How DeepSeek Decides Which Local Businesses to Recommend

What DeepSeek Actually Is (And Why You Should Care)

Most American business owners first heard about DeepSeek in January 2025, when the Hangzhou-based AI lab released a model that matched or exceeded GPT-4-class performance while reportedly costing roughly $5.6 million to train, a fraction of what Western labs spend. The stock market reacted dramatically. Nvidia dropped $600 billion in market cap in a single day. The AI industry had a new variable to account for.

But that framing missed the more consequential story for local businesses. DeepSeek is not just a cheaper model. It is a fundamentally different type of AI assistant, open-weight (meaning the model weights are publicly released), Chinese in origin, trained on a distinct mix of data, and rapidly accumulating users who ask it questions about businesses, services, and local recommendations.

DeepSeek AI is headquartered in Hangzhou, China, and operates under the umbrella of High-Flyer, a quantitative hedge fund. The lab has released a series of models: DeepSeek-V2, V3, V3.1, V3.2, and the reasoning-focused R1 series. As of mid-2026, the primary API models are referred to as deepseek-v4-flash and deepseek-v4-pro, with older names (deepseek-chat, deepseek-reasoner) scheduled for deprecation in July 2026.

Why does any of this matter for a plumber in Pasadena or a dental practice in Denver? Because when someone types a local business query into DeepSeek, whether through the app, the website, or one of thousands of third-party integrations, the model generates an answer based on a specific set of signals. Understanding those signals is the entire game.

How Many People Are Using DeepSeek to Find Businesses

The numbers are larger than most American business owners realize. DeepSeek reached 130 million active users by the end of 2025 and its web footprint hit 350.8 million visits in March 2026 alone, per data aggregated by Backlinko and Business of Apps. The app has been downloaded 173 million times since its January 2025 launch.

The geographic distribution matters for local business optimization. China leads with approximately 35% of monthly active users. India follows as the second-largest user base at roughly 20%. The United States represents a smaller but fast-growing share, particularly among developers and cost-conscious enterprise users who discovered DeepSeek through its API pricing advantage.

A Microsoft report from January 2026 found DeepSeek commanding an estimated 56% AI market share in Belarus, 49% in Cuba, and 43% in Russia, illustrating its particular traction in markets where Western AI platforms face accessibility or cost barriers. In enterprise contexts globally, DeepSeek now ranks third by market share behind Anthropic and OpenAI in developer SDK usage.

The practical implication for businesses: DeepSeek is not a fringe tool. It is a platform that hundreds of millions of queries pass through monthly. Local business recommendation queries, while a subset of that volume, represent a real and growing traffic source that most businesses are not optimizing for at all.

The Architecture: Why DeepSeek's Recommendations Look Different

DeepSeek's V3 and R1 models use a Transformer architecture incorporating SwiGLU activations, Rotary Position Embedding (RoPE), and RMSNorm, inheriting Multi-head Latent Attention (MLA) and a Mixture-of-Experts (MoE) design from DeepSeek V2. These are not just academic details. The MoE architecture is the reason DeepSeek produces qualitatively different answers from GPT-4 class models for the same business query.

In a standard dense model like GPT-4, all parameters activate for every token. In DeepSeek's MoE design, only a specialized subset of "expert" modules activates per token. The 671-billion-parameter R1 model, for example, activates only about 37 billion parameters per inference pass. This selective activation means different routing paths for different query types.

V3 vs R1: Which Mode Handles Business Queries

DeepSeek V3 (decoder-only architecture, 685B parameters, 37B active) is the general-purpose conversational model. It handles the vast majority of user queries, including local business searches. DeepSeek R1 is the reasoning-optimized chain-of-thought model, better suited for logic-heavy tasks.

For practical business optimization, V3 is the model to understand. It draws its answers from training data for general queries and shifts to live web retrieval when the web search feature is active. The model that answers "best HVAC contractor near me" is almost certainly a V3-class model, not R1.

The Data Sources DeepSeek Pulls From

This is the section most businesses skip, and it is the most consequential. DeepSeek's training data composition determines which businesses exist in the model's learned knowledge base, and therefore which businesses it has any chance of recommending when a user asks a local query.

Per DeepSeek's own technical documentation, DeepSeek-V3-Base was trained exclusively on plain web pages and e-books, without any synthetic data incorporated into the base training corpus. The primary web data source is Common Crawl, the same large-scale web snapshot used by many foundational models, supplemented by self-collected data that reportedly respects robots.txt directives.

