Answers You Can Trust
Any AI can write a confident paragraph about your numbers. The hard part — the part that decides whether you can act on the answer — is knowing when to trust it. That is what PlaidCloud is built for.
When you ask a connected AI assistant to explain your data, PlaidCloud doesn’t just hand back a figure. It grades how much to trust that figure, tells you in plain language where the ground is soft, reconciles its own arithmetic, and refuses to make anything up. This is the difference between an assistant that sounds right and one you can put in front of a CFO.
Every Answer Carries a Confidence Signal
Section titled “Every Answer Carries a Confidence Signal”PlaidCloud rates its own answers — High, Medium, or Low confidence — and says why. A clean, fully-attributed result comes back as High. If the two periods you’re comparing aren’t equally complete, or part of a change can’t be pinned to a single cause, PlaidCloud says so and lowers its own confidence rather than presenting a shaky number as a certain one.
You never have to wonder whether the assistant is sure. It tells you, up front, in the answer.
It Tells You When to Be Careful
Section titled “It Tells You When to Be Careful”Instead of burying assumptions, PlaidCloud surfaces them as plain-language heads-up notes attached to the answer:
| When an answer flags… | It means… |
|---|---|
| “This describes the whole pool” | The figure is a total; to see how it shifts between members (regions, products, cost centers), ask about a specific one. It appears only where the answer shows you no member breakdown — where one is shown, that note is not true of it and is not printed. Where the note names an earlier step in the chain — it opens “Stage 2 (…)” — narrowing your question won’t reach that step, so trace its own table instead. |
| “These periods aren’t equally complete” | One period may be a partial month or a short window, so part of the movement could be missing data rather than a real change. The row counts it quotes are for the whole result — on a table that recombines several allocation branches, they cover every branch together. |
| “The pieces don’t fully reconcile” | The detailed breakdown doesn’t perfectly sum to the headline number — treat the split as indicative, not exact. |
| “The precise cause is partial” | The totals are correct, but the exact driver of the change can’t be fully attributed from the data on hand. |
| “Some targets couldn’t be measured” | On a what-if, the calculation for one step failed, so the results it writes carry no figure at all rather than a guess. The totals cover what was measured, and anything downstream of that step is left out rather than estimated from it. |
| “Estimated on today’s shares” | A what-if estimate splits the change across the affected results by each one’s current share of the pool — so the figures add up — and reflects how your model is configured today, not a precise forecast of a future in which the shares may have moved. |
These aren’t fine print. They’re the safeguards that keep a confident-sounding answer from quietly overstating what the data actually supports.
A note that describes only part of the result says which part. A note can be raised by one part of a result rather than by all of it, and its figures then belong to that part alone — so it opens by naming it. On a result that combines several allocation branches that reads “Branch 4 (admin costs): …”; on one allocating several value columns at once it names the column, “Column margin: …”. Read an unprefixed note as being about the result you asked for, and a prefixed one as being about that part of it.
Where it applies to more than one part, it names them all — “Columns revenue, margin: …”. So a column missing from that list is a column the note does not apply to, which is the point of listing them: on a three-column result where two share a note, naming one of them would leave the other reading as exempt. A note raised by every part carries no prefix at all, since there is nothing to distinguish.
One case reads differently, and deliberately. Where the parts each raised the note with their own figures, the note shown is one part’s, and quoting several names in front of one part’s numbers would misattribute them — so it names that part and lists the others after it: “Column revenue (also raised by margin): …”. The figures in that sentence are revenue’s; margin raised the same kind of note with figures of its own.
It Checks Its Own Math
Section titled “It Checks Its Own Math”When PlaidCloud explains why a number moved, it doesn’t stop at the first plausible story. It reconciles the parts back against the whole and, if they don’t line up, it says so and dials back its confidence — so a subtle gap in the data shows up as a caveat, never as false precision.
It Never Invents a Number
Section titled “It Never Invents a Number”Every figure comes from a real query against your data. If a question needs data you don’t have access to, or the data simply isn’t there, PlaidCloud tells you plainly instead of guessing. PlaidCloud invents nothing: no hallucinated totals, no invented account names, no made-up trends.
