Answers
Why does ChatGPT give wrong answers about my company data?
Because the model has access to your data but no agreement about what it means. It does not know which records refer to the same customer, which definition of a metric your company uses, whether the number is current, or what was said on the calls. Lacking those, it infers them per question — so the answer comes back confident, specific, and sometimes wrong.
The failure is confident, not blank
A model that cannot answer rarely says so. It produces a number with a plausible explanation, because producing plausible output is what it is built to do. Nothing in the response distinguishes a figure derived from a verified join from one assembled out of whichever rows were retrieved.
The practical test is repetition. Ask the same question twice, worded differently, in two sessions. If the numbers differ, the model was reconstructing the answer rather than looking it up — and a single run would have given you no way to know.
Four reasons the answer comes out wrong
No resolved identity. The same customer exists as different records in the CRM, the billing system, and the support tool, with no key that works across all three. The model guesses the join by name or domain, which silently drops subsidiaries, renamed accounts, and anything with a typo.
No agreed definitions. If “active customer” means one thing to sales and another to finance, the model picks one per query. This is the most common source of two colleagues getting two different totals from the same question.
Stale inputs. A nightly batch or a manual CSV export means the model answers as of a point in the past, with nothing in the output to indicate when. Questions about the current quarter are the ones most likely to be asked and most likely to be stale.
Unreadable conversations. The reasons behind the numbers live in calls, tickets, and emails. If those are not joined to accounts, the model answers “why” questions from structured fields that were never designed to explain anything.
Connecting it straight to your systems doesn't fix it
Giving the assistant a direct connection over MCP addresses two of the four. Reach improves, because connectors get to systems that were previously out of scope, and freshness improves, because a live query beats a batch.
The other two are untouched. MCP has no concept of a resolved account key and no shared glossary, so identity and definitions are still inferred per query. That is why teams often report that connecting more systems made answers faster and more specific without making them more consistent.
Working out which one you have
The four fail differently and are worth separating before buying anything. If totals shift between sessions, the cause is identity or definitions. If totals are stable but wrong against the source of truth, look at freshness. If the numbers hold up but “why” questions produce generic answers, the unstructured record is missing.
The assessment at /ai-accuracy-score walks through this against your own stack and shows which business questions your systems can answer, which they can answer but not verify, and which are guesswork today.
What actually fixes it
All four are input problems, so they are fixed in the layer between your systems and your AI rather than in the model or the prompt. That means one verified account key, one enforced definition per metric, monitored pipelines with freshness visible, and conversations joined back to accounts.
Riley maintains that layer and exposes it to the assistants your teams already use, so answers come with the records behind them and stop changing between sessions.
Related questions
Is this a hallucination problem?
Not in the usual sense. The model is generally reading real records; the error is in which records it decided belong together and what it assumed a term meant. Grounding fixes it; guardrails against invented facts do not.
Would fine-tuning a model on our data fix it?
No. Fine-tuning adjusts style and task behaviour, and bakes in a snapshot that goes stale. Business questions need current data and verifiable sources, which is a retrieval and governance problem rather than a training one.