Financial services is the industry where the cost of being wrong is regulatory. QGI ships AI that every compliance officer, regulator, and auditor can replay — not black-box scores, not "trust the model" answers.
Every "RAG + LLM" pattern in the stack becomes QAG — Quantum-Augmented Generation over Q-Prime — so contradictions in policy, contract, and regulatory corpora surface as structured signals before the decision gets made.
7M+
Regulated lending cycles per year (US)
> 20%
Mortgage compliance errors flagged per avg cycle
80%
Target reduction in analysis time (QGI pilots)
Provable
Audit trail surface
Mortgage compliance, credit, claims, AML/KYC, and disclosure review
QGI works with regulated teams to scope deterministic AI pilots around the decisions they already review, approve, and defend.
"Bring the workflow, the rulebook, and the audit requirement. QGI maps the policy corpus, defines success criteria, and shows where QAG can make the decision path easier to review."
"Regulated industries need AI they can audit and trust. The evaluation path starts with one business decision and the evidence trail behind it."
"Show me why." If the only answer is "the model is 94% confident," the decision doesn't hold up in a consent order, a class action, or an AG inquiry. Compliance needs reasoning it can sign.
01
Federal and state regulators increasingly expect that any AI touching a credit file, claim, or KYC record is explainable, replayable, and admissible. Probabilistic retrieval alone doesn't clear the bar.
02
Classical RAG retrieves the nearest neighbors in a vector space and hopes the LLM composes a defensible answer. In regulated decisioning, 'hopes' is a liability — the same prompt can yield two different rationales.
03
A wrongly-denied mortgage, a missed AML flag, or an unsupported claim denial is not just customer harm — it's a CFPB letter, a class action, or a state Attorney General inquiry. Reasoning needs to be defended.
04
Compliance teams are stretched thin. The path forward isn't replacing them with black-box LLMs — it's giving them deterministic signals they can sign, dispute, and document.
The same architecture runs underneath every financial services workflow QGI ships, from credit underwriting to AML triage.
Regulatory rulebooks, investor guidelines, state overlays, contracts, and policy documents are encoded as a quantum-structured hypergraph — preserving polarity, scope, conditions, and cross-rule dependencies. This is where "a lender must ... unless ..." actually stays attached to its exception.
Before generation, the Hilbert-Space Compacting layer projects the state into seven interpretable signals — Relevance, Conflict, Overlap, Redundancy, Coverage, Coherence, Topology. In FinServ this is where the system says 'this loan's DTI contradicts the investor overlay' in a way the compliance officer can read and sign.
Multi-step workflows — an underwriting review, an AML investigation, a disclosure sign-off — run as agents on top of QAG. Each step is structured, logged, and replayable. The audit trail isn't a PDF you generate at the end; it's the runtime itself.
Six workflows we're deploying into regulated financial services — each takes classical probabilistic retrieval as input and produces deterministic, replayable reasoning as output.
Guidelines, state-by-state rules, investor overlays, loan files. Q-Prime encodes the corpus as a quantum-structured hypergraph so contradictions between overlays and investor criteria surface as explicit Conflict signals before the model writes a single word of the decision letter.
Applicant files reasoned against ECOA / Reg B / state fair-lending rules. Every adverse-action reason code is traced back to the specific rule and the specific data point — with the full reasoning chain versioned and replayable for regulator review.
Policy language, coverage conditions, and loss reports reasoned together. The QAG Engine flags the cases where policy exclusions contradict the stated claim context so adjusters get a ranked, explainable queue instead of opaque scores.
Typologies, watchlists, sanctions regimes, and transaction graphs unified in a structured representation. Agents investigate with a traceable reasoning chain that survives OFAC, FinCEN, or FCA inspection — and dramatically reduce false-positive triage load.
Submission data reasoned against appetite guidelines, regulatory filings, and treaty language. The same reasoning chain that prices a risk can also defend it in a market conduct exam — no hidden feature importance, no unexplained decline.
10-Ks, prospectuses, swap disclosures, and regulatory filings routed through Documents QAG so contradictions between filings, MD&A narrative, and reported financials surface before the filing goes out — not during an SEC comment letter.
Forked from NVIDIA AI Blueprints and other production-grade bases, rewired to Q-Prime and the QAG Engine inside the QGI Enterprise Factory.
Primary pipeline for policy, contract, and regulatory corpora — the base for mortgage, insurance, and capital-markets workflows.
Reasoning agents connected to enterprise data — the execution layer for underwriting and claims workflows.
Multi-agent coordination with conflict signals — the pattern underlying AML investigation swarms and disclosure review.
Regulatory-text equivalence across languages — critical for global lenders, cross-border KYC, and multi-jurisdiction insurance.
Pilot intake is open for mortgage compliance, credit, claims, AML/KYC, and disclosure review
Policy and regulatory-document workflows rebuilt as deterministic QAG
Reasoning platform for decisions that must be explained, audited, and defended
We work with a small number of regulated teams each quarter — lenders, insurers, asset managers, compliance platforms. Bring us the workflow, the rulebook, and the audit requirement. We'll ship the QAG pipeline.
