Don't just check for hallucinations. Constrain generation to approved sources.
AI systems excel at generating fluent, confident text. But fluency and accuracy are not the same thing. When organizations deploy AI for high-stakes documentation—regulatory submissions, legal briefs, compliance reports, technical documentation—hallucinated citations, fabricated sources, and invented details create liability exposure and regulatory risk.
Traditional approaches rely on retrieval-augmented generation or post-generation fact-checking. Both are probabilistic. Neither can demonstrate fidelity to approved sources with certainty. Organizations submitting work to regulators, examiners, auditors, or courts need verifiable source fidelity.
The Source Fidelity Engine constrains AI-generated content to derive from approved source material. Organizations define the materials the system may reference, and the architecture limits generation to those boundaries.
Approved Source Definition
Organizations define the corpus of approved materials. Technical disclosures. Regulatory guidance. Legal precedents. Internal documentation. The system operates within defined boundaries.
Source Constraint
Generation is limited to approved materials. The system restricts references to content outside the defined corpus.
Output Traceability
Generated elements—claims, citations, technical details, supporting facts—are mapped to source locations within the approved corpus for supported output types. Provenance tracking is built into the system.
Third-Party Verification
Examiners, auditors, and reviewers can examine whether output elements derive from approved materials. The system provides audit trails to support compliance review.
Organizations working with AI-generated documentation where source accuracy and traceability matter.
Structured source constraint, not probabilistic retrieval. Traditional systems retrieve relevant content but do not restrict what the model can generate. Source Fidelity Engine constrains generation to approved materials and provides traceability for output elements.
Traceability by design, not post-hoc checking. Output elements are mapped to source locations during generation for supported output types. Reviewers can examine provenance. Compliance support is built in.
Hallucination constrained through source-bound generation. The system limits citation fabrication and detail invention by restricting generation to defined source boundaries.
External review supported. Auditors, examiners, and reviewers can examine source mappings and review system operation. Transparency supports trust.
ArchI's constraint architecture operates independently of model size or capability. Organizations can deploy with cloud APIs for rapid integration, lean local models for air-gap security and complete data sovereignty, or hybrid approaches balancing cost and control. No retraining required. Integrates with existing infrastructure.
Safety guarantees don't degrade with smaller models. Lightweight local models with architectural enforcement can provide stronger compliance guarantees than larger cloud models relying on behavioral guidelines, because enforcement is structural, not persuasive.
Customer-controlled cloud tenants, on-premises infrastructure, or air-gap environments.
Competitors say: "We retrieve relevant content."
We say: "The model speaks from approved sources."