DeepSmith vs Siftly: Tracking How AI Describes Your Brand vs Shaping It With Content

Two platforms can report the same number and still lead a team to two very different quarters. That is the shape of the deepsmith vs siftly decision. Both measure how AI describes your brand across the engines buyers now consult before they ever reach a website, and both report the familiar quartet of mention rate, citation rate, share of voice, and trend. The divergence begins after the report is generated. Siftly converts the signal into content briefs, cited-source lists, and outreach targets, then hands the work to whoever writes. DeepSmith converts the same signal into finished articles inside the platform and publishes them to the live site. The decision is therefore less a question of dashboard depth than a question of where the constraint actually sits: in measurement, or in the production capacity to act on what measurement finds.

DeepSmith vs Siftly at a Glance

Siftly AI Brand Tracking: What the Platform Measures

Siftly is a generative engine optimization platform that monitors how AI engines describe and recommend a brand, then recommends fixes. The tracked set spans ChatGPT, Perplexity, Gemini, Google AI Overviews and AI Mode, and Microsoft Copilot, and every tier receives all five. That is an unusually flat entitlement structure for the category, and it is the single strongest argument for Siftly at the entry price.

The reporting layer covers mention rate, citation rate, share of voice against competitors, and period-over-period trend, with per-prompt answer history underneath. Two additions distinguish siftly ai brand tracking from a baseline monitor. The first is sentiment scoring, which classifies mentions as positive, neutral, or negative rather than counting them uniformly. The second is a six-dimension scoring model spanning mention, answer position, share of voice, cited sources, sentiment, and ranking position, which produces a more granular composite than a two-metric summary.

The unit of tracking is the material point of difference. Siftly organizes measurement around products, SKUs, and category pages rather than around a general prompt portfolio, and its plan limits are denominated the same way: one product on Try, three on Starter, ten on Growth, thirty on Pro. That structure maps cleanly onto shopping-shaped queries, where an engine is being asked to recommend an item rather than explain a concept. The higher tiers extend the same logic into marketplace optimization and Reddit engagement.

The company is Y Combinator backed and was selected for Maruti Suzuki's Incubation Programme Cohort 5, which is a reasonable signal of staying power in a category that is still consolidating.

What DeepSmith Does Differently

DeepSmith is an AI search analytics and content production platform in one. The measurement side tracks mention rate, citation rate, share of voice, sentiment, and visibility trend across ten engines, ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, Google AI Mode, Grok, Meta AI, Microsoft Copilot, and DeepSeek, with a competitor leaderboard and a view of the sources the engines cite most. Coverage rises by plan: Pro tracks ChatGPT, Grow adds Perplexity, Scale adds Gemini, and Enterprise and Custom cover all ten.

The production side is what changes the workflow. Every tracked prompt that returns a gap can become an idea in New Ideas, every idea can be scheduled, and the Writer turns a scheduled idea into a finished article that is researched, internally and externally linked, fitted with a cover image, and carrying publish-ready metadata. Opportunity Agents make that first step explicit: an agent reads the visibility data or the Content Map, which holds your site plus unlimited competitor sites on one topic taxonomy refreshed every 24 hours, and returns ideas that each carry the data point justifying them. Autowrite takes the same path without anyone in the application: the article is configured at planning time, writes itself on its scheduled date, and lands in Produced Content. From there it publishes directly to WordPress, Webflow, Strapi, Sanity, Contentful, or a custom webhook.

Grounding comes from Deep IQ, a stored brand context layer holding positioning, product profiles, buyer personas, brand voice, visual guidelines, and content type templates. That context feeds the writer on every run, which is the mechanism behind consistent voice and accurate product claims at volume rather than a promise of it. The output is a finished, on-brand article rather than a first draft awaiting rescue: a production engine, not a writing assistant.

Engine Coverage: Where the Tracked Sets Diverge

The asymmetry runs one way. Every engine Siftly tracks, Microsoft Copilot and Google AI Overviews included, sits inside DeepSmith's set of ten, which extends further to Claude, Grok, Meta AI, and DeepSeek.

What separates the two is the ladder rather than the list. Siftly's entitlement is flat across tiers; DeepSmith's full ten sits at Enterprise and the self-serve plans below it widen one engine at a time. An organization whose buyers sit inside a Microsoft estate, or whose Bing-influenced traffic is material, can get that read on either platform, but gets it at the entry price only on Siftly.

