Turn access to data into business understanding.

Data access answers “where can the model get information?” Business context answers “what does that information mean?” A practical integration needs both, alongside access controls and a way to inspect results. For structured business questions, the relationships between records and the definitions of measures are central to the answer.

Consider “Did we meet our delivery target in June?” First establish the time period, target and definition of on-time delivery. Then identify completed shipments and compare promised with actual dates. Calculate the result and expose the supporting records. If the question becomes “Why did we miss the target?”, investigating specific late orders requires additional evidence; the percentage alone does not establish a cause.

Talamys focuses on the business context connecting your data to AI answers. The website illustrates how shared definitions, an enterprise map and evidence can support questions across commerce, fulfilment and finance. A demo starts from your business question so the required data domain and expected evidence can be discussed concretely.

Use a small set of questions with known answers. Check the selected records, metric definitions, excluded cases and behavior when information is missing. Include questions that span multiple domains and cases where an answer should remain uncertain. Confirm supported data sources, deployment requirements and access-control behavior during technical evaluation rather than assuming them from a generic integration claim.

Delivery performance, invoice payment status and the investigation of a late order are examples shown on this website. Pick one relevant question, define the expected answer and identify the records needed to substantiate it. The demonstration uses fictional data; a discussion of your own use case is the next step toward determining product fit.