Grok, Meta AI and DeepSeek: Do Smaller Engines Matter?

#01What makes a "smaller" engine different?

"Smaller" here means smaller in share of the conversations your buyers have, not smaller in capability. Each of these assistants is built by a large company with its own distribution, data and priorities. That matters more for visibility than model quality.

Three questions decide how an engine can describe your brand. Does it search the live web, or answer mainly from what the model learned in training? Which sources does it reach for first? And does it show citations, so a reader can click through to you? The answers differ a lot between engines, and they change as products update. Our view: write down the answers for each engine you track, with the date you checked, so your team doesn't argue from memory.

#02How does Grok find information?

xAI's developer documentation describes a web search tool that lets Grok "search the web in real-time and browse web pages," and the same documentation describes an X search tool alongside it (xAI). Developers can limit search to particular domains or exclude domains, and responses can return citations.

For marketers, the practical point is that public posts on X can be part of what Grok reads. A brand with an active, accurate presence on X, and named experts who post useful detail there, gives Grok more to work with. A brand that's silent on X leaves that space to customers, competitors and critics. Our view: check what Grok says about your category before investing, because the effect varies by topic.

#03How does Meta AI find information?

Meta describes Meta AI as available across WhatsApp, Instagram, Facebook and Messenger, plus a standalone app and the web, with the ability to search across the web for recommendations and research (Meta). Meta also says answers can be personalised using information people have chosen to share on Meta products, in some countries.

That distribution is the key difference. People meet Meta AI while they're already chatting or scrolling, not while they're deliberately researching. Questions are often casual: where to eat, what to buy, how to fix something. Our view: consumer brands, local businesses and anything bought through social discovery have more reason to watch Meta AI than a B2B software company selling to procurement teams.

#04How does DeepSeek behave?

DeepSeek is an AI assistant and model family from a Chinese AI company, available as an app, on the web and through an API. In MentionX's sample, DeepSeek answers almost never included citations (see the table below). That means even when it mentions your brand, readers have no link to follow, and you can't easily see which sources shaped the answer.

When an engine shows no citations, you're mostly influencing what its model already knows, which comes from training data gathered over time. Clear, consistent descriptions of your company across your site, listings and third-party coverage are the main lever. Changes take longer to show up than in engines that search the live web.

#05Who should track these engines?

Start from your buyers, not the engines. Ask in sales calls and onboarding which AI tools people use for research. Check referral traffic in analytics for visits from these assistants' domains. If a meaningful share of your audience uses an engine, track it. If almost nobody does, a quarterly spot check is enough.

As a rough guide: consumer brands active on social platforms should look at Meta AI and Grok early. Brands whose buyers are active on X, such as developer tools, media and finance, should look at Grok. Brands selling into markets where DeepSeek is popular, or to technical audiences who use it through APIs, should look at DeepSeek. Our view: most B2B teams should get ChatGPT, Google AI features and Perplexity right first.

#06How do you add a smaller engine to your tracking?

Use your existing prompt set rather than writing a new one, so results compare with your other engines. Run each prompt several times, because answers vary. Record whether your brand is mentioned, how it's described, whether there are citations and which sources appear. Keep the engine's version or mode in your notes, since products change often.

Compare results by intent: brand-specific, comparison, recommendation and problem prompts. An engine that names you on brand questions but never on recommendations has a different problem from one that describes you inaccurately.

#07From MentionX data: how engines differOriginal data

Method. Read-only aggregate queries over MentionX scans from 22 August to 29 October 2026 (UTC): 2,217 engine runs (2,154 completed) across 18 anonymised projects and six engines.

MentionX ran the same prompts across six engines. DeepSeek is one of them; Grok and Meta AI weren't in this sample:

Our view: DeepSeek's near-total absence of citations stands out. Engines that cite nothing give you no referral traffic and less insight into sources, so measure mentions and accuracy rather than clicks.

