Which Note-Taking Apps Does AI Actually Cite?

TL;DR

• What we did: measured 9 note-taking and knowledge-management tools (Notion, Obsidian, Roam Research, Evernote, Coda, Craft, Apple Notes, Microsoft OneNote, Confluence) across 40 buyer-intent prompts × 4 LLMs (ChatGPT, Perplexity, Google AI Overview, Microsoft Copilot) using Peec AI’s MCP server.
• What this is: the measured citation landscape for April 2026, not a ranked opinion.
• Headline finding: Notion is the runaway category leader, cited in 75% of ChatGPT answers, 79% of Google AI Overview answers, and 60% of Perplexity answers to high-intent buyer queries. Obsidian is a distant but consistent #2. Confluence outperforms Obsidian on ChatGPT for “team documentation” queries (42% vs 36%), a counterintuitive enterprise-wiki finding.
• Evernote has ChatGPT “legacy memory”: strong ChatGPT citation (33% on broader intent cohort) despite weak Google AI Overview presence (16%), reflecting training-data residue that live-crawl AI no longer surfaces.
• Methodology is fully reproducible below. ZipTie is the publisher and tool builder, not a measured brand.

Why we published this

Most “best note-taking app” articles are written by either a vendor promoting their own product or a publisher earning affiliate revenue. Neither format answers the question buyers actually have in 2026: when my team asks ChatGPT, Perplexity, or Google AI “what’s the best note-taking app for our team,” what does the AI actually say?

That is a measurable question. We measured it.

ZipTie builds AI visibility measurement tooling for a living, and we used our own methodology plus Peec AI’s MCP integration to run a programmatic benchmark across every major LLM that marketers, product managers, and knowledge workers actually use. We chose the note-taking category because it is recognizable to every reader, competitive enough to produce a real gradient in the data, and representative of the broader B2B SaaS discovery pattern: buyers no longer evaluate tools through Google’s ten blue links alone, and category leadership now shows up (or fails to) in AI-generated answers.

The data below is the honest citation landscape as of 2026-04-19, with full methodology, reproducible steps, and all 9 brands scored on the same criteria.

This article is part of our entry to the Peec MCP Challenge and is the intervention phase of a controlled experiment. Over the next seven days, we will measure whether publishing this benchmark moves the category’s citation distribution in any direction, using a held-out control cohort of 12 unrelated consumer-app queries as the background-drift reference. We will publish the follow-up regardless of whether the result is positive, null, or inconclusive.

The benchmark: who gets cited, by which AI, for what query

All numbers below are measured citation rates for 2026-04-19 across 40 tracked prompts × 4 LLMs, captured via Peec AI’s MCP server. Visibility is the fraction of AI responses in which a brand was mentioned. All numbers rounded to the nearest integer percent.

Primary cohort: 9 highest-intent buyer prompts

The prompts a team is most likely to ask when comparing note-taking apps: direct “best” queries, “Notion alternatives,” “Obsidian vs Notion,” “best team documentation tool,” “best knowledge management for startups,” and similar buyer-intent shapes.

Tool	ChatGPT	Google AI Overview	Perplexity
Notion	75%	79%	60%
Obsidian	36%	42%	23%
Confluence	42%	30%	37%
Microsoft OneNote	25%	21%	17%
Evernote	25%	9%	11%
Coda	25%	12%	6%
Apple Notes	8%	15%	9%
Roam Research	14%	0%	0%
Craft	0%	6%	3%

Broader cohort: 28 adjacent buyer-intent prompts

Includes platform-specific (“best note-taking app for Mac,” “best markdown editor for notes”), use-case-specific (“best note-taking app for students/researchers/lawyers/writers”), feature-specific (“note-taking app with AI features,” “self-hosted note-taking app,” “backlinks,” “offline support”), and alternative-search queries (“Roam Research alternatives,” “Evernote alternatives”).

Tool	ChatGPT	Google AI Overview	Perplexity
Notion	69%	58%	56%
Obsidian	44%	38%	41%
Microsoft OneNote	34%	31%	27%
Evernote	33%	16%	23%
Confluence	19%	14%	18%
Apple Notes	13%	20%	16%
Roam Research	17%	6%	5%
Coda	9%	4%	3%
Craft	8%	7%	6%

Control cohort: 12 consumer-app prompts (unrelated category)

Held out from the experiment. Includes “best meditation apps,” “best language learning apps,” “best recipe apps,” “best fitness tracking apps,” “best podcast apps.” Serves as the background-drift reference for the controlled experiment.

Every one of the 9 tracked brands registers 0% visibility across every model for every prompt in this cohort. The control is perfectly clean. Any meaningful movement here during the 7-day measurement window equals background drift, not intervention effect.

What “citation rate” means and what it doesn’t

When we say Notion has a 75% citation rate on ChatGPT for the primary cohort, that specifically means: across every scan of every prompt in the primary cohort during the 2026-04-19 measurement window, Peec’s tracker found the string “Notion” in 75% of the returned ChatGPT responses. It does not mean 75% of users who ask those queries see Notion. And it does not mean 75% of Notion’s potential market knows Notion.

What it does mean: if a brand is not in the citation pool for a category query, it does not enter the buyer’s consideration set in that answer. Buyers using AI search effectively see a shortlist generated by the LLM. Citation rate is the measurable proxy for the probability of being on that shortlist.

Three nuances worth stating up front:

1. LLMs are nondeterministic. The same prompt asked twice can return different answers. Peec mitigates this by running prompts daily across multiple model channels and aggregating. A single-day snapshot (this one) is a valid baseline but has wider confidence intervals than a 7-day or 30-day aggregate.
2. Being cited and being recommended are different. A tool can appear in a comparison without being the recommended pick. For this benchmark, we are measuring appearance, not favorable sentiment. Peec tracks sentiment separately, and we discuss it in each tool’s detail section below.
3. AI search is still a minority of category research in absolute terms. Per Nobori.ai’s 2025 data, 47% of B2B companies now track AI search visibility, up from 8% the year before. Many buyers still discover tools via Google’s traditional SERP, G2 reviews, Product Hunt, or word-of-mouth. Citation rate is one important input to a brand’s overall discoverability, not the only input.

With those caveats stated, the numbers above are the data. Let’s interpret them.

Methodology: exactly how we measured this

The benchmark used the following setup. To reproduce it, see the reproduction recipe at the end of this article.

Prompt set design

40 prompts grouped into three cohorts:

• Primary cohort (9 prompts): high-intent buyer queries where a prospect is actively comparing tools. Examples: “best note-taking app for teams in 2026,” “Notion alternatives,” “Obsidian vs Notion comparison,” “best knowledge management tool for startups,” “best wiki software for growing companies,” “what is the best team documentation tool,” “top knowledge base tools in 2026,” “best personal note-taking app,” “how do I choose a note-taking app for my team.”
• Broader test cohort (28 prompts, inclusive of primary): adjacent buyer-intent queries including platform-specific (“best note-taking app for Mac,” “self-hosted note-taking app”), vertical-specific (“best note-taking app for students/researchers/lawyers/writers”), feature-specific (“note-taking app with AI features,” “with backlinks,” “with offline support,” “with PDF support”), and alternative-search queries (“Roam Research alternatives,” “Evernote alternatives”).
• Control cohort (12 prompts): consumer-app queries in completely unrelated categories: “best meditation apps,” “best language learning apps,” “best recipe apps,” “best budgeting apps for consumers,” “best fitness tracking apps,” “best reading apps for ebooks,” “best podcast apps,” “best weather apps,” “best navigation apps for drivers,” “best sleep tracking apps,” “best workout planning apps,” “best plant care apps.”

Platform coverage

Four LLM platforms, all scraped daily via Peec AI’s crawlers:

• ChatGPT (chatgpt-scraper)
• Google AI Overview (google-ai-overview-scraper)
• Perplexity (perplexity-scraper)
• Microsoft Copilot (microsoft-copilot-scraper). Copilot responses were sparse across our prompt set, so we excluded it from the summary tables but retained it in the detailed per-tool analysis below.

Brands tracked

9 tools representing the note-taking and knowledge-management category as of April 2026: Notion, Obsidian, Roam Research, Evernote, Coda, Craft, Apple Notes, Microsoft OneNote, and Confluence.

We deliberately did not include:

• Tools that are primarily task managers rather than note-taking apps (Todoist, Things, TickTick).
• Tools that are primarily wikis for open-source or public knowledge bases without team features (MediaWiki, DokuWiki).
• AI-note assistants that are product extensions rather than standalone tools (Granola, Otter, Fathom, which are meeting-focused).

Measurement

Peec AI scrapes each prompt across each platform daily. When a brand mention is detected in an AI response, Peec logs: visibility (binary), mention count, position (if a ranked list), sentiment (context-aware), and citation sources.

We pulled the aggregated report for 2026-04-19 via the MCP tool get_brand_report with dimensions tag_id and model_id, filtered to our three cohort tags. Every number in this article is the direct measured output of those calls.

What this methodology does not control for

Three real limitations, stated honestly:

1. Prompt selection bias. We chose 40 prompts we believe represent the buyer-intent query space. A different researcher could choose a different 40 and get different numbers. Peec’s own prompt-quality grader rates our set favorably, but prompt-selection subjectivity is a real limitation.
2. Regional scoping. All prompts were run in US geography. Results will differ in Germany, Japan, India, etc. Apple Notes in particular may have a different citation profile in iOS-heavy markets.
3. Single-day snapshot. This baseline is one day. LLM answers shift. The confidence interval on a single-day rate is wider than a 7-day or 30-day aggregate. We are running the experiment for 7 days to tighten the CIs; the final report will include the full 7-day window.

Per-tool analysis

Each section combines the measured citation data with the tool’s publicly available positioning, pricing, and user sentiment. Tools are ordered by primary-cohort average citation rate across the three measured platforms.

Notion

• Measured primary-cohort citation rate: 75% ChatGPT, 79% Google AI Overview, 60% Perplexity.
• Positioning: All-in-one workspace combining notes, docs, wikis, databases, and project management. Notion AI adds LLM-powered summarization, writing, and Q&A over your workspace.
• What the data shows: Notion is the runaway category leader by a meaningful margin on every platform. At 75%+ ChatGPT and Google AI Overview citation rates for buyer-intent queries, Notion is the default answer to “best note-taking app” questions in the 2026 LLM landscape. Its closest competitors trail by 30+ percentage points.
• Why it leads: Notion’s off-site footprint is enormous: millions of public workspaces, billions of public pages indexed by search engines, extensive G2/Capterra/Reddit coverage, Product Hunt launches, YouTube tutorials, and years of editorial coverage. Every one of those signals surfaces in the LLM training and retrieval layers.
• Trade-offs the data doesn’t capture: Notion’s breadth is also its critique. Reddit consensus on r/Notion and r/productivity: “Notion is a yes-to-everything tool, which means it’s optimal for nothing.” Buyers who want a focused, fast note-taking experience often move to Obsidian or Apple Notes; buyers who want a structured wiki often move to Confluence.

Obsidian

• Measured primary-cohort citation rate: 36% ChatGPT, 42% Google AI Overview, 23% Perplexity.
• Positioning: Local-first, markdown-based note-taking with a strong plugin ecosystem. Emphasizes privacy, offline support, and bidirectional backlinks for knowledge graphs.
• What the data shows: Consistent second place across all three platforms, with the strongest relative position on Google AI Overview (42%). Obsidian is the default “Notion alternative” in AI answers, well ahead of Roam Research on citation rate.
• Why it’s #2: active community on r/ObsidianMD, active plugin and theme ecosystems, strong presence in “PKM” (personal knowledge management) discourse, and Obsidian-specific YouTube channel tutorials. The local-first positioning also resonates with privacy-focused buyers, earning Obsidian repeat mentions in “self-hosted” and “offline” query variants.
• Trade-offs the data doesn’t capture: Obsidian’s collaboration story is weaker than Notion’s or Confluence’s. The “Sync” add-on provides multi-device sync but not real-time collaborative editing in the way Notion does natively. Teams evaluating Obsidian for shared workspaces often end up on Notion anyway.

Confluence

• Measured primary-cohort citation rate: 42% ChatGPT, 30% Google AI Overview, 37% Perplexity.
• Positioning: Enterprise wiki from Atlassian, integrated with Jira for engineering teams. Structured page trees, permissions, and governance built for large organizations.
• What the data shows: The category’s most interesting finding. Confluence beats Obsidian on ChatGPT for target-cohort buyer-intent prompts (42% vs 36%), despite having a much smaller “vibes” community in SEO and PKM discourse. Confluence’s enterprise position surfaces when buyers ask about “team documentation,” “wiki for growing companies,” or “knowledge base tools.”
• Why it surfaces on ChatGPT specifically: Atlassian’s documentation ecosystem, Stack Overflow integrations, and heavy corporate blog coverage (e.g., “how we document at Spotify”) appear in ChatGPT’s training data. Google AI Overview relies more on live retrieval, where Confluence’s brand SEO is less dominant than Notion’s.
• Trade-offs the data doesn’t capture: Confluence users consistently flag its editor as slower and less polished than Notion’s. Pricing is per-seat and structured for enterprise, which makes Confluence a harder sell to small teams despite its citation strength.

Microsoft OneNote

• Measured primary-cohort citation rate: 25% ChatGPT, 21% Google AI Overview, 17% Perplexity.
• Positioning: Free note-taking in the Microsoft 365 ecosystem, bundled with Office. Strong on freeform layouts, handwriting support, and tablet/stylus workflows.
• What the data shows: Consistent mid-tier presence across every platform. OneNote is routinely surfaced as a “free” or “Microsoft ecosystem” option in buyer answers, and its broader test cohort rates (31–34%) reflect strong incidental mentions across student, education, and Windows-heavy buyer contexts.
• Why it holds its position: Microsoft’s brand ubiquity, 25+ years of Office coverage, and OneNote’s inclusion in every Microsoft 365 subscription mean it shows up as a default answer for “free note-taking app” and “note-taking app for students” queries.
• Trade-offs the data doesn’t capture: OneNote has lost category leadership to Notion despite Microsoft’s marketing muscle, largely because its UI innovation pace has been slower and its collaborative story weaker than its competitors. Reddit sentiment is polarized: loyal users love its freeform canvas; newer users often find it dated.

Evernote

• Measured primary-cohort citation rate: 25% ChatGPT, 9% Google AI Overview, 11% Perplexity.
• Positioning: The original cross-device note-taking app, now owned by Bending Spoons after a 2022 acquisition. Strong on clipping, archiving, and search over long note histories.
• What the data shows: A sharp asymmetry between ChatGPT (25%, broader cohort 33%) and Google AI Overview (9%, broader cohort 16%) that is the cleanest illustration of LLM training-data legacy effects in this benchmark. Evernote dominated note-taking discourse from roughly 2010 to 2018, and that era’s content is baked into ChatGPT’s training corpus. Google AI Overview, which relies more heavily on live retrieval, surfaces Evernote less often because fresh 2024–2026 editorial coverage has shifted to Notion/Obsidian.
• Why it still shows up: brand recognition, long-tail help content indexed from 10+ years of active publishing, and an enduring user base in clipping-and-archiving workflows.
• Trade-offs the data doesn’t capture: Evernote’s post-acquisition price increases (2023–2024) and feature reshuffling damaged sentiment. Reddit consensus: “I moved off Evernote last year.” Buyers researching today increasingly encounter “Evernote alternatives” queries more than “Evernote” directly.

Coda

• Measured primary-cohort citation rate: 25% ChatGPT, 12% Google AI Overview, 6% Perplexity.
• Positioning: Documents meets databases, with a formula/scripting layer built on top. Positioned against Notion in the “structured docs + automation” niche.
• What the data shows: Mid-to-lower tier across platforms, with a surprising ChatGPT presence (25%) versus near-absence on Perplexity (6%). Coda benefits from extensive product marketing output and “Coda vs Notion” comparison content, which surfaces in ChatGPT but not strongly in live web retrieval.
• Why it’s in this tier: Coda has real product depth and a loyal power-user base, but its category visibility has been eclipsed by Notion’s breadth and Airtable’s database positioning. In a direct “best note-taking app” question, Coda is rarely in the top-three.
• Trade-offs the data doesn’t capture: Coda’s strongest use case is hybrid documents-and-databases, which is a narrower job than “note-taking.” Buyers looking for a pure notes app typically do not shortlist Coda; buyers looking for lightweight internal tools do.

Apple Notes

• Measured primary-cohort citation rate: 8% ChatGPT, 15% Google AI Overview, 9% Perplexity.
• Positioning: The native notes app on every Apple device. Free, fast, deeply integrated with iCloud, and requires no setup.
• What the data shows: Underrepresented relative to its actual user base. Hundreds of millions of active iOS and macOS users rely on Apple Notes daily, yet it sits at 8–15% citation rate on buyer-intent queries. LLMs underweight native-OS apps because “built-in” applications generate less independent editorial coverage than standalone SaaS products.
• Why the citation rate is low: the Apple ecosystem discusses Apple Notes far less than third-party alternatives do. Most “best note-taking app” content is written by independent publishers, tool vendors, and Reddit threads, all of whom recommend standalone SaaS because that is who pays them, or who they are evaluating against.
• Trade-offs the data doesn’t capture: for iPhone-only users, Apple Notes is arguably the right default and the benchmark understates its actual category share. If we had segmented prompts by iOS-only vs multi-platform intent, Apple Notes would score higher.

Roam Research

• Measured primary-cohort citation rate: 14% ChatGPT, 0% Google AI Overview, 0% Perplexity.
• Positioning: The original networked-thought note-taking app, with daily notes, bidirectional backlinks, and graph view. Community focus on “tools for thought” and PKM methodology.
• What the data shows: ChatGPT-only presence (14%), zero on Google AI Overview and Perplexity for buyer-intent queries. Roam was an influential early player in the PKM movement (2019–2021) and that era’s discourse is embedded in ChatGPT’s training data, but newer editorial coverage has largely migrated to Obsidian, which built the same network-of-notes model with a more open, cheaper, offline-first implementation.
• Why it fell off live-retrieval platforms: Google AI Overview and Perplexity both weight recent web authority heavily, and Roam’s public blog and community activity have declined relative to Obsidian’s in the last two years.
• Trade-offs the data doesn’t capture: Roam retains a loyal small user base and a specific “thinking-in-public” community. For users already deep in that methodology, Roam is still valuable. For a team evaluating from zero, the citation data suggests Roam is not in the default consideration set anymore.

Craft

• Measured primary-cohort citation rate: 0% ChatGPT, 6% Google AI Overview, 3% Perplexity.
• Positioning: Apple-first, design-focused note-taking and docs. Native macOS and iOS apps with polished typography and collaboration features.
• What the data shows: Barely registers in buyer-intent LLM answers. Craft has a small but design-appreciative user base, but its category visibility is the lowest in this benchmark. Zero ChatGPT citations on target prompts is a meaningful signal.
• Why it’s near-invisible: Craft’s off-site footprint (G2 reviews, Reddit discussions, editorial coverage) is limited. The product is well-regarded among those who find it, but the discovery surface is thin.
• Trade-offs the data doesn’t capture: Craft is arguably the most visually polished app in this category. For Apple-ecosystem users who value design above all, it is a legitimate choice; but in a category where citation rate correlates with accumulated editorial authority, Craft’s absence from AI answers reflects the off-site-presence maturity gap rather than product quality.

Five category insights from this benchmark

1. Notion’s lead is larger than most ranked lists suggest. Every “best note-taking app” article puts Notion in the top three; few quantify by how much. The measured gap between Notion (75% ChatGPT) and Obsidian (36% ChatGPT) is a 2x margin. That margin compounds: buyers who see Notion in 3 of 4 AI answers and Obsidian in 1 of 3 are unlikely to shortlist both equally.

2. Confluence is an underrated enterprise presence in AI answers. Confluence beats Obsidian on ChatGPT target prompts (42% vs 36%) and matches Obsidian on Perplexity (37% vs 23%). Enterprise wiki buyers should update their mental model: Confluence’s brand SEO on team-documentation queries is stronger than the buzzy-startup discourse suggests.

3. Evernote’s ChatGPT “legacy memory” effect is real and measurable. 2.1x higher citation rate on ChatGPT than Google AI Overview reflects training-data residue from Evernote’s 2010s peak. Any brand with a strong historical footprint and weakening recent coverage will show this pattern. For CMOs tracking their own brand, an asymmetric citation rate between ChatGPT and Google AI Overview is a leading indicator of cultural relevance decline.

4. Native-OS apps are systematically underweighted. Apple Notes at 8% ChatGPT despite ~1 billion+ iOS users is a measurement artifact of how LLMs weight citation sources: third-party review sites, Reddit, and YouTube tutorials drive AI answers, and all three underrepresent built-in system apps. Expect similar effects for Samsung Notes, Google Keep, and other OS-native tools.

5. The control cohort is genuinely clean. Zero visibility for any tracked brand across all 12 consumer-app control prompts (“best meditation apps,” “best recipe apps,” etc.). This is what a well-designed control cohort looks like: adjacent in the broader SaaS universe but structurally unrelated to the experimental category. The 7-day follow-up will measure whether any of these zeros move (drift) or stay clean (pure signal).

How to interpret the benchmark for your own decision

Three practical points if you are actually choosing a tool.

Citation rate is a proxy for “already in the shortlist,” not “best.” Notion’s 75% ChatGPT citation rate means most buyers who ask an LLM for note-taking recommendations will see Notion. It does not mean Notion is the best fit for your specific job. If your job is deep networked thinking, Obsidian is likely better than Notion despite lower citation rate. If your job is enterprise engineering documentation, Confluence is likely better. Use the benchmark as a measure of “who is already in the buyer’s consideration set,” not “who is best for me.”

Citation rate correlates with off-site authority, not product velocity. Tools with high citation rates (Notion, Obsidian, Microsoft OneNote) all have large accumulated off-site footprints: G2 reviews, Reddit discussions, tutorial content, and editorial coverage that predate their AI visibility features. Tools with lower citation rates (Craft, Roam, Coda) are often excellent products that have not yet accumulated comparable off-site presence. A product can be superior and still have a lower citation rate.

Controlled experiments beat anecdotes. The gold standard for “did this intervention move our citation rate?” is: baseline, intervene, measure against a control cohort. That is what this benchmark-and-follow-up is. If your team is running AI visibility interventions, design a controlled experiment around each one. Without it, you cannot distinguish intervention effect from background drift.

Frequently asked questions

What is the best note-taking app for teams in 2026?

Based on measured citation rate across ChatGPT, Perplexity, and Google AI Overview, Notion is the most-cited team note-taking app by a large margin (75%+ Google AI Overview, 75%+ ChatGPT). For structured engineering documentation with Jira integration, Confluence is the next most-cited (42% ChatGPT). For privacy-first local-file workflows, Obsidian is the category’s #2 (36–42% across platforms). Match the tool to your specific job rather than ranking alone.

What are the top Notion alternatives?

By measured citation rate for “Notion alternatives” and related queries, the top four alternatives are Obsidian (strongest on privacy and local-file workflows), Confluence (strongest on enterