DeepSmith vs Mentionable: Brand Mention Tracking vs Closing the Gaps It Finds

The DeepSmith vs Mentionable decision is not a feature contest between two similar dashboards. It is a decision about where the work stops. Mentionable measures how large language models talk about a brand and hands the team a prioritised list of fixes. DeepSmith measures the same class of signals and then produces the articles that address them, inside the same product. Both are legitimate positions in the AI brand mention monitoring category, and the correct choice depends almost entirely on where a given team is currently constrained.

Teams whose production pipeline already functions, with writers, editors, and a working CMS workflow, tend to need instrumentation rather than capacity. Teams that can already see the gap but cannot staff the response tend to need the opposite. This comparison sets out what each product does, what each does not do, and which reader situation each one fits.

DeepSmith vs Mentionable at a glance

What Mentionable does well

Mentionable is a measurement product built by a solo developer in France, launched in mid-January 2026 and operating without outside funding. A third-party review published by Salesdorado in July 2026 rated it 4.2 out of 5 and reported roughly 50 brand customers running more than 100,000 prompts per month. That profile matters for evaluation: the product is young, the roadmap moves quickly, and the operational surface is small.

Its clearest structural advantage is that engine access is flat rather than tiered. Mentionable tracks ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI Mode, and Google AI Overview on every paid plan, with Grok appearing in the traffic-attribution copy on the features page. A brand on the entry plan watches the same engines as a brand on the agency plan. Coverage is limited by credit budget rather than by plan gating, which is a meaningfully different constraint.

Several capabilities in the mentionable brand tracking feature set have no direct equivalent in the comparison. Fan-out queries surface the sub-questions a model runs before composing an answer, which exposes topics a brand needs to exist on rather than simply prompts it is losing. Missed citations identify answers that draw on the brand's own pages without naming the brand, a failure mode that a mention-rate chart alone tends to hide. The Quantified Backlinks module lists the domains models cite within a niche alongside market pricing for placements, converting a source report into a procurement decision. A Reddit module surfaces threads AI engines cite and drafts reply variants in three tones.

The MCP server is the other genuine differentiator. It exposes Mentionable's data inside Claude Desktop, Cursor, ChatGPT, and any MCP-compatible agent, so a practitioner can interrogate their visibility data conversationally rather than through a dashboard. For teams already operating agent-native workflows, that reduces the friction of checking anything at all.

The agency workflow is mature relative to the product's age. White-label reporting covers logo, colors, custom domain, and support email; bulk mode runs up to 500 audits in parallel against the same prompt set; audits are packaged as client-shareable deliverables. Six free, no-signup utilities sit outside the paid product as lead magnets: a visibility check, a backlink checker covering up to 50 domains, an llms.txt generator, a page audit, a schema generator, and a GEO audit.

Where Mentionable stops

Mentionable does not write. That is a design decision rather than a defect, but it is the decision that determines whether the tool solves a given team's problem. Every recommended fix, whether a heading restructure, a missing comparison page, or an entire topic the fan-out data says the brand does not cover, has to be briefed, drafted, edited, and published somewhere else. The tool does not run a content calendar, does not store a brand-voice ground truth, does not generate distribution assets, and does not publish to a CMS.

Three further constraints deserve weight in an evaluation. First, scan cadence is a function of credit budget rather than a plan setting: 1,500 credits on the Growth tier can be spent, for example, on roughly 45 prompts across seven engines four times per month, and page audits and Reddit enrichment draw from the same pool at approximately 100 credits per page audit. Weekly collection is the practical ceiling on lower tiers. Second, the concentration risk is real. The Salesdorado review treats the solo-founder structure as both a velocity advantage and a maturity risk, which is a reasonable reading for any team building a client-facing offering on top of the product. Third, the public review footprint is thin, with one substantial third-party review and no G2 or Capterra presence at the time of writing, and documentation is French-first with a functional but secondary English site.

