DeepSeek Has Your Facts Wrong: What You Can Do

Every AI answer about your brand comes from one of two places. The first is what the model learned during training, frozen at a knowledge cutoff. The second is pages fetched live for that question, if the product searched the web at all.

That split matters because the two errors have different fixes:

• Training-data errors. The model learned an old price, a former CEO, or a product you retired. Nothing you publish today reaches these answers until the vendor trains and ships a newer model.
• Retrieval errors. The model fetched a page that says the wrong thing, or couldn't fetch the page that says the right thing. These are the errors you can work on.
• Fabrication. The model fills a gap with a confident guess, such as an invented headcount or a feature you never had.

If you're not sure which one you're looking at, our guide to telling a hallucination from an outdated answer walks through the signs. The short version: if the DeepSeek answer shows source links, it drew on retrieved pages. If it shows none, assume it came from training.

As of October 2026, the evidence base behind this library holds no DeepSeek documentation on three things site owners usually look for:

• A correction or feedback channel for brand facts. We found none documented.
• A named crawler user agent with a stated purpose, so there is no DeepSeek robots.txt token we can tell you to check.
• A published knowledge cutoff for the models behind its consumer app.

That absence is the finding. Compare OpenAI, which documents separate bots and says of them: "Each setting is independent of the others." There, GPTBot governs training use and OAI-SearchBot governs ChatGPT search. With that documentation, a site owner can check a specific control. With DeepSeek, you can't, so treat any guide that names a DeepSeek-specific lever as unverified.

What this means in practice: don't spend time hunting for a form or an inbox. The same is true of most engines, as our answer on whether there is an official way to submit brand facts explains. Put the time into the evidence DeepSeek and every other engine can read.

You need a baseline, or you won't know whether anything you did made a difference.

1. Write down the exact question that produced the wrong answer, word for word.
2. Ask it again in a fresh session, a few times. Answers vary between runs, so one bad answer is a sample, not a verdict.
3. Note whether the answer shows sources. If it does, open each one and find the sentence carrying the wrong fact.
4. Classify the error: outdated, fabricated, or copied from a third-party page.
5. Save the date and the full answer text.

Say you run a hypothetical project-management tool that dropped its free tier last year, and DeepSeek still offers it to users. If no sources appear, that's almost certainly training data, and the fix is slow. If a review site appears as the source, the fix starts with that review site.

This is the part fully in your control, and it helps on every engine, not just DeepSeek.

State each fact once, specifically

The one positive experimental signal for on-page content is a specific, quotable fact on a page that can be fetched: a price, a number, a one-line definition. "Plans start at $29 a month" gives an engine a sentence to quote. "Affordable plans for every team" gives it nothing.

Remove contradictions on your own site

Check that the same fact reads the same way on your pricing page, about page, footer, press page and any structured data. A contradiction inside your own site is a plausible source of a wrong answer, and it's entirely yours to fix. The same logic applies when ChatGPT gets your pricing wrong.

Put the facts in the raw HTML

Vercel's crawler study found that ChatGPT and Claude crawlers fetch JavaScript files (11.50% and 23.84% of requests) but don't execute them, so they can't read client-side rendered content. If your prices or product names appear only after JavaScript runs, a crawler that doesn't render sees an empty page. Server-render the facts that matter.

Keep crawlers able to reach you

A firewall or CDN can block an AI crawler that robots.txt allows. Check what your edge returns to bots, not just what robots.txt says.

When engines cite other sites instead of yours, being named correctly on those sites is the route into the answer. The evidence shows a strong association here, not a guaranteed effect, so treat it as reducing the number of wrong sources rather than as a switch.

Start with the pages that showed up as sources in your recorded answers. Then work outward:

• Directory and comparison listings with stale pricing or features.
• Old press coverage describing a product you've retired.
• Partner and integration pages using your former name.

Our playbook on asking third-party sites to fix wrong facts covers how to make those requests. For facts that changed after a model was trained, read correcting outdated facts past a knowledge cutoff. Be honest about timing: training-era errors fade only as newer models replace older ones.

Some shortcuts make things worse.

• No hidden text or prompt injection. Research on LLM search agents shows manipulated content can change answers once it's in the result set. It's illegitimate, it can be found, and it puts your reputation at risk. Never plant instructions for AI crawlers.
• No mass rewriting for AI. A July 2026 critical survey of 45 GEO studies found no technique with a stable, longitudinal, cross-platform causal effect on discoverability. It also warns that citation-oriented rewrites can impair retrieval.
• No promises from vendors. Nobody outside DeepSeek controls what its model says. Anyone guaranteeing a corrected answer by a date is guessing.

Once your own facts are consistent, see how other engines describe you. The free checker on this site shows a snapshot for up to three buyer questions. The same diagnosis applies when Claude gets your company wrong.