The ladder cuts both ways. Siftly unlocks all five engines at $79 per month, which is real breadth at a low entry price. What that tier does not include is depth: one product, one shopping prompt, and one SKU. Five engines observing a single prompt is a narrow measurement surface: the number of distinct questions under observation, not the number of engines, sets the resolution of the read. DeepSmith takes the opposite trade at a comparable price, offering one engine with fifty tracked prompts on Pro. Teams that need multi-engine breadth immediately reach it on Grow at $199 per month, still well below the Siftly tier at which output volume becomes substantial.

Neither structure is wrong. They optimize for different first questions. Siftly's structure answers "where are we mentioned," across as many surfaces as possible, at the lowest possible entry cost. DeepSmith's structure answers "which of the questions our buyers actually ask are we losing," in depth on one engine first, with the option to widen by tier.

Tracking Depth: Sentiment Scoring vs Page-Level Attribution

Both platforms report the same core metrics, so the differentiation sits in what each adds on top.

Both platforms score sentiment, separating a favorable mention from a damaging one, so tone is on the dashboard either way. Siftly extends that qualitative layer furthest: the six-dimension model adds answer position and ranking position to the standard set. For a brand with an active reputation problem, or one whose category involves frequent unfavorable comparisons, that granularity has real diagnostic value a mention count cannot supply. It stops short of resolution, however. A sentiment score identifies that an answer is unflattering; it does not identify which published page the engine drew that framing from, and the remediation still has to be authored somewhere else.

DeepSmith adds the attribution layer. The Pages view reports which specific URLs earn citations, each page's share of total citations, and the prompts driving them, and competitor citations are broken out by platform and by the exact page winning them. The practical consequence is that a visibility gap resolves to a page-level instruction rather than a directional observation: this URL is being cited for this prompt on this engine, and this competitor page is taking the citations we are not. Sentiment sits on the same overview beside mention rate, citation rate, share of voice, and visibility trend, so tone is read next to the page that produced it.

The distinction matters because the two layers respond to different interventions. Sentiment data tends to route to messaging, PR, and review management. Attribution data tends to route to the content calendar, which is the loop DeepSmith closes inside a single platform.

From Signal to Published Page: The Structural Difference

This is the axis on which the comparison actually turns. Siftly produces inputs to a content operation. DeepSmith produces the operation's output.

Siftly's deliverables are briefs for category pages and blog posts, optimization runs, and citation-source targets covering publications, subreddits, and comparison pages. Those are useful artifacts for a team that already employs writers, editors, and a publishing workflow, and the outreach tooling in particular addresses a surface content production alone does not reach. The constraint is that every one of those artifacts terminates in a handoff. A brief is a specification for work that has not happened yet, and the volume figures in Siftly's pricing count specifications rather than finished pages. There is no native publishing path, so the last mile runs through whatever stack the team already operates.

DeepSmith's deliverable is the finished page. Keyword coverage, heading structure, schema guidance, internal linking against the enriched sitemap, external citation links drawn from a trusted-sources list, metadata, and a cover image are produced inside the writing pipeline rather than added afterward in review. Once the article exists, the Apps Library converts it into platform-native versions for LinkedIn, X, Medium, Substack, newsletter email, Reddit, and other channels, so distribution is a step in the same workflow rather than a project that gets postponed.

Article allowances are banded by plan on the DeepSmith side, at twenty, forty, and ninety per month across Pro, Grow, and Scale, with larger operations on Enterprise. Those are finished articles rather than briefs: Siftly's Growth tier caps at thirty blog outputs and ten category pages at $999 per month, so the ceiling exists on both sides and the units are not equivalent.

Pricing at Comparable Tiers

List pricing, monthly billing, with annual billing lowering the effective rate on both platforms.

Three observations follow from the table for teams weighing deepsmith or siftly on cost. The entry tiers sit within twenty dollars of each other and buy different things: Siftly Try buys measurement breadth across five engines on a single prompt, and DeepSmith Pro buys fifty prompts of depth on one engine plus twenty finished articles. At the popular middle, Siftly Growth at $999 is roughly five times DeepSmith Grow at $199, and roughly two and a half times DeepSmith Scale at $399, with the Siftly premium buying five-engine coverage, marketplace optimization, and Reddit engagement rather than additional finished output. At the top of the published range, the gap widens further: Siftly Pro at $2,999 against DeepSmith Scale at $399, before Enterprise pricing enters the discussion.

Trial terms favor Siftly on duration. Fourteen days with no credit card required is the more generous term, and a formal evaluation process can use the extra week. DeepSmith compresses the same evaluation differently, populating the workspace with a brand brief, competitor set, starter tracking prompts, and a first batch of ideas before payment, so the seven days begin with a configured account rather than an empty one.

Where Siftly Is the Stronger Choice

Siftly is the better instrument when the visibility problem is product-shaped. When an engine is asked which running shoe, which CRM for restaurants, or which protein powder to buy, the answer is assembled from product listings, marketplace signals, and category pages, and Siftly tracks at exactly that grain. A general prompt portfolio measured at the brand level will not resolve to a SKU, and no amount of blog production compensates for absence from a shopping answer.

Three situations point clearly toward Siftly. An e-commerce or DTC brand whose primary AI surface is product recommendation is the central case. A team that needs several engines observed from the cheapest tier is the second, since Siftly's five-engine entitlement is flat at $79 while DeepSmith widens by plan. A team that already runs a functioning content stack, with writers and editors in place, and needs only a tracker with brief generation and citation-source outreach layered on top is the third. In that last case the production capability inside DeepSmith is capacity the team has already bought elsewhere.

The corresponding limits are worth naming plainly. Siftly's outputs are briefs and optimization runs rather than finished articles, so a writing function is a prerequisite rather than an option. The tier naming and the product-and-SKU unit of tracking signal retail as the primary persona, which fits some buyers and not others. And the pricing ladder steepens quickly, with the mid tier at $999 and the top published tier at $2,999 per month.

Where DeepSmith Is the Stronger Choice

DeepSmith is the better instrument when the constraint is production rather than perception. Content teams evaluating a tracker in this category often already suspect where they are losing; what they lack is the throughput to respond at the pace the engines re-crawl and re-answer. A tracker that adds a second dashboard to that situation adds precision to a diagnosis without changing the treatment.

Four situations point toward DeepSmith. Teams whose primary KPI is published pages earning AI citations benefit from tracking and production sharing one context, because the gap a prompt surfaces becomes a scheduled article without re-briefing. Teams with a modest writing bench get the most leverage, since the pipeline absorbs research, SEO structure, linking, imagery, metadata, and publishing rather than producing another queue of specifications. Agencies and multi-brand operators get first-class isolation through Multi-Workspace, where each brand carries its own context, content, and plan, with the honest caveat that each workspace is billed independently and belongs in the cost model. And organizations that want measurement to become a standing backlog, rather than a monthly report someone reads and files, get that loop natively.

Two boundaries genuinely bear on this choice. Pro tracks ChatGPT only, so day-one multi-engine measurement starts at Grow at $199, and ChatGPT is where most buyer research still begins. And the platform is built for content and marketing teams, not for e-commerce product listing optimization at SKU scale, which is precisely the case Siftly is built for.

Which Should You Choose

The decision compresses into three questions about the bottleneck.

If the bottleneck is measurement breadth, both platforms work and the choice reduces to price and plan structure. Siftly delivers its five engines at every tier from $79. DeepSmith delivers depth of prompt coverage first and widens by tier, reaching three engines at $399 and all ten at Enterprise. Teams that want the widest engine list at the lowest tier should choose Siftly. Teams that need engines outside Siftly's five, Claude, Grok, Meta AI, or DeepSeek, should choose DeepSmith.

If the bottleneck is product-level visibility in shopping answers, choose Siftly. Tracking at the SKU and category-page level, marketplace optimization, and the retail orientation of the entire product are purpose-built for that surface, and a content-first platform will not substitute for them.

If the bottleneck is content production, choose DeepSmith. This is the common case for the content and marketing leads asking whether to buy deepsmith or siftly, and it is the case where the difference compounds. One platform that measures the gap and publishes the page against it removes the handoff that ordinarily consumes the weeks between diagnosis and remedy. A team weighing a siftly alternative primarily because briefs keep arriving faster than articles leave is describing a production problem, and the production problem is the one DeepSmith is built to solve.

For teams in that third situation, the fastest way to test the claim is against real data rather than a feature grid. Start a free DeepSmith trial and the workspace arrives with a brand brief, competitor set, and starter tracking prompts already populated, so the first week produces both a visibility read and finished articles rather than a configuration exercise.