In fintech, a hallucinated AI answer isn't a product bug.
It's a regulatory event.
We build production AI systems for US fintech companies — document intelligence platforms, investor verification pipelines, compliance-ready RAG systems. Every output is citation-traced, confidence-scored, and auditable before it reaches a user or a regulator.
In most industries, an AI system that occasionally produces wrong answers is a usability problem. In fintech, it's a compliance event, a liability, or a reputational incident. The failure modes are the same — but the consequences are categorically different.
Most AI vendors don't build for this. They build for demo accuracy. We build for regulatory durability: every output citation-traced, every retrieval access-controlled, every quality gate automated.
Fintech-specific failure modes
Hallucinated compliance answers
An AI that produces ungrounded answers on regulatory queries doesn't just create bad UX — it creates audit exposure. Every answer needs to be traceable to a source document and defensible in front of a regulator.
Access control at the wrong layer
In multi-analyst environments, API-level access control isn't enough. Restricted financial documents surface to unauthorized users on semantically similar queries unless RBAC is enforced at the retrieval layer itself.
No audit trail on AI outputs
Regulators ask where the answer came from. If your AI system can't answer that question with a timestamped, source-linked audit trail, you have undisclosed risk in every deployment.
Hybrid retrieval pipelines with RBAC at the retrieval layer, citation tracing on every response, and RAGAS evaluation running on every deploy. Built to survive a compliance review, not just a product demo.
Learn more →KYC, AML, and sanctions screening integrated into a single compliance workflow. Audit-trail-complete processing, multi-provider orchestration, real-time ops visibility — and idempotent writes so no verification event is ever lost.
Learn more →AWS Airflow orchestration for high-volume financial data. Fault-tolerant DAGs, schema validation, lineage tracking, and Terraform-managed S3 lifecycle. Running 10 TB+/week in production with 99.9% uptime.
Learn more →RAGAS evaluation pipelines, golden test suites with 200+ financial query/answer pairs, regression testing on every deploy. You know before your users do — and before a regulator asks.
Learn more →FastAPI backends with audit logging, JWT/OAuth 2.0, RBAC, and structured logging with trace IDs. Deployed on AWS ECS or Kubernetes. Every event timestamped and stored — no gaps in the audit log.
Learn more →A 2-week technical assessment of your AI setup, data infrastructure, and compliance posture. Full written report with honest findings — before you commit to a build or before a regulatory review forces the conversation.
Start here — $3,500 →A US financial analytics platform needed to query proprietary market data and third-party financial feeds daily — with answers that could be cited, audited, and defended under compliance requirements. The previous approach produced hallucinated answers on complex financial documents.
System metrics · Live
"We had three previous attempts at RAG for our compliance team. All three collapsed on our real data within two months. Jonix built the fourth — it's been running at 99.9% uptime for eight months and survived two regulatory reviews without a single issue."
VP Engineering
US financial analytics platform · Series B · compliance-regulated
Most AI systems are built for accuracy on clean data, then compliance-hardened after the first audit finding. We start from the compliance requirements and build backward — every design decision made with the assumption that a regulator will eventually ask to see it.
Every fintech engagement includes
Compliance requirements mapping
We map your specific regulatory constraints before writing a line of code.
Retrieval-layer access control
RBAC enforced before vector search runs — not at the API response layer.
Full audit trail on every output
Every AI response linked to its source. Timestamped. Stored. Defensible.
Continuous evaluation post-launch
RAGAS pipelines and golden test suites running on every deploy — not one-time.
30 minutes. We'll talk about your compliance requirements, your data, and what a production-ready AI system looks like in your environment.
Book a Free Scoping Callor start with a 2-week AI Readiness Audit — $3,500