AI That Cites Every Source.
Defends Every Decision.
No uncited claims. Every copilot response is grounded in specific source documents with citations you can trace, verify, and defend to any regulator.
Generative AI Has a Trust Problem In Regulated Industries, It's a Compliance Problem
When ChatGPT tells you a company is on a sanctions list, you can't submit that to your regulator. There's no source document, no confidence score, no audit trail, no way to verify the claim was accurate at the time it was made.
For enterprises under regulatory scrutiny — financial institutions under BSA/AML, healthcare under HIPAA — "the AI said so" is not an acceptable basis for a compliance decision. Regulators require reproducible, auditable evidence trails.
LLMs are probabilistic. They generate statistically plausible text, not verified facts. They cannot provide deterministic, explainable results that survive regulatory examination. Evidence-first architecture is the prerequisite for deploying AI in any regulated workflow.
From Source Document to Auditable Decision — Every Step Traceable
Evidence-First vs Generic AI
| Dimension | Generic AI Copilots | ioNova Evidence-First |
|---|---|---|
| Source Attribution | None — parametric memory | ✓ Mandatory citations to source docs |
| Confidence Signaling | Binary — answer or decline | ✓ Graduated 0–100 scores with thresholds |
| Reproducibility | Non-deterministic | ✓ Deterministic evidence retrieval |
| Audit Trail | Chat logs only | ✓ Complete evidence packs with reasoning |
| Hallucination Risk | High — plausible but unverified | ✓ Uncitable claims explicitly flagged |
| Regulatory Acceptance | Insufficient | ✓ Designed for regulator examination |
Evidence-First AI — Your Questions Answered
What is evidence-first AI and how does it prevent hallucinations?
Evidence-first AI is an architectural approach where every factual claim produced by an AI copilot must be grounded in specific, traceable source documents — never generated from the model's parametric memory alone. Unlike generic AI chatbots that produce statistically plausible text without verification, ioNova's copilots pair every claim with a mandatory citation including the source document, relevant passage, date of record, and confidence score. Claims that cannot be cited are explicitly flagged as uncertain. This eliminates AI hallucination as a risk vector for regulated workflows, because every response carries a verifiable evidence trail.
Why can't generic AI copilots be used for compliance decisions?
Generic AI copilots like ChatGPT are probabilistic systems — they generate text that is statistically likely to be correct, but they cannot provide deterministic, reproducible, explainable results that survive regulatory examination. When a generic AI tells you a company is on a sanctions list, there is no source document, no confidence score, no audit trail, and no way to verify the claim was accurate at the time it was made. Under regulatory frameworks like BSA/AML, HIPAA, SR 11-7, and the EU AI Act, "the AI said so" is not an acceptable basis for a compliance decision. Regulators require reproducible, auditable evidence trails — which is exactly what evidence-first architecture provides.
How do evidence packs work in ioNova and what do they contain?
Every interaction with ioNova's copilots generates a complete audit-grade evidence pack containing: the original query, all retrieved source documents with relevance rankings, the generated response, mandatory citations linking each claim to specific passages, confidence scores on a 0–100 graduated scale with configurable thresholds, and the complete reasoning chain. These evidence packs are cryptographically timestamped and stored immutably, ensuring they can be presented to regulators exactly as produced. This achieves 100% citation coverage and delivers 50–70% time savings in compliance investigations by providing pre-assembled evidence for every decision.
What is AI explainability and how does ioNova achieve it?
AI explainability refers to the ability to understand, interpret, and trace why an AI system made a particular decision. ioNova achieves explainability through three mechanisms: grounded retrieval ensures every claim traces to a source document; mandatory citation injection pairs each factual assertion with verifiable evidence; and complete decision replayability allows any decision to be reconstructed with the exact data, model version, and configuration that existed at the time. This goes beyond "explainable AI" frameworks that simply provide post-hoc rationales — ioNova's evidence is produced during normal operation, not generated retrospectively.
How does evidence-first AI handle uncertainty and low-confidence results?
Unlike generic AI that either provides an answer or declines, ioNova uses a graduated confidence scoring system from 0 to 100, with configurable thresholds for different workflow types. When confidence falls below threshold, the system explicitly flags the claim as uncertain and routes it for human review — it never silently guesses. This approach means analysts know precisely which elements of an AI-generated response they can trust and which require additional verification, eliminating the binary "trust everything or nothing" dilemma that plagues generic AI deployments in regulated environments.