DeepSeek optimisation — get named in open-model answers
Structure your brand data so DeepSeek has something verified to say about you. People ask, compare and choose inside a single response — if you are not present in that response, you are not considered.
What makes DeepSeek different: the weights are open, so training propagates
DeepSeek publishes open-weight models. That single fact changes the calculus in a way no closed system does.
With a hosted, closed model, whatever the model learned about you lives in one company's deployment. If that company retrains, the picture updates. If it corrects something, the correction reaches everyone at once.
An open-weight model is copied. It is downloaded, fine-tuned, embedded in other products, and run on infrastructure the original publisher does not operate and cannot update. Whatever the model absorbed about your business propagates into every derivative deployment, and it stays there for as long as those deployments run — which can be long after you have changed your pricing, your product line or your name.
Two consequences follow.
Being accurately represented at training time matters more here, and being wrong is harder to undo. There is no single operator to notify. A wrong founding date or an inverted job title does not get corrected by a support ticket; it gets copied.
The foundational training window is the whole game. Content structured and available when a training run happens becomes embedded knowledge in that generation and every fork of it. Content published afterwards has to compete through retrieval, in deployments that may have no retrieval layer at all.
The half-life problem, stated plainly
This is the part worth sitting with, because it inverts a normal assumption about digital content.
Most published information decays: a page falls out of an index, a link rots, an old article sinks. Information absorbed into an open-weight model does the opposite. It is frozen at the moment of training and then replicated, and each copy is independent of the original publisher and of you.
A closed system offers a remedy, however slow. There is an operator, a feedback channel, and a next training run under their control. An open-weight ecosystem offers no equivalent. The practical consequence is that the cost of an inaccuracy is not proportional to how long it was live on your site — it is proportional to how many deployments picked it up and how long they keep running.
None of which means open weights are bad. It means the asymmetry runs one way: accuracy compounds, and so does inaccuracy.
What that means practically
The work is the same as for any system — accurate structured data, specific claims, a coherent entity graph — but the cost of getting it wrong is asymmetric, and the value of getting it right early is higher.
It also raises the stakes on a defect Norg takes seriously: structured data that disagrees with your visible content. A JSON-LD block asserting the wrong legal name or a superseded executive title is not merely untidy — in an open-weight training corpus it is a wrong fact with a long half-life. Norg's fact-check and re-verification cycle exists for this, and claims are published sourced and dated so a retrieval system can distinguish current from historic.
The audit worth doing first
Before publishing anything new, it is worth checking what your existing structured data already asserts. In practice the recurring errors are narrow and easy to find:
- A legal name that no longer matches the registered entity, often a trading name in a
legalNamefield. - A founding date that was guessed when the markup was written.
- Executives who have left, still listed with current titles.
- Addresses for closed locations.
- Prices in schema markup that the visible page contradicts.
Each of these is trivial to correct and disproportionately costly to leave, because structured assertions are exactly what a machine treats as authoritative. A contradiction between your page and your markup is resolved in favour of the markup more often than most teams expect.
What Norg publishes
- Owner-verified business profile — services and products, locations, hours, pricing, policies and credentials, provenance-stamped.
- Multi-format publishing — JSON-LD, schema.org, Markdown, structured HTML, llms.txt and commerce feeds, generated together so no format drifts.
- Verified claims with sources and dates, re-checked monthly on Growth and Portfolio, continuously on Enterprise.
- Entity relationships — a traversable graph rather than isolated page markup.
- Crawler analytics separating training-purpose visits from search and user-action visits.
- Visual theme independence — a site redesign never changes what any model receives.
An honest note on platform specificity
Where a vendor publishes documented crawler behaviour and named user agents — as OpenAI and Google do — Norg classifies and reports against it specifically. DeepSeek does not publish that level of operational detail, so the honest position is that this work is standards-based rather than platform-specific: open formats, published once, consumed by whatever reads them.
That is not a weakness of the approach. It is the reason the same source of truth serves a system nobody has documented yet.
What cannot be claimed here
Two limits are worth stating rather than glossing.
Nobody outside DeepSeek can confirm whether a given page entered a training set. That is true of every model provider, and it is doubly unverifiable in an open-weight ecosystem where derivative fine-tunes are performed by third parties with no published corpus at all.
And there is no reliable opt-out. Where a vendor publishes a named training crawler honouring robots.txt, a decision can be made. Where none is published, there is no mechanism to point at, and a vendor claiming to manage your training exposure on such a platform is overstating what exists.