What Common Crawl Captures (And What It Misses)

Common Crawl snapshots publicly accessible HTML pages across the web. It is remarkably broad but structurally blind to JavaScript-rendered content. Pages that require JavaScript execution to display their core content are typically captured in a degraded or empty state. This has direct implications for local businesses:

DeepSeekBot and Live Web Retrieval

DeepSeekBot is the web crawler operated by DeepSeek AI for ongoing data collection. Unlike GPTBot (OpenAI) or ClaudeBot (Anthropic), DeepSeekBot's user agent string is less consistently documented in public sources, which makes it harder to track in server logs. The bot operates under the same principle as other AI crawlers: collecting publicly accessible content to update or supplement training and retrieval pipelines.

When a user activates DeepSeek's live web search feature, the system shifts to real-time retrieval, using a RAG-like pipeline to pull current web pages and generate grounded answers. This live retrieval mode is more transparent than the base training data approach. Businesses with well-structured, crawlable web pages benefit most from this mode because the model retrieves their content directly for the query at hand.

What DeepSeek Looks For When Recommending a Local Business

DeepSeek does not have a published "ranking algorithm" for business recommendations the way Google publishes Webmaster Guidelines. What it has is a learned behavior shaped by its training data and architecture. Through analysis of query patterns and output behavior, several consistent signals emerge that influence whether and how DeepSeek surfaces a local business.

Signal 1: Factual Density and Specificity

DeepSeek's MoE architecture is particularly responsive to factual, structured information. When your business page contains specific, verifiable facts — license numbers, years in operation, service areas with named cities and neighborhoods, certifications from named organizations, real pricing ranges — the model can extract and reproduce that information confidently. Generic language like "serving all of Southern California" produces less extractable signal than "licensed in California (License #1058816), serving Los Angeles, Pasadena, and the San Gabriel Valley."

Signal 2: Cross-Source Consistency

When DeepSeek encounters your business name, address, phone number, and service description in multiple independent crawlable sources, it builds confidence in that information. Inconsistency across sources (different phone numbers, varying business names, conflicting service area descriptions) creates ambiguity that the model resolves by reducing recommendation confidence or omitting the business entirely.

Signal 3: Content Authority Within a Category

Models trained on Common Crawl learn implicit hierarchies of authority. A business that publishes substantive, technically accurate content about its service category signals expertise in a way that a thin service page does not. A roofing company that publishes a detailed guide on "how to identify storm damage on different roofing materials" becomes associated with roofing expertise in the model's learned representations. That association influences recommendations even when a user does not directly reference the guide.

Signal 4: Recency and Freshness

DeepSeek's training data has a knowledge cutoff, and live retrieval mode rewards recently published or updated content. Businesses that consistently publish new content, whether blog posts, service updates, or case studies, appear in more training snapshots over time and perform better in live retrieval when web search is active. Stale websites that have not been updated in years exist in older data snapshots only.

Signal 5: Context Completeness

When a user asks "best electrician in Austin for panel upgrades," DeepSeek needs to match the query against businesses it knows about. A business whose content specifically addresses panel upgrade services, in Austin, with relevant context about pricing range, typical project scope, and licensing, satisfies the query context far more completely than one whose site only says "residential electrician."

How DeepSeek Compares to ChatGPT, Perplexity, and Gemini

For local businesses trying to optimize across the AI search landscape, understanding how DeepSeek differs from other platforms is not an academic exercise. It determines where optimization effort pays the most dividend and where the same action produces different results.

The practical takeaway from this comparison: Perplexity remains the most transparent and directly optimizable platform for local businesses because it always shows its sources and always retrieves live web content. DeepSeek is harder to track (no footnotes by default) but reaches a different and growing user base. Gemini has a structural advantage for businesses with strong Google Maps presence. ChatGPT requires browsing mode for live business data.

Optimizing for one platform does not mean ignoring the others. The foundational signals (crawlable content, consistent NAP, specific factual information, schema markup) benefit all of them. But platform-specific strategies differ, particularly around Gemini's GBP integration and Perplexity's real-time retrieval. For a deeper dive into Perplexity's citation mechanics, see our guide on how Perplexity decides what to cite.

Optimizing for DeepSeek vs Other AI Platforms

DeepSeek optimization shares a large foundation with general AEO (Answer Engine Optimization), but has meaningful differences that reward specific execution choices. Here is how the platform-specific priorities play out.

Where DeepSeek Optimization Matches General AEO

The fundamentals are universal: your business needs substantive, crawlable content on your own domain. Pages that render in plain HTML without requiring JavaScript to display core business information. Consistent name, address, and phone number across every platform where you appear. Schema markup that tells AI parsers exactly what type of business you are, what services you offer, and where you operate.

