In most finance teams the analyst is a bottleneck by accident. Every “quick question” — what drove the variance, how much runway is left, which cohort is actually profitable — turns into a ticket, a spreadsheet pull, and a half-day wait. The question was simple. Getting the data was not.
Ask the ledger directly
iDBQuery lets a finance lead ask in plain language and get the answer straight from the ledger and the operational systems behind it. “Show me operating expense variance by department this quarter versus budget” becomes a real query, run against the real numbers, returned with the breakdown — no SQL, no waiting on the one person who knows the schema.
Because the answer is a query and not a guess, it comes with the rows it touched. When the CFO asks “where did this come from?”, the answer is one click away: the exact transactions, accounts and periods that rolled up into the figure.
Reporting that maintains itself
The same engine drives live dashboards and scheduled reports. A monthly pack that someone used to rebuild by hand becomes a saved view that refreshes against current data, with every line still traceable to source. The close gets shorter not because anyone worked faster, but because the data stopped being a manual scavenger hunt.
The point is not to replace the analyst. It is to free them from fetching numbers so they can do the judgement work — interpreting the variance — that the business actually needs them for.
Keep reading
Why cited answers beat a black-box chatbot
A confident number you cannot verify is worse than no number at all. We made “show your working” a hard guarantee — every figure traces to the exact source row it came from.
FEDERATIONOne live model vs. building a data warehouse
A warehouse is months of pipelines, schema design and a copy of your data that is stale the moment it lands. We connect your sources into one live, queryable model — no ETL, no second copy.
PLATFORMThe semantic layer: understanding a database it has never seen
Real schemas are cryptic — tables called tbl_acct_2, columns named flg and amt3. The semantic layer learns what they mean, so plain-language questions hit the right rows the first time.