You review the definitions.
Opscien proposes descriptions and cleaning steps for your tables. You can correct them before approval. The reviewed table version is then published for analysis.
Review what your data means. Inspect the figures behind an answer. Keep decisions with the people responsible for them. These are controls built into how Opscien works.
Explore the demoMore visitors do not always mean proportionally more purchases. Here is how an evidence receipt could explain a retail investigation, from its figures to its limits.

Up 15.1% across two matching 14-day periods in September 2026.
What did the answer use?
84 daily records across three stores and 28 days. All Home widgets, chat answers, the application and report share the same records.
| Store | 1–14 Sep | 15–28 Sep |
|---|---|---|
| Store X | 4,096 | 4,432 |
| Store Y | 5,216 | 6,616 |
| Store Z | 3,256 | 3,424 |
| Total | 12,568 | 14,472 |
Are we measuring the same thing?
Compare 1–14 September with 15–28 September 2026: 14 days in each window, with the same stores and weekday mix.
Footfall counts visits, not unique people. Conversion is total transactions divided by total visits, rather than an average of store percentages.
How was the number obtained?
The store changes add up: Store X +336, Store Y +1,400 and Store Z +168 visits produce the total increase of 1,904.
What does the evidence leave open?
The figures show where conversion fell. They do not establish whether staffing, stock availability or visitor mix caused it.
Sales exclude tax and returns. The recovery scenario holds footfall and basket constant; it is not a forecast.
What should the team check next?
Review Store Y’s hourly staffing, queues, stock availability and visitor mix. Validate the counter coverage before testing one supported intervention.
These are proposed next steps. No operational change has been made.
Opscien proposes descriptions and cleaning steps for your tables. You can correct them before approval. The reviewed table version is then published for analysis.
Analysis tools compute the figures. Before presenting an answer, Opscien checks its numbers against the results available to the run. Unsupported figures are withheld and marked.
Forecasts show uncertainty ranges when the model supports them. What-if studies need evidence for the relationships they use. An answer can be limited or refused when that evidence is missing.
Decision cards give an administrator or workspace owner those three choices. Rejection requires a reason. The decision record keeps who decided and what they chose.
The audit log records events such as analysis tool calls, definition approvals, and decision reviews. Reports have review and publishing steps, with a way to return to an earlier published version.
Requests to the AI are checked against your workspace’s spending budget before they run. A request that exceeds the available budget is refused. Only the workspace owner can raise that budget.
Our studies examine numerical reasoning, the value of extra analytical steps, and whether systems respond appropriately to missing or newly available evidence.
We publish the limits alongside the findings. A result on a controlled benchmark is not a blanket claim about the product.
Read the research