System 03 / financial intelligence
Models that support financial judgment.
We apply machine learning and language models to financial data, documents, and research workflows—with evaluation, human review, and monitoring treated as system components.
Illustrative workflow. Models do not independently guarantee decisions or outcomes.
- 01 Documents
- 02 Market events
- 03 Research retrieval
- 04 Model monitoring
Capabilities / applied financial AI
Narrow tasks. Visible evaluation.
Financial intelligence is most useful when the task, source material, acceptable error, and review path are explicit. We focus on bounded workflows rather than general claims of autonomy.
- Document intelligence
- Extraction, classification, summarization, and structured review workflows for financial reports, filings, research, and operational documents.
- Market-event analysis
- Systems that organize news, announcements, and time-aligned market context into testable labels or research inputs without presenting model output as fact.
- Research knowledge systems
- Retrieval and question-answering interfaces over controlled financial source collections, with document references and permission-aware access patterns.
- Evaluation and monitoring
- Test sets, error analysis, drift indicators, review queues, and operational telemetry designed around the model's actual financial task.
Model control / 04 stages
Evaluation belongs inside the architecture.
A model response is an intermediate system event. It needs source context, a measurable task, an escalation path, and a record that can be inspected later.
Bound the task
Define intended users, source material, action boundary, and unacceptable outcomes.
Measure the errors
Build representative tests and examine failure classes, not only average scores.
Place human control
Route consequential or uncertain outputs to an explicit approval or correction step.
Watch the system
Track input shifts, model behavior, feedback, latency, and provider changes.
Machine-learning and language-model outputs can be incomplete, outdated, or incorrect. They require task-specific evaluation and should not be treated as investment advice or guaranteed market predictions.
System controls / before deployment
Design for correction, not perfection.
Reliable operation begins by assuming the model can fail and providing ways to detect, contain, review, and correct that failure.
- 01Source groundingPreserve the material used to produce a response and expose references where the task allows.
- 02Uncertainty handlingDefine abstention, review, and fallback behavior for incomplete or ambiguous inputs.
- 03Change controlVersion prompts, models, providers, test sets, and evaluation criteria.
- 04Data boundaryLimit what is collected, sent to providers, retained, and exposed to each user.
Project intake / financial intelligence
Start with the decision workflow.
Describe the source data, user, review point, and error that the system must be designed to handle.