Princeton Strategy Group

Fine-tuned AI for regulated industries

We put open-weight language models into production inside banks and regulated firms — fine-tuned to the domain, running on infrastructure the institution controls. Then we proved it works by shipping two products of our own.

Who we are

PSG is a boutique risk-and-AI advisory formed by veteran Wall Street trading and risk professionals. For over a decade our experts have advised large financial institutions on model risk management, AI/ML adoption, and regulatory programs — CCAR/DFAST, SR 11-7 model validation, AML and fraud model reviews. That heritage shapes how we build AI today: models that can survive an examiner's questions, not just a demo.

Your weights are your fate

Every prompt, correction, and workflow your team feeds a rented frontier model is a distillation of your institution's judgment — your typologies, your thresholds, your niche — flowing into a system your competitors rent by the token. None of it accrues to you. And the strategic cost is only the beginning:

RegulatorsA self-hosted fine-tuned model can be version-frozen for an exam cycle, its outputs reproduced on demand, its training data documented and owned. A vendor API changes on the vendor's schedule — which makes version-pinned validation under SR 11-7 impossible by construction.
LegalFor financial-crimes work, SAR confidentiality (12 CFR 21.11; 31 CFR 1020.320) makes case detail entered into third-party AI a disclosure question most counsel have not yet been asked in writing. "The vendor says our data isn't used for training" is a contract term. A model that never leaves your infrastructure is an architecture.
AuditEvery AI-assisted decision needs a reproducible trail. When a vendor deprecates the model behind last quarter's decisions, you cannot reproduce them for an auditor — or an examiner. Owned weights make the trail permanent.
Data securityCustomer PII and confidential records should not have to leave your building to get AI leverage. Small fine-tuned models running in your VPC or on-premises close that gap without sacrificing capability.

What we've built

We don't just advise this — we ship it. Both products below run on small open-weight models we fine-tuned in-house, serving real users from single-GPU infrastructure. They are working proof that production-grade domain AI no longer requires a frontier lab — and that your data never has to leave your control.

ARIA — Agentic Risk Intelligence for AML

Built for banks of all sizes. Ask in plain English and ARIA sweeps rule thresholds with false-positive/SAR trade-off tiers, backtests against transaction history, runs behavioral segmentation and OFAC fuzzy screening, and drafts FinCEN-6W SAR narratives — producing examiner-ready documentation as a byproduct of the analysis itself. Runs vendor-hosted on dedicated GPU, in your VPC, or air-gapped on-premises; the weights never leave infrastructure you control. Live demo at aria.risktune.ai · published asset-band pricing at risktune.ai/pricing.

ARIA — analyst working with agentic AML threshold-tuning, segmentation, and policy agents

VARIA — Veracity Analysis & Reference Intelligence Agent

The verification engine behind DocuTruth (docutruth.ai). Upload a legal filing or research paper: VARIA extracts every citation and reference, verifies each against authoritative sources — court records, DOI registries, scholarly indexes — and scores genuineness and relevance, catching fabricated and AI-hallucinated citations before they reach a court, a journal, or a regulator. The same fine-tuned model family as ARIA, pointed at a different failure mode of the AI era.

VARIA — reference verification: citation network analysis over an uploaded document

If your institution wants AI leverage without giving away its data, its niche, or its defensibility — talk to us.