Limits. A convenience sample of mostly B2B software projects, run through engine APIs, which can behave differently from the consumer apps. Perplexity, Copilot and DeepSeek figures come from a single day (26 October 2026), so treat them as a snapshot, not a trend.

#08Worked example: Ledgerline decides which smaller engines to track

Ledgerline is a fictional accounts-payable tool. Figures are illustrative.

Ledgerline, a fictional accounts-payable tool, adds one question to its onboarding survey: "Which AI assistants do you use for work research?" After two months, most answers name ChatGPT, Google and Copilot. A handful mention Grok, mostly developers who integrate Ledgerline's API. Almost nobody mentions Meta AI.

The team adds Grok to its monthly tracking for 15 developer-focused prompts only, and sets a quarterly spot check for Meta AI and DeepSeek. When Grok describes Ledgerline's API limits incorrectly, the team finds an old developer thread on X repeating outdated limits. A developer advocate posts a short, dated correction with a link to the docs. The next month's runs show the correct limits in most answers in this illustrative example.

#09What can you do this month?

Run ten of your most important prompts in each of these three assistants and note what they say about your brand and category. If any answer is wrong, find the likely source and fix it at the source: your site, your listings or your public posts. Add one question about AI tools to your customer survey. Then decide which engine, if any, earns a place in your monthly tracking.

#10How do you check what each engine says about you?

Pick a small, fixed set of prompts: your brand name alone, your brand against two alternatives, "best tool for" your main use case, and two problem-led questions your buyers actually ask. Run each prompt three times in each engine on the same day, in a fresh session, signed out where that's possible. Personalisation in Meta AI and conversation memory in any assistant can colour results, so note whether you were signed in.

Record four things per answer: whether you're named, where you appear in any list, whether the description is accurate, and which sources are shown. A simple spreadsheet with one row per run is enough. After a month you'll see which engine gets you wrong most often, and that's where to spend effort first.

#11Which sources tend to shape these answers?

Engines that search the live web lean on pages that answer the question directly: comparison articles, review sites, documentation and well-structured product pages. Grok can also draw on posts from X. Engines that answer mainly from training rely on what was written about you over a long period, across many sites.

That gives you two jobs. For live-search engines, make sure the pages that rank for your category questions describe you accurately, including pages you don't own, such as directories and reviews. For training-led engines, make your core facts consistent everywhere: what you do, who it's for, where you operate and what it costs, if you publish pricing. Contradictions between your site, your listings and old press coverage are the most common reason assistants get details wrong.

#12What should you not do?

Don't flood X with promotional posts hoping Grok will notice. Posts that read like ads are unlikely to help and may hurt how people see you. Don't create separate content for each engine either; good pages written for people work across engines. And don't treat one surprising answer as a trend. Assistants vary from run to run, so look at patterns across several runs and several weeks before changing strategy.

Our view: the biggest wins from smaller engines usually come from fixing inaccurate facts, not from chasing extra mentions. An assistant that describes you wrongly to a small audience is still doing damage you can measure and fix.

#13How often should you review your engine list?

Review it every quarter. New assistants launch, existing ones add search or citation features, and your buyers' habits shift. Keep a short log with the date, the engine, what changed and whether you added or dropped it from tracking. Over a year that log shows leadership why your coverage looks the way it does, and stops anyone from asking, again, why you aren't tracking the assistant that was in the news last week. If an engine rarely names anyone in your category, note that too; low coverage across the board is a different situation from low coverage for you alone.

#14Common questions

Should every brand track Grok, Meta AI and DeepSeek? No. Track them if your buyers use them. Ask customers and check referral data first.

Does posting on X help with Grok? It can, because xAI documents an X search tool. Results vary, so test your own prompts.

Why does DeepSeek show so few citations? In MentionX's sample it almost never cited sources. Focus on consistent descriptions of your brand everywhere.

Is Meta AI relevant for B2B? Usually less so, because people use it inside social and messaging apps. Check with your buyers.