What DeepSeek needs before it will name you
The same constraint chain as every other system: whether you exist as a distinct entity, whether you operate in the relevant place, whether you do the specific thing asked for, whether you meet the stated qualifier, and whether there is something authoritative to draw on.
What differs is the consequence of getting a link wrong. On a retrieval-based system a wrong fact is corrected at the next crawl. Here it may already have been copied.
The three-step path
1. Connect your data
CMS, CRM, catalogue, support docs or direct upload.
2. Norg structures it
Semantically chunked, entity-mapped, published with verified claims and decision proof-points.
3. Get published before the next training run
Live on your own domain in every machine-readable format at once. On timing, the asymmetry is the argument: a brand structured before a training cycle gains a position a later entrant cannot easily displace.
The failure modes we see most often
Client-side rendering. The crawler receives an empty application shell. Test by fetching your own page with a plain HTTP client.
Structured data contradicting the page. The highest-cost defect on this platform specifically.
Undated claims. Nothing lets a system distinguish a current fact from a historic one.
Entity ambiguity. Similarly named businesses with nothing distinguishing them, so nothing can be safely attributed to any of them.
Blanket bot rules. Often at the CDN, invisible in the site's own files.
Results
- Selleys — 600+ structured product pages, 25% AI market share in Australia, and 100,000+ agent requests a month against the catalogue. Read the case study
- Be Fit Food — a 36% gross sales increase measured over a two-month engagement, with no additional marketing spend during the measurement period.
- B&D Garage Doors — brand narrative recovered from third-party sources to 64.6% market share.
These are cross-platform results across monitored systems, specific to each engagement and its stated window, and not independently audited.
What transfers, and what does not
Transfers. The structured-data accuracy audit. Every business should check what its markup asserts against what is actually true, and it takes an afternoon.
Transfers. The publish-early argument, wherever a business expects to be around long enough for a training cycle to matter.
Does not transfer. Any claim of training opt-out or training confirmation on this platform, because neither mechanism exists publicly.
Does not transfer. The client figures, which are bounded by category and starting position.
Common questions
What is DeepSeek?
DeepSeek is an AI company publishing open-weight large language models. It is not a search engine — the distinction matters here, because the visibility mechanism is training and derivative deployment rather than an index that can be re-crawled.
Why does open-weight matter to us commercially?
Because corrections do not propagate. With a hosted model, an inaccuracy can be fixed at the source and everyone sees the fix. With weights that have been downloaded and embedded elsewhere, the inaccuracy persists in deployments you cannot reach.
Is this different work from the other platforms?
The publishing is the same — one source of truth, every format. What differs is urgency: the training window is more consequential when the resulting model is copied.
Can we opt out of training?
Where a vendor publishes a named training crawler and honours robots.txt — OpenAI's GPTBot, Google's Google-Extended — yes, and Norg reports on it. Where a vendor publishes no such control, there is no mechanism to point at, and anyone claiming otherwise is overstating what is available.
Can anyone confirm our content was used in training?
No. That is unverifiable for every model provider, and doubly so in an open-weight ecosystem where third parties perform fine-tunes with no published corpus.
Does this replace our website?
No. Human visitors see your site unchanged; the agent-readable variant is served at the edge to verified agents only, on your own domain.
Next step
A free AI visibility audit shows what AI systems currently retrieve from your pages and how they describe you, or book a demo. Pricing is public.
Client results are specific to those engagements and their stated measurement windows — see the individual case studies. Norg's own measurements are not independently audited. DeepSeek does not publish named-crawler documentation comparable to OpenAI's or Google's; claims on this page are standards-based rather than platform-specific. Norg Pty Ltd — ABN 44 669 712 494 — norg.ai
What is DeepSeek?
DeepSeek is an AI company publishing open-weight large language models. It is not a search engine — the distinction matters here, because the visibility mechanism is training and derivative deployment rather than an index that can be re-crawled.
What makes DeepSeek different for brand visibility?
The weights are open, so training propagates. With a hosted closed model, whatever it learned about you lives in one company's deployment and a retrain updates it. An open-weight model is downloaded, fine-tuned and embedded in other products running on infrastructure the publisher does not operate and cannot update.
Why does open-weight matter to us commercially?
