If this page has been found, a familiar predicament is likely at hand: decisions must be made under uncertainty, and the solitary model—or solitary expert—will not suffice. What is needed is a disciplined way to surface assumptions, test them, and let human judgment and machine inference meet without confusion.

AnAssumption builds AI tools grounded in scientific method: state the hypothesis, formalise it, confront it with data, revise. The instruments are modelling and simulation to expose structure, language models to extract and score evidence from text, and decision procedures that report uncertainty, sensitivity, and counterfactuals. The aim is complementarity—machines to widen perception and shorten feedback loops; humans to set aims and accept responsibility.

Work focuses on macroeconomics, investment research, and organisational decisions where ambiguity is chronic. Multi-agent simulations generate scenarios and stress tests; domain-adapted language models read filings, policy, and research; calibration and out-of-sample checks keep results inside reliability bounds. Assumptions are explicit and measurable, so revision becomes a feature, not a failure.

Integration is pragmatic: map the firm’s information flow, remove bottlenecks, and embed services behind clean interfaces with data lineage and audit trails. Tutorials and consulting translate method into practice—model design, uncertainty measurement, retrieval and prompting without superstition, evaluation that resists wishful thinking—so teams operate scientifically rather than by folklore. A final remark: any single perspective is incomplete. an assumption assembles multiple, testable views and places them before responsible humans at the moment of choice. If clarity emerges, it will be by construction—explicit assumptions, measured uncertainty, and complementary strengths that add rather than cancel.