DeepSmith vs Peec: AI Visibility Analytics vs a Track-and-Produce Platform

Teams weighing DeepSmith vs Peec are usually deciding one thing before any feature list matters: whether the job ends when the visibility gap is identified, or continues until an article exists that closes it. Both products sit in the growing market for ai search analytics tools, and both report how AI engines describe a brand, which sources those engines cite, and how the brand compares against a competitor set. They separate at the point of action. Peec surfaces the gap and hands the team a prioritized recommendation to execute in other tools. DeepSmith surfaces the same gap and then plans, writes, and publishes the piece intended to close it, grounded in stored brand context. The choice of DeepSmith or Peec AI therefore depends less on dashboard depth than on where the reader's own constraint sits: in seeing the problem, or in producing the content that resolves it.

DeepSmith vs Peec at a glance

The table below summarizes both platforms on the dimensions that tend to decide the purchase. Peec publishes two pricing tracks, one for brands and one for agencies; the figures here reflect the brands track.

The sections that follow take each product in turn, then compare them criterion by criterion.

DeepSmith: measurement wired into production

DeepSmith is one platform for AI search analytics and content production. It is a production engine rather than a writing assistant, and the output is a finished, publish-ready article rather than a first draft that a human then rescues.

What DeepSmith measures

The AEO module is the measurement layer. It reports mention rate, citation rate, share of voice, and a visibility trend, broken out per platform, with a competitor leaderboard and a view of the sources AI engines cite most often. A Prompts view holds the tracked questions with per-prompt mention and citation rates plus full answer history, and Discover Prompts generates a starter set from stored product, persona, and buyer-stage context. A Pages view attributes citations to specific pages, showing each page's share of total citations and the prompts driving them. The distinction between a mention and a citation is not cosmetic, since the two respond to different interventions.

Engine coverage scales by tier rather than arriving at once. Pro tracks ChatGPT. Grow adds Perplexity. Scale adds Gemini. Claude and Google AI Mode are reserved for Enterprise. That gating constrains a team wanting broad coverage without an enterprise commitment, though ChatGPT, the one engine present on every tier, is where most buyer research starts, and each tier pairs its engine coverage with an article allowance a pure tracker does not carry.

What DeepSmith produces

Production is where the product diverges from a pure tracker. Content Intelligence decides what to write next, drawing on competitor publishing detected as it ships, a Remix function that turns a working competitor page into usable idea titles, tracked keyword clusters with volume and current coverage, and a Discover Topics view for high-opportunity clusters sourced from the team's own site, a competitor's site, or Search Console.

Content Studio turns those ideas into articles. An Idea Bank feeds a Planned Content calendar supporting individual or bulk scheduling. The Writer converts one planned idea into a finished, brand-grounded article with research, internal and external links, a cover image, and publish-ready metadata. Autowrite runs the same workflow hands-off: an article is configured at planning time and writes itself on its scheduled date into Produced Content. From there a human can preview, revise body and metadata, regenerate the cover, and publish to WordPress, Strapi, Webflow, or a webhook, with Markdown and HTML export as a fallback.

Two further layers support production at volume. Deep IQ stores brand context as structured data: company positioning and claim boundaries, per-product profiles, buyer personas, brand voice settings, visual guidelines, and reusable content-type templates. Every module reads from it, which is what allows output to stay consistent at volume without re-briefing each article. The Sitemap module ingests published pages, gives each an AI summary and a classification across topic, type, angle, buyer stage, and key phrases, and keeps that current, powering internal linking, coverage signals, and ideation dedup. Distribution runs through Repurpose and the Apps Library, which adapt a finished article into platform-native versions for LinkedIn, X, Medium, Substack, newsletter email, Reddit, Instagram, Slack and Discord, and other channels.

DeepSmith pricing and limits

DeepSmith publishes four plans. Pro is $99 per month, or $80 on annual billing, with 20 articles a month, 50 tracked prompts, 5 seats, and ChatGPT tracking. Grow, marked most popular, is $199 per month or $160 annual, with 40 articles, 100 prompts, 7 seats, and Perplexity added. Scale is $399 per month or $299 annual, with 90 articles, 200 prompts, 10 seats, and Gemini added. Enterprise is custom, covers all five named engines, and adds 1:1 expert onboarding and a dedicated account manager. A 7-day free trial applies, with no long-term contracts and no cancellation fees.

Where DeepSmith stops short

DeepSmith tracks mention and citation across the engines it covers and does not control or guarantee rankings, citations, traffic, or revenue. Publish-ready describes the state of the output, not a promise that oversight is unnecessary; Autowrite can publish hands-off, and a human can equally review first. On the measurement side, several Peec capabilities have no equivalent here. Sentiment is not a headline metric, and position within an answer is not reported as its own number. Crawlability diagnostics, server-log bot analytics, product-level AI Shopping tracking, a Looker Studio connector, and an MCP server for AI assistants are not advertised features. Seat counts are capped below Enterprise, which matters for wide stakeholder groups.