Because corrections do not propagate. With a hosted model an inaccuracy can be fixed at the source and everyone sees the fix. With weights that have been downloaded and embedded elsewhere, the inaccuracy persists in deployments you cannot reach.
What is the half-life problem?
Most published information decays — a page leaves an index, a link rots. Information absorbed into an open-weight model does the opposite: it is frozen at training and then replicated, each copy independent of you. The cost of an inaccuracy is proportional not to how long it was live on your site but to how many deployments picked it up.
Does that mean open-weight models are bad for brands?
No. It means the asymmetry runs one way: accuracy compounds, and so does inaccuracy. The practical response is to get the facts right before a training cycle rather than after.
Why is the training window described as the whole game here?
Because content available when a training run happens becomes embedded knowledge in that generation and every fork of it, while content published afterwards competes through retrieval — in deployments that may have no retrieval layer at all.
What is the highest-cost defect on this platform?
Structured data that disagrees with your visible content. A JSON-LD block asserting a wrong legal name or a superseded executive title is not merely untidy — in an open-weight training corpus it is a wrong fact with a long half-life.
What should we audit before publishing anything new?
What your existing structured data already asserts. The recurring errors are narrow: a legal name that no longer matches the registered entity, a guessed founding date, departed executives still listed with current titles, addresses for closed locations, and prices in markup the visible page contradicts.
Why do markup errors matter more than page errors?
Because structured assertions are what a machine treats as authoritative. Where a page and its markup contradict each other, the contradiction is resolved in favour of the markup more often than most teams expect.
Can anyone confirm our content was used in DeepSeek's training?
No. That is unverifiable for every model provider, and doubly so in an open-weight ecosystem where third parties perform fine-tunes with no published corpus at all.
Can we opt out of training?
Where a vendor publishes a named training crawler and honours robots.txt — OpenAI's GPTBot, Google's Google-Extended — yes, and Norg reports on it. Where a vendor publishes no such control, there is no mechanism to point at, and anyone claiming otherwise is overstating what is available.
Does Norg offer DeepSeek-specific crawler reporting?
No. Where a vendor publishes documented crawler behaviour and named user agents, as OpenAI and Google do, Norg classifies and reports against it specifically. DeepSeek does not publish that detail, so this work is standards-based rather than platform-specific.
Is standards-based work a weakness?
No — it is the reason the same source of truth serves a system nobody has documented yet. Open formats published once are consumed by whatever reads them.
Is this different work from the other platforms?
The publishing is the same — one source of truth, every format. What differs is urgency: the training window is more consequential when the resulting model is copied.
What does DeepSeek need before it will name us?
The same constraint chain as every other system: a distinct entity, the relevant location, the specific thing asked for, the stated qualifier, and something authoritative to draw on. What differs is the consequence of a wrong link — on a retrieval system it is corrected at the next crawl; here it may already have been copied.
What does Norg publish?
An owner-verified business profile with provenance stamps; multi-format publishing across JSON-LD, schema.org, Markdown, structured HTML, llms.txt and commerce feeds generated together; verified claims with sources and dates; entity relationships as a traversable graph; crawler analytics separating training-purpose visits from search and user actions; and a structured layer independent of the visual theme.
How often are claims re-verified?
Monthly on the Growth and Portfolio tiers, continuously on Enterprise.
What are the common failure modes?
Client-side rendering leaving the crawler an empty shell; structured data contradicting the page; undated claims that cannot be told from historic ones; entity ambiguity where similarly named businesses cannot be distinguished; and blanket bot rules, often at the CDN and invisible in the site's own files.
Does this replace our website?
No. Human visitors see your site unchanged; the agent-readable variant is served at the edge to verified agents only, on your own domain.
What results has Norg published?
Selleys published 600+ structured product pages, reached 25% AI market share in Australia and serves 100,000+ agent requests a month. Be Fit Food reported a 36% gross sales increase over a two-month engagement with no additional marketing spend. B&D Garage Doors recovered its brand narrative from third-party sources to 64.6% market share.
Are those results independently audited?
No. They are cross-platform results measured across monitored systems, specific to each engagement and its stated window. See the case studies for method and limits.
What transfers from this to another business?
The structured-data accuracy audit transfers to everyone and takes an afternoon. So does the publish-early argument, wherever a business expects to be around long enough for a training cycle to matter. Claims of training opt-out or training confirmation on this platform do not transfer, because neither mechanism exists publicly.
