Technology
The Relvia architecture is engineered as a research stack — autonomous research, verification, and evaluation, working together to produce confidence-scored intelligence.
Each layer engineered as production infrastructure.
Multi-Agent Research Orchestration
An orchestrator decomposes the input into subtasks and dispatches them across specialized research agents operating in parallel. Each agent runs with explicit constraints — scope, source preferences, and evidence contracts — so its output can be verified downstream without manual interpretation.
- Task decomposition with explicit evidence contracts
- Parallel agent execution with scoped tool access
- Structured intermediate outputs, not free-form text
Source Verification
Sources are scored at retrieval time by provenance, recency, corroboration, and domain authority. The verification layer maintains an auditable trail so that any conclusion can be traced back to the evidence that supports it.
- Provenance and recency-aware retrieval scoring
- Cross-source corroboration before claim acceptance
- Per-claim citation graphs for full traceability
Evaluation Engine
The evaluation engine runs as an independent system rather than a final filter. It compares model outputs, detects conflicts, and stress-tests claims against both retrieved evidence and alternate model reasoning paths.
- Cross-model comparison and disagreement detection
- Adversarial stress-testing of high-impact claims
- Independently versioned from research agents
Confidence Layer
Every output is annotated with a calibrated confidence level. High-confidence findings are surfaced as conclusions; uncertain claims are surfaced as hypotheses with explicit gaps. Decisions are made on signal, not surface.
- Calibrated confidence levels per conclusion
- Explicit hypotheses for low-support findings
- Failure-mode reporting for transparency
Reporting Interface
Outputs are designed as decision-support artifacts. Reports are structured, traceable, and built for analysts and operators — not chat consumption — with native APIs for downstream systems.
- Structured report objects, not free-form chat
- Citations, claim graphs, and confidence inline
- API surfaces for downstream automation
Decisions need structure, not text.
Every Relvia output is a structured report object — claims, citations, evidence, and confidence — exposed via API for use in downstream systems and dashboards.
{
"query_id": "rlv_a18f3d",
"claims": [
{
"id": "c_01",
"statement": "Market growth forecast remains > 12% YoY.",
"confidence": "high",
"sources": ["src_412", "src_557", "src_603"],
"cross_model_agreement": 0.92
},
{
"id": "c_02",
"statement": "Pricing power shifts toward new entrants.",
"confidence": "medium",
"sources": ["src_318"],
"cross_model_agreement": 0.61
}
],
"verification": {
"sources_evaluated": 142,
"claims_verified": 38,
"conflicts_resolved": 7
},
"report": "https://api.relvialabs.ai/r/rlv_a18f3d"
}