Peec AI: measurement depth for marketing teams

Peec positions itself as AI search analytics for marketing teams, with visibility, position, and sentiment as its stated center of gravity. Its own structured documentation reframes the product as a generative engine optimization platform for analyzing brand performance, benchmarking competitors, and optimizing presence across AI search engines. Public trust signals include a claim of more than 2,500 brands and agencies and a 4.9 out of 5 rating on G2. Reported company history places it at €650K in annual recurring revenue within four months of launching in early 2025, which suggests measurement demand in this category is real and fast-moving.

What Peec measures

Four metrics anchor the product. Visibility reports the percentage of AI responses in which the brand appears. Position ranks brands within a given chat and includes every detected brand rather than only the tracked set, a wider aperture than most trackers offer. Sentiment monitors how the brand is framed. Share of Voice expresses brand mentions as a percentage of all tracked-brand mentions.

Underneath those metrics, the analysis is granular for a peec ai visibility tracker at this price point. Sources and citations are treated as separate objects: sources are all URLs an engine accessed, citations are the subset explicitly referenced in the answer text, and both are categorized as editorial, corporate, user-generated, reference, or the brand's own website. Prompt management runs on active, suggested, and inactive states with a prompt volume score and AI-suggested additions. A Brand Insights hub consolidates KPI dashboards, performance graphs, a cross-dimensional matrix, and a rankings table.

Three capabilities sit outside what most content-focused platforms attempt. AI Shopping tracks individual products inside AI recommendation surfaces such as the ChatGPT product carousel, reporting win rate, position, and price comparison. A crawlability check tests robots.txt against more than 40 bots, and Crawl Insights integrates server logs to show actual bot traffic. An MCP server allows conversational querying from Claude, Cursor, VS Code or GitHub Copilot, and Windsurf, which fits teams that prefer to interrogate analytics from an assistant rather than a dashboard.

What Peec recommends rather than produces

Peec Actions is the product's bridge from measurement to work. It generates prioritized strategy recommendations categorized by owned pages, editorial coverage, reference sites, and user-generated communities. The recommendations are the deliverable; execution happens elsewhere. There is no article generation, no brand context layer, no sitemap ingestion, and no CMS publishing inside the product. Third-party reviews describe the pattern directly: the platform is strong at description and comparatively weak at prescription, in that it identifies gaps but does not close them.

That boundary is a design decision rather than an oversight. A team that already runs a functioning content operation may prefer a tool that stays in its lane and exports cleanly, which Peec does through Looker Studio, a public API, the MCP server, and CSV export.

Peec AI pricing and seats

Peec publishes two tracks. On the brands track, Starter is $95 per month with 50 prompts, 3 models, 1 project, and daily tracking. Pro is $245 per month with 150 prompts, 2 projects, and 3 countries. Advanced is $495 per month with 350 prompts, 5 projects, and the Looker Studio connector. Enterprise is custom, with unlimited prompts and projects, all models, API access, single sign-on, and dedicated support. Every brands plan includes unlimited users.

The agency track prices separately at $245, $495, and $795 per month for Essential, Growth, and Scale, scaling projects and client seats, with a custom Comprehensive tier adding pitch projects, API, MCP, and single sign-on. A 7-day free trial applies to self-serve signup. Reported pricing has varied over time in third-party coverage, so a buyer should verify current published rates.

Where Peec stops short

Peec does not produce content or distribution assets, the defining limit for any team whose bottleneck is production rather than measurement. It has no brand context layer, so nothing in the product enforces voice or product accuracy in downstream writing, and it does not ingest and classify existing site content. It offers no native CRM integration and no end-to-end attribution to pipeline. Per-prompt cost has been noted as higher than some competitors in third-party analysis. Its own documentation is candid about a category-wide constraint: AI models read HTML, so content behind a paywall or dependent on JavaScript rendering may not be visible to them at all. Like every product in this space, Peec neither controls nor guarantees rankings, citations, traffic, or revenue.

DeepSmith vs Peec by criterion

The two products overlap substantially on core measurement. Both track brand presence across major engines, both report share of voice against a competitor set, and both surface prioritized next steps. The differences below are where a decision turns.

Closing the gap versus surfacing it

This is the central distinction. Peec ends at the recommendation: here is the prompt where the brand loses, here is the category of action that would help. Production then happens in a separate stack, on a separate timeline. DeepSmith closes the loop inside one workspace, because the system that surfaces the gap also schedules, writes, and publishes the piece meant to close it, using the same stored brand context that shapes every other output. For an organization that deliberately separates analytics from production, Peec fits cleanly. For an organization whose constraint is the writing itself, evaluating a peec ai alternative that produces content is the more direct path to acting on what the dashboard reports.