These are not DeepSeek-specific recommendations. They improve your standing across ChatGPT, Gemini, Perplexity, and Claude simultaneously. The businesses that are waiting to see which AI platform "wins" before investing in this foundation are making a strategic error. The foundation is the same regardless of which model a given user chooses.

Where DeepSeek Optimization Differs

DeepSeek places relatively higher weight on cross-source consistency because its MoE routing is sensitive to data conflicts. A business with identical, specific descriptions across ten crawlable sources generates a cleaner training signal than one with slight variations. This is more important for DeepSeek than for Perplexity, which retrieves live content and can reconcile discrepancies in real time.

DeepSeek also performs differently on bilingual content (covered in the next section) and on content that is well-represented in the specific web corpora that Common Crawl captures frequently. Websites that have been indexed and snapshotted consistently over time have a structural advantage because they appear in more training data snapshots.

The Bilingual Challenge: English-Speaking Businesses on a Chinese-Origin Model

This section addresses a real and underappreciated dimension of DeepSeek optimization that most English-language AEO guides ignore entirely.

DeepSeek was trained on a corpus with significant Chinese-language content. While the English-language training data is substantial (and the model performs well in English), the model's underlying representations were shaped by a bilingual training process that differs from purely English-trained models. This produces observable differences in how DeepSeek handles certain query types and business categories.

Where English-Language Businesses See Reduced Performance

Businesses in categories that are underrepresented in Chinese-language web content may find that DeepSeek produces fewer or less confident recommendations than ChatGPT or Gemini for those categories. Highly localized American service categories (specific trade licenses, US-specific regulatory frameworks, niche regional cuisines) are areas where DeepSeek's training data is thinner relative to Western-trained models.

Conversely, categories that appear frequently across both English and Chinese-language business content (restaurants, hotels, technology services, e-commerce, healthcare) are likely better represented and produce more consistent recommendations.

English Content Quality Still Dominates

DeepSeek's English-language performance benchmarks, as of the V3 and V3.2 releases, are competitive with GPT-4o across most general language tasks. For the vast majority of local business recommendation queries in English, the bilingual training origin does not produce dramatically different output quality. The more impactful variable is still what data is available about your business in the crawlable web.

Businesses with multilingual customer bases (English and Spanish, English and Mandarin) may find that creating content in multiple languages produces a meaningful lift in DeepSeek visibility, since those language combinations appear more frequently in its training data composition. This is not guaranteed, and it requires legitimate multilingual content, not machine-translated filler.

The 5 Mistakes Businesses Make Trying to Optimize for DeepSeek

After analyzing how businesses approach AI search optimization, five patterns consistently reduce DeepSeek recommendation likelihood. Some are the same mistakes made on every AI platform. Others are specific to how DeepSeek works.

1. Treating DeepSeek Like a Search Engine

DeepSeek is a language model that generates recommendations based on learned associations from training data. It is not a search engine that ranks web pages against keyword queries in real time (in its default mode). Stuffing keywords into page titles, building exact-match anchor text, or chasing ranking signals designed for Google are largely wasted effort for DeepSeek optimization. The goal is not to rank for a keyword. It is to be so well-represented in the model's learned knowledge that when a user asks a relevant question, your business is a confident, retrievable answer.

2. Over-Relying on Google Business Profile as the Primary Web Presence

GBP has essentially no value for DeepSeek recommendations in standard (non-Gemini) mode. It is a JavaScript-gated, Google-proprietary dataset. Businesses that have invested entirely in GBP optimization while neglecting their own website domain are invisible to DeepSeek's training data. This mistake is expensive to correct because it requires building a real website content foundation from near-zero.

3. Publishing Content That Only Exists Behind Login Walls or Apps

App-only businesses, subscription gated content, and lead-form-gated landing pages share a common DeepSeek problem: they are not in the training data. If the primary content about your services only exists inside a mobile app, behind a login, or on a page that requires form submission before displaying information, DeepSeek cannot learn from it. Move your substantive business information to public, crawlable HTML pages.

4. Ignoring NAP Consistency Across Crawlable Directories

A business with five different phone numbers across five crawlable directories creates a data conflict that DeepSeek's training process absorbs as ambiguity. The model learns that this business's contact information is unreliable. Over time, this reduces recommendation confidence. An audit that standardizes NAP across every crawlable directory where the business appears, including old, forgotten listings, has an outsized impact on AI recommendation consistency.