The Written Summary Is Checked, Not Just Written
Section titled “The Written Summary Is Checked, Not Just Written”When an assistant turns an analysis into a readable paragraph, there’s a quiet risk: the prose drops a caveat, or rounds a figure into something the data never said. PlaidCloud closes that gap. Alongside the structured result it can return a plain-language summary built directly from the analysis — one that states the confidence level, carries every caveat, and contains no figure that didn’t come from a real query against your data.
It comes with a companion faithfulness check your assistant can run on its own reworded version: did it keep the confidence level, keep every caveat, and avoid inventing a figure? If the rewrite drifts, the check catches it. The result is a narrative that’s provably faithful to the numbers underneath — not merely fluent.
It Suggests the Next Question
Section titled “It Suggests the Next Question”A good analyst doesn’t just answer — they tell you what to ask next. Each summary suggests the natural follow-up, drawn from what the analysis actually found: scope to a single member when the figure is a whole-pool total, break the change down by a dimension when one looks like it’s driving it, or point at a specific step when a result table is built by more than one. You can act on the suggestion without knowing the exact wording — just ask for it.
Where the analysis found several candidates it won’t guess between — two steps that both build a table, or several fields too close to separate as the explanation for a change — it offers each of them as a separate suggestion rather than asking you to pick without saying what there is to pick from. Suggestions you can act on exactly as written come first.
A search that found nothing is still worth telling you about. Where you don’t name a field to break the change down by, the assistant looks for the one that best explains it — and where nothing it tried explained the change well enough to report, it says so rather than going quiet: the change reads as a broad move across the whole pool, and here is the field that came closest, naming its largest member and the share of the change that member accounts for. The member is named because this is the one finding the answer is unsure of, so it is the one you most need to be able to check — and it is what makes the accompanying warning, that a follow-up may lead with a different member, something you can test. That field is offered as a suggestion you can run as written, described as the strongest of a weak field rather than as a close call, because the search judged it non-explanatory. Confidence in the figures is not reduced for it — nothing was chosen, so there is no guess to discount.
These last two are different outcomes, and the wording keeps them apart: several fields scoring equally well gets you one suggestion each, while none scoring well enough gets you a single closest-thing offered as exactly that.
On a result with several value columns, the note names the column it searched. Those results — cost, revenue and margin side by side, say — are searched one column at a time, while the answer itself leads with the summary column. A “nothing found” result therefore says which column’s change it covers, rather than reading as a verdict on the whole result. Read it for what it names: another column of the same result may well have a field that explains it, and asking for that breakdown directly will tell you.
Two percentages that look alike are told apart. When the assistant picks the field that best explains a change, the figure it reports is that field’s shift in share of the pool. When you break the change down by a field yourself, the figure is each member’s own change. These are different measurements over different totals, and both are correct — so each says which one it is rather than both reading as a plain percentage of the change. It matters most on the suggestion itself: taking the recommended breakdown will give you a different number for the same member, and the suggestion says so before you run it.
That holds for the closest-candidate note above as well, where the search settled on nothing: the share it quotes for the field that came closest is a shift in share too, so a breakdown by that field will report a larger number for the same member. Being below the bar is a statement about the search, not a ceiling on what the breakdown will show.
Why This Is Different
Section titled “Why This Is Different”Most AI analytics tools are confident whether or not they’re right. A generic chatbot bolted onto a dashboard will produce a fluent, authoritative-sounding answer — and give you no way to tell a solid one from a wrong one. To that kind of tool, every number is just a number.
PlaidCloud is built the other way around. Confidence grading, self-reconciliation, and honest caveats are part of every answer, because an answer you can’t trust isn’t worth having. That honesty is the whole point: it’s what lets you take an AI-generated explanation and actually use it — in a board deck, a forecast, a decision — without re-checking it by hand.
Works With the AI You Already Use
Section titled “Works With the AI You Already Use”You don’t adopt a new tool to get this. PlaidCloud’s honest analysis comes through whichever assistant your team already lives in:
Related
Section titled “Related”- Tracing Allocations — see honest confidence and caveats at work on a real allocation question
- Analysis Paths — name your key tables so you can just ask “why did this change?”
- Microsoft 365 Copilot — bring honest AI analysis to your whole team