Engine coverage at the entry tier

Coverage favors Peec at the bottom of the range. Peec offers six default engines, ChatGPT, Google AI Overviews, Google AI Mode, Microsoft Copilot, Perplexity, and Gemini, with three active at a time on lower plans, and unlocks Claude and several additional models at Enterprise. DeepSmith names five engines and gates them by tier, giving ChatGPT on Pro, Perplexity on Grow, Gemini on Scale, and reserving Claude and Google AI Mode for Enterprise. A team that needs breadth of engine coverage at roughly $100 per month will find Peec Starter the more direct route. A team that has concluded most of its buyers ask ChatGPT first may accept single-engine coverage at the entry tier and value the article allowance instead. Coverage varies enough by tier in both products that verifying the specific plan, rather than the published engine list, is the safer habit.

Metrics: citation attribution versus position and sentiment

The metric sets are close cousins with different emphases. DeepSmith centers on where the brand appears and which page earns the citation, the orientation of a platform built to act on the answer by improving or creating pages. Peec centers on where the brand ranks inside an answer and how it is framed, supported by a source-versus-citation breakdown by publisher category. A brand-reputation mandate, or a category where being named third rather than first changes outcomes, is better served by position and sentiment as first-class numbers. A production mandate is better served by page-level citation attribution, the number that tells an editor which asset to fix next.

Seats, agencies, and team shape

Peec includes unlimited users on every brands plan and runs a dedicated agency track with client seats and pitch projects. DeepSmith caps seats at 5, 7, and 10 across Pro, Grow, and Scale, with custom counts at Enterprise, and structures multi-client work as isolated Workspaces with their own context, content, and plan. A fifteen-person cross-functional group wanting shared dashboard access will hit the DeepSmith ceiling well before a comparable Peec plan. An agency whose deliverable is published articles rather than reporting will find the Workspace model closer to how it bills.

Integrations and diagnostics available in one product only

The integration surfaces point in opposite directions and reveal each product's intent. DeepSmith integrates into content management systems, publishing to WordPress, Strapi, Webflow, or webhooks. Peec integrates into the analytics stack through a Looker Studio connector, a public API, CSV export, and an MCP server for AI assistants. Several further Peec capabilities have no DeepSmith equivalent: crawlability checking against more than 40 bots and Crawl Insights from server logs give technical teams evidence of how engines reach the site, a different class of information from what any answer-sampling tracker produces, and AI Shopping tracking is differentiated for ecommerce teams, since product-level presence in a recommendation carousel is not captured by brand-level mention rates. Deep IQ, sitemap classification, Autowrite scheduling, and the Apps Library run the other way. The products are not feature-for-feature substitutes in either direction.

Cost per outcome

Headline prices are close at the entry tier and diverge above it, but the units purchased differ, which makes a straight comparison misleading. DeepSmith meters articles per month alongside prompts, at 20, 40, and 90; Peec meters prompts, projects, and countries, at 50, 150, and 350 prompts. A team that would otherwise commission those articles should weigh the DeepSmith allowance against what that volume costs through freelancers or an agency, then treat the tracking as bundled. A team with writers on payroll should treat production as redundant capacity, where Peec's prompt volume and engine breadth read stronger per dollar.

Which should you choose

The decision resolves once the team names its own bottleneck honestly.

Choose Peec AI when measurement is the mandate. The team already runs a functioning content operation, in-house or through an agency, and does not need another writing tool. The priority is knowing where the brand ranks in AI answers, how it is framed, which publisher categories feed the engines, and how that trends against competitors. Peec also earns the pick on several specific needs: unlimited seats from day one, broad default engine coverage at the entry price, product-level AI Shopping tracking for ecommerce, server-side crawl diagnostics for technical SEO, an agency structure with pitch projects, and analytics-side integrations. In these situations a peec ai visibility tracker is not a compromise; it is the correctly scoped instrument.

Choose DeepSmith when production is the constraint. Content is a growth channel, the backlog of identified gaps outruns the team's capacity to write, and the objective is more published on-brand articles per month without adding headcount. DeepSmith fits when SEO and AEO structure should be applied during creation rather than in review, when brand voice and product accuracy need to hold steady at higher volume through stored context, when internal linking and cover images and metadata are recurring manual costs, and when scheduled hands-off production through Autowrite would keep the pipeline moving during busy weeks. Here the single-platform loop from visibility gap to published page is the differentiator.

For teams that need both measurement depth and production capacity, neither product substitutes for the other's strongest feature. The practical approach is to lead with whichever bottleneck costs more today, run both trials against the same prompt set, and revisit the second function once the first is under control. Teams that want to test the production side directly can start a DeepSmith free trial and see real data and real drafts against their own brand context before committing.