Prominent Advisory Group | Independent LLM Validation for Financial Document AI

What the benchmark found

We tested five frontier LLMs — ChatGPT-4o, Claude, Gemini, Grok, and DeepSeek — against 789 human-annotated paragraphs from S&P 100 10-K filings.

59–69%LLM accuracy against human ground truth. A domain-specific model reached 83%.

1 in 3LLM classifications diverged from expert judgment — even when the models agreed with each other.

850–3,660×measured cost difference between a domain classifier and commercial LLM APIs at scale.

Vo, Kim, Plachkinova & Lestyk (2026), Benchmarking ESG Risk Classification in 10-K Filings, Journal of Computer Information Systems · DOI · free preprint

Is this for you?

The engagement is built for four situations:

A fintech whose bank partners or enterprise buyers are asking whether the model has been independently validated — and the deal is waiting on the answer.
A community bank or credit union that adopted an AI document tool and now needs an SR 11-7-defensible validation file it cannot produce internally.
A broker-dealer or adviser using LLMs in regulated workflows under 2026 supervisory expectations.
A team whose AI must appear in an EU AI Act technical file before the August 2, 2026 high-risk obligations, with accuracy and robustness actually measured. Plain-language guide to these rules →

The engagement

Independent LLM Validation Report

$6,500 fixed · 10 business days

Tested: accuracy against dual-coded human ground truth, per-category performance with pre-registered thresholds, repeated-run stability, robustness under perturbation, confidence calibration, and a severity-rated failure-mode inventory.
Delivered: a written report mapping each finding to SR 11-7 documentation expectations and structured for reuse in an Annex IV technical file.
Kept: the full reproducible test harness (Python) — rerun it yourself on every model update.
Explained: one walkthrough session with your risk or engineering team, plus remediation recommendations for every flagged finding.

Larger scopes — multiple models, custom ground-truth construction, quarterly revalidation retainers — quoted fixed after the scoping call. Works on vendor systems through inputs and outputs; no vendor cooperation required.

How it runs

01
Scoping call
Confirm the system, the task, the test data, and the thresholds your risk owner needs. Fixed fee confirmed in writing.
30 min
02
Ground truth & harness
Test set assembled or audited, thresholds pre-registered, your model wrapped in the validation harness.
Days 1–4
03
Validation battery
Performance, stability, robustness, calibration, and slice analysis run and documented.
Days 5–8
04
Report & walkthrough
Written report delivered with the harness; findings walked through with your team.
Days 9–10

Independence is structural, not a slogan: this practice builds no models, sells no AI products, and takes no vendor referral fees. Thresholds are agreed before testing begins, so no finding — favorable or not — changes what you pay.

About the validator

Ace Vo, PhD

I'm an Associate Professor of Information Systems and Business Analytics, and the lead author of one of the first large-scale, peer-reviewed benchmarks of frontier LLMs against human ground truth on regulatory filings — the study this practice's methodology comes from.

Alongside academic work, I've spent years on production NLP systems, including large-scale clinical and legal text pipelines. That's where the gap between benchmark claims and deployed reality became impossible to ignore, and why this practice exists.

ORCID Full background Email

Common questions

We can't share production data.

Most engagements run on a held-out test set you provide, de-identified samples, or public filings matched to your task. Ground truth can be built entirely on your side using our protocol.

Our system is a vendor product we don't control.

That's the most common case. The harness tests any system through its inputs and outputs — API, batch file, or export. No vendor cooperation required.

Will this satisfy our examiner?

No external report can promise a regulatory outcome. What the report does is document independent testing in the structure model-risk reviewers expect: pre-registered thresholds, reproducible methods, findings tied to remediation.

What happens when the model updates?

You keep the harness, so revalidation is a rerun, not a new project. A quarterly revalidation retainer is available if you'd rather we run and document it.

One call decides if this fits.

Bring the system and the question your risk owner is asking. You'll leave with a fixed fee and a start date, or a straight answer that you don't need this.

Request a scoping call