5. Not Publishing Content at Scale for the Category

One well-optimized homepage is not enough to build a meaningful training data footprint. Businesses that publish consistent, substantive content across multiple pages — service pages, FAQ pages, location pages, blog posts, case studies — appear in more training snapshots and build stronger learned associations. The businesses that dominate AI recommendations in any local category are typically the ones that have published the most useful, specific, crawlable content about that category. There is no algorithmic shortcut to this. Volume and quality of crawlable content is the long-term moat.

Looking Ahead: DeepSeek's Trajectory

DeepSeek's model releases have followed a rapid cadence: V2, V3, V3.1, V3.2 within roughly 18 months, with R1 and its distilled variants running alongside. The pattern suggests a lab moving faster than most Western competitors expected. As of mid-2026, the V4 generation is in active deployment, and the deprecation of older model aliases is scheduled for July 2026.

Several trends are worth tracking for businesses that want to stay ahead of DeepSeek's recommendation behavior:

Live Retrieval Becoming the Default

As DeepSeek matures its web search integration, the balance between training-data-based recommendations and live-retrieval-based recommendations will shift. This is favorable for businesses, because live retrieval is more responsive to current content than a training data snapshot. Businesses that invest in crawlable content now will benefit doubly: first from the training data they are accumulating, and second from the live retrieval pipeline as it becomes more prominent.

Enterprise Adoption Driving More Business Queries

DeepSeek's dramatic API cost advantage is driving enterprise and developer adoption at scale. As more consumer-facing applications are built on DeepSeek, the volume of business recommendation queries flowing through DeepSeek-based backends increases. The businesses that are well-positioned in DeepSeek's knowledge base today will be the default recommendations in those applications tomorrow.

Open-Weight Proliferation

Because DeepSeek releases model weights publicly, derivative models and fine-tuned variants will proliferate. Some of these will be domain-specific (local services, real estate, healthcare) and may produce dramatically different recommendation behavior than the base model. This proliferation makes the foundational optimization strategies more important, not less, because businesses cannot anticipate which fine-tuned variant a user will encounter. Broad, clean, factual crawlable presence is the defense against unknown model variants.

Frequently Asked Questions

Does DeepSeek have a live web search function for business queries?

DeepSeek has integrated web search capabilities, first introduced with DeepSeek R1 in January 2025. When enabled, it performs real-time retrieval using a RAG-like pipeline. However, unlike Perplexity, which always retrieves live results, DeepSeek's default chat mode draws primarily from training data (Common Crawl and web pages collected before its knowledge cutoff), supplementing with live retrieval only when the web search feature is active or the query demands current information.

Why does DeepSeek sometimes recommend different businesses than ChatGPT or Gemini?

DeepSeek was trained primarily on Chinese-language and English-language web data, with a distinct data composition and MoE architecture that differs from OpenAI and Google models. As a result, DeepSeek may weight certain source types differently, give higher prominence to businesses with consistent structured data across multiple crawlable platforms, and handle bilingual business descriptions with different precision than Western-trained models.

Is DeepSeek safe for businesses to optimize toward?

From a content optimization standpoint, yes. The signals that earn DeepSeek citations are identical to those that improve visibility across all major AI platforms. There are separate geopolitical and data-privacy considerations around DeepSeek's Chinese origin (Hangzhou-based DeepSeek AI) that individual businesses and enterprises should evaluate independently, but the optimization work itself carries no downside risk.

How much does it cost to use DeepSeek via API versus ChatGPT?

DeepSeek is dramatically cheaper at the API level. As of mid-2026, DeepSeek V4-Flash costs approximately $0.14 per million input tokens (cache miss) versus ChatGPT-4o at roughly $5 per million input tokens, a difference of more than 35x. This cost gap has driven significant developer and enterprise adoption, which expands the surface area of business recommendation queries flowing through DeepSeek-based systems.

What is the single most important thing a local business can do to appear in DeepSeek recommendations?

Publish substantive, crawlable, plain-text content on your own domain that directly answers the questions your target customers ask. DeepSeek rewards specificity and factual density. A plumber whose website has a detailed page on "how to fix a slab leak in Southern California" with licensing information, process steps, and real cost ranges will consistently outperform a competitor whose site only lists services in a JavaScript-rendered menu.

Does DeepSeek use Google Business Profile or Yelp data when recommending local businesses?

Not in any reliable or direct way. DeepSeek's training data consists primarily of plain web pages and e-books. JavaScript-rendered platforms like Google Maps, Yelp listings, and most review aggregators are either not crawled or poorly represented in training data. Businesses that rely solely on GBP or Yelp for their online presence are largely invisible to DeepSeek's recommendation engine.