Real-time hallucination filter
Catches unsupported claims, fabricated citations, and grounding drift before they reach users.
We build systems where every output has a reason, every decision has a trail, and every answer can point to its source. Not as a compliance feature - as an architectural principle.
Can you explain why the system said what it said? Can you trace a customer-facing answer back to a source document? Can a regulator audit the decision path?
We build systems where the answer is yes to all three. Not by adding an "explainability layer" after the fact - by making traceability, grounding, and human oversight structural properties of the architecture from the start.
How tracing works
Natural language input enters the system
RAG pipeline pulls relevant documents with citation IDs
Model produces answer grounded in retrieved sources
Real-time filter catches unsupported claims before delivery
User sees the response and exactly where it came from
Capabilities
RAG systems, citation-tracked responses, factual memory - AI that tells you where its answer came from.
When AI makes a recommendation or classification, the reasoning is visible. Not a black box.
Every interaction, every decision, every escalation - logged, structured, and queryable. For internal review or external regulators.
Real-time systems that catch when an AI is generating unsupported claims, fabricating citations, or drifting from grounded sources.
Systems designed with escalation paths, approval gates, and human override - built into the architecture, not added as an afterthought.
Catches unsupported claims, fabricated citations, and grounding drift before they reach users.
GraphRAG with full citation tracking. Every answer traces to a source document.
Traceability, grounding, and human oversight - structural properties, not afterthoughts.