What DeepSmith does well

DeepSmith is one platform for AI search analytics and content production. The tracking layer reports mention rate, citation rate, share of voice, sentiment, and visibility trend, with a per-platform breakdown, a competitor leaderboard, and the sources engines cite most often. Prompts carry per-prompt mention and citation rates with full answer history, and Discover Prompts generates a starter set from stored product, persona, and buyer-stage context. The Pages view attributes citations to specific URLs and to the prompts producing them, and Competitor Citations shows which rival pages win the same prompts on which platform.

The distinguishing property is that this data does not terminate in a report. Content Map turns competitor publishing and your own pages into one topic taxonomy with the coverage gaps named, and Opportunity Agents read that map and the visibility data to return ideas with the justifying data point attached to each one. Content Studio moves those ideas through New Ideas, a planning calendar, and the Writer, which produces a finished article with research, internal and external links, a cover image, and publish-ready metadata. Autowrite runs the same sequence on a schedule with no one inside the application, and Produced Content is where a human reviews, revises, and publishes to WordPress, Strapi, Webflow, or a custom webhook.

Two supporting layers make that output usable rather than merely fast. Deep IQ stores company positioning, product profiles, buyer personas, brand voice, visual guidelines, and content types as structured context that every generation reads, which is what keeps voice and product claims stable across volume rather than drifting per writer. Content Map ingests published pages, classifies each by topic and funnel stage, and re-checks sitemaps every 24 hours so new pages fold in on their own; that index powers internal linking, ideation dedup, coverage signals, and the Pages view in the analytics module. Distribution is attached to the article rather than deferred: the Apps Library converts a finished piece into platform-native versions for LinkedIn, X, Medium, Substack, newsletter and nurture email, Reddit, and other channels, each adapted to that channel's tone and length.

Where DeepSmith is constrained

Engine access is the clearest trade, and it is a tier boundary rather than a coverage gap. DeepSmith tracks ten engines in total, but access rises with the plan: Pro covers ChatGPT only, Grow adds Perplexity, Scale adds Gemini, and Enterprise covers all ten. A brand that wants Copilot, Grok, or Google AI Overviews watched from the entry plan will not get them there, and a brand that needs multi-engine coverage on a modest budget will find Mentionable's flat engine access more economical for measurement alone. One qualification bounds that trade: Mentionable's breadth is metered by a shared credit pool rather than plan gating, so watching every engine on a low tier tends to cap collection at weekly rather than buy deeper coverage. Each engine DeepSmith tracks also feeds production directly rather than a dashboard read in isolation.

The second constraint follows from the bundle. Production capacity is how the pricing is structured, so a buyer who wants measurement only is paying for article and seat allocations that will go unused. For a team that has no intention of moving production into the platform, that is a poor fit regardless of how good the tracking is. The 7-day trial is generous relative to Mentionable's 4 days but still short for a category whose value accumulates over collection cycles.

Finally, publish-ready is a claim about structural and editorial consistency, not a promise that no human ever reads the output. Autowrite can run hands-off, and many teams do run routine pieces that way, but sensitive, technical, or regulated content should still pass an editor. Internal-link automation likewise operates against the ingested sitemap and the configured setup rather than guaranteeing a fixed count on every piece.

Engine coverage and pricing compared

The two products price against different units, which makes headline comparison misleading unless the unit is stated. Mentionable bills scan credits, where one credit represents one prompt queried on one engine for one persona, and over-quota usage on the Agency plan is charged at 0.02 euros per scan equivalent against a customer-set monthly cap. DeepSmith bills bundled allocations of articles, tracked prompts, and seats, with engines added as the tier rises.

At the entry point the two sit close together. Mentionable Growth at 79 euros per month covers one project, up to 115 tracked prompts, 1,500 credits, all engines, and unlimited team members. DeepSmith Pro at $99 per month covers 20 articles, 50 tracked prompts, 5 seats, and ChatGPT only. A measurement-first buyer gets substantially more tracking surface for the money at Mentionable; a buyer who intends to publish gets twenty finished articles that Mentionable does not attempt to provide.

In the middle, Mentionable Pro at 149 euros per month adds three projects, up to 231 prompts, 3,000 credits, MCP integration, and priority support. DeepSmith Grow at $199 per month adds Perplexity, 40 articles, 100 prompts, and 7 seats. At the top of the published range, Mentionable Agency at 299 euros per month adds unlimited projects, up to 385 prompts, 5,000 credits, white-label reporting, and pay-as-you-go overage, while DeepSmith Scale at $399 per month adds Gemini, 90 articles, 200 prompts, and 10 seats. DeepSmith also offers a custom Enterprise tier covering all ten engines with a dedicated account manager and 1:1 onboarding; Mentionable publishes no enterprise tier. Prices are quoted in euros by Mentionable and in dollars by DeepSmith, so exchange-rate movement affects any direct comparison.

Time from signal to shipped article

The operational difference shows up most clearly in the interval between detecting a gap and publishing something that addresses it. With Mentionable, the signal arrives in a dashboard, a person interprets it, a brief is written, a writer or a separate AI tool produces a draft, an editor revises it, and someone publishes. In most mid-market teams that path runs from several days to several weeks, and the constraint is rarely the tool.

With DeepSmith, the signal moves into New Ideas, gets scheduled, and the Writer produces a finished piece that lands in Produced Content for optional review before publishing to the connected CMS, with distribution assets attached. Routine pieces can complete within a day. The qualification matters: this compresses the production step, not the judgement step, and teams that gate every piece through senior review will see a smaller improvement than teams that gate selectively.

That difference is also the honest argument for running both. Nothing prevents a team from using mentionable brand tracking for flat engine access on a low tier and running production on a separate platform, and for organisations with a large existing content function, that split may be the better arrangement.

Agency and multi-client operations

Both products serve agencies, but they serve different agency business models. Mentionable's Agency plan is built for selling visibility reporting: white-label branding, bulk audits at up to 500 in parallel, and shareable client-ready deliverables map directly onto a retainer where the deliverable is the report. DeepSmith's Multi-Workspace is built for running content production on behalf of clients, with each brand isolated in its own workspace carrying its own Deep IQ context, content, plan, and billing. White-label reporting is not part of DeepSmith's offering.

An agency whose revenue comes from audits and advisory will find Mentionable's packaging closer to the shape of the work. An agency whose revenue comes from producing published content at volume will find the workspace model closer.

Which should you choose

The choice between DeepSmith or Mentionable resolves along one axis: whether the binding constraint is knowing where the brand is invisible or producing the content that fixes it.

Choose Mentionable when measurement is the gap. Teams with a functioning content operation, an existing writing stack, and a specific need for broad engine coverage at a low entry price are the natural fit. The same applies to buyers who want to query visibility data through MCP inside Claude Desktop or Cursor, to agencies selling visibility reports, and to French-market teams who will benefit from French-first documentation. Anyone assessing a Mentionable AI alternative purely on tracking breadth should recognise that Mentionable's flat engine access is difficult to beat at its price.

Choose DeepSmith when production is the gap. Teams that can already see where they are losing citations but cannot staff the response are the natural fit, as are teams that want SEO and AEO formatting, internal linking, schema, cover images, and metadata handled during generation rather than bolted on afterward. Brand-voice consistency at volume, distribution assets generated from each article, and direct publishing into WordPress, Strapi, or Webflow all point the same direction. A buyer shortlisting DeepSmith as a Mentionable AI alternative is not trading engine breadth for a production pipeline, since DeepSmith tracks ten engines to Mentionable's seven to eight; what the plan decides is how many of them are switched on. The tier decision then follows engine requirements: all ten engines require Enterprise, while the production pipeline is there from Pro.

Consider neither when the requirement sits outside both. Enterprise analytics with custom SLAs, audit logs, and formal certifications is a maturity requirement that neither product's current stage clearly satisfies. Teams that need only large-corpus prompt and demand research, or only a general-purpose writing tool with no measurement requirement, are better served elsewhere.

For most teams the decision reduces to a single question about sequence. If the organisation will act on a gap report within a week, measurement alone is sufficient and the reporting tool should be chosen on coverage and price. If gap reports have been accumulating without a corresponding increase in published output, the constraint is production, and instrumentation will not move it.

Teams evaluating whether the closed loop changes their throughput can test it directly rather than by inference. DeepSmith offers a 7-day free trial with real tracking data and real drafts, no credit card required, and no long-term contract.