliance the exportable, long-term audit trail the legacy system couldn't provide.

The interface supports comparative queries across companies and reporting periods directly, surfacing the specific filing passages that support a result rather than returning a flat list of document links for the analyst to open one by one.

The Results

3.4x faster search and a usability score in the top tier

Search speed improved 3.4x against the legacy Boolean interface, and the system scored 91 on the System Usability Scale — a strong result that reflected how directly analysts could go from question to answer without translation into query syntax. The three staff-hours a day previously spent on query construction across the team were eliminated entirely.

Compliance gained a structured, exportable audit trail for the first time, satisfying an internal requirement that the legacy tooling had never been able to meet without a parallel manual logging process.

KEEP READING

See How Other Teams are

Winning with Seven Billion

See How Other Teams are

Winning with Seven Billion

Explore more studies on AI, analytics, and enterprise intelligence.

The lowest-risk way to find out if AI is right for your business.

Phase 0 is a two-week discovery that tells you exactly which problems AI can solve, what it will take to build, and what it will cost. No obligation beyond it.

ABOUT Seven Billion

Seven Billion is an Applied AI company — a team of data scientists and AI engineers who build and deploy AI systems that run in production. Founded in 2020. Offices in Boston, USA and Bengaluru, India.

OFFICE

Boston, USA
Bengaluru, India

Intelligence that delivers starts here.

Whether you are mapping your first AI use case or scaling AI across the enterprise, we will help you cut through the noise and build something that actually ships.

ABOUT Seven Billion

Seven Billion is an Applied AI company — a team of data scientists and AI engineers who build and deploy AI systems that run in production. Founded in 2020. Offices in Boston, USA and Bengaluru, India.

OFFICE

Boston, USA
Bengaluru, India

The lowest-risk way to find out if AI is right for your business.

Whether you are mapping your first AI use case or scaling AI across the enterprise, we will help you cut through the noise and build something that actually ships.

ABOUT Seven Billion

Seven Billion is an Applied AI company — a team of data scientists and AI engineers who build and deploy AI systems that run in production. Founded in 2020. Offices in Boston, USA and Bengaluru, India.

OFFICE

Boston, USA
Bengaluru, India