AI Methodology

At Radiant ML, artificial intelligence is not a marketing buzzword — it is the analytical engine behind every engagement. But we are equally clear about what AI does and does not do. Our models generate insights; our advisors generate judgment. No client-facing deliverable reaches you without human review.

The Radiant Pipeline

1. 01 — Data Ingestion

   Portfolio holdings, market prices, macroeconomic indicators, and fundamental data are ingested from licensed third-party providers and normalized into our data warehouse.

2. 02 — Model Processing

   Machine learning models compute factor exposures, risk metrics, correlation structures, and scenario outcomes. Models include gradient-boosted regressors for return attribution and neural networks for regime classification.

3. 03 — Advisor Review

   A qualified advisor examines all model outputs, flags anomalies, adds qualitative context, and ensures recommendations align with the client's stated objectives and constraints.

4. 04 — Client Delivery

   Final deliverables — reports, dashboards, and advisory memos — are published to the client portal with full methodology documentation and data lineage.

Model Governance

• All models are version-controlled with documented training data, hyperparameters, and validation metrics.
• Out-of-sample testing is required before any model enters production.
• Models are retrained on a quarterly schedule or when performance degradation is detected.
• Clients may request methodology briefs for any model used in their engagement.

RadiantAI Assistant

Our client-facing AI assistant (RadiantAI) is built on a large language model fine-tuned for financial Q&A. It can summarize portfolio reports, explain factor exposures in plain language, and answer questions about market events — but it does not make autonomous investment recommendations. All RadiantAI responses include source citations to underlying data.