This appendix covers the architecture, intelligence pipeline, and technical capabilities that power the risk profiles and analysis delivered in the POC as well as a preview of what we're building next.
Every risk profile is produced by a multi-stage pipeline: data ingestion, retrieval, driver discovery, risk quantification, graph construction, and agent-driven analysis.
News · Social · Legal
Academic · Regulatory
High-recall retrieval
Proprietary reranking
Risk driver extraction
and hierarchy
Perceived and actual risk
Entity attribution
Comparable, calibrated
Outcome-tied metrics
Entity relationships
Decision traces
Constructs and iterates on search queries to maximize coverage and relevance.
Finds the evidence and mechanisms driving a risk and the factors that amplify exposure.
Structured, cited reports at analyst depth for underwriting and due diligence.
Traces decision outcomes to surface proven mitigators from precedent.
Traditional tools treat risks in isolation. But risk has an underlying structure: risks interact, amplify each other, and cascade across data sources, industries, and time in patterns that are invisible when looking at any single risk type.
Social media concern about endocrine disruptors triggers research funding, which fuels litigation years later. Brand sentiment shifts on design forums precede mass product migration, visible in search trends before quarterly earnings.
RiskWise surfaces, quantifies, and predicts these patterns by continuously monitoring the full signal landscape and tracing causal chains across data types.
RiskWise operates a closed-loop evaluation flywheel where every production output is measured, every model is benchmarked, and every feedback signal improves the next cycle. This is a production system that compounds in accuracy with every signal it processes.
RiskWise deploys purpose-built AI agents, each trained on internally curated risk research data and validated against the continuous evaluation framework. These agents reason over evidence, cite it, and deliver structured intelligence at analyst-level quality.
Validates search coverage across heterogeneous data sources before downstream processing, ensuring every analysis starts from the most relevant data.
Surfaces the factors that create and amplify risk through iterative reasoning and refinement. Genuine decomposition, not keyword matching.
Generates structured, cited, actionable research reports on evolving risks with the depth of a senior analyst.
Analyzes outcomes across our data, then surfaces potential mitigators by tracing how historical decisions and evidence led to specific results.
These capabilities are actively in development and will extend the intelligence pipeline with deeper graph-based reasoning, propagation analysis, and native interoperability for agent-driven workflows.