Models your model risk team will approve.

Financial AI lives under SR 11-7, fair-lending scrutiny, and auditors who ask hard questions. We build ML and LLM systems with the documentation, monitoring, and explainability that get them through validation — and into production.

Discuss your use case →What we build

quantpi · industry/telemetry

$ industry.constraints()
✓ frame: SR 11-7 · fair lending · explainability-first
✓ deployment: cloud · hybrid · air-gapped
✓ proof standard: measured on your data
# domain constraints are design inputs, not blockers

IND/01What's at stake

In finance, an unexplainable model is an unapproved model.

The binding constraint in financial AI isn't accuracy — it's the second-line review. Models that can't articulate their drivers, demonstrate stability, and prove disparate-impact testing don't ship, no matter their AUC. We design for validation from the first commit: documentation, challenger models, and monitoring as deliverables, not afterthoughts.

IND/02What we build here

Use cases we ship

Fraud & AML detection

Real-time scoring with adaptive thresholds, alert triage that cuts false positives, and case-management integration — measured on analyst hours saved.

metric: false-positive reduction

Document automation

KYC packets, loan files, claims, trade confirmations — extracted, validated, and routed with per-field confidence and four-eyes review where rules demand it.

routing: confidence + rules

Credit & risk model engineering

Modern ML risk models with full MRM documentation: development evidence, benchmarking, sensitivity analysis, and ongoing-monitoring plans.

docs: validation-ready

Customer intelligence & advice support

RAG over product terms, policies, and regulations so advisors answer from the source — cited, current, and logged.

grounding: policy-cited

Regulatory change monitoring

Tracking and impact-classification of regulatory updates against your obligations register — first-pass triage at machine speed.

triage: obligation-mapped

Surveillance & conduct analytics

Communication and trade surveillance with explainable flags and tunable precision — fewer, better alerts for your compliance team.

alerts: explainable

IND/03Domain constraints we design for

Built for your constraints

Model risk management aligned to SR 11-7 / ECB TRIM expectations
Fair lending and disparate-impact testing wired into CI
Explainability (SHAP, reason codes) as a product feature
Data residency and sovereignty by deployment design
Complete decision logs for supervisory requests
Challenger models and ongoing monitoring as standard

IND/04Questions, answered straight

FAQ

Can LLMs be used in regulated financial workflows?

Yes, scoped correctly: drafting, summarization, triage, and retrieval with human decision-makers — rather than autonomous determinations on credit or claims. We design the human-AI boundary explicitly and log it, which is what both regulators and your second line want to see.

How do you handle model explainability?

As a requirement, not a retrofit: feature attribution (SHAP), stable reason codes for adverse action, and documentation linking model behavior to business logic. Where deep models can't meet the explainability bar for a decision class, we'll say so and propose an interpretable alternative.

What about our data leaving the bank?

It doesn't have to. We deploy inside your VPC or fully on-premises, including open-weight LLM stacks. Several patterns route only non-sensitive workloads to external APIs with everything material staying inside your boundary.

Do you work with our model validation team?

From day one — we'd rather meet the second line at design review than at rejection. Our documentation packs are structured around what validators actually check: conceptual soundness, development evidence, benchmarking, and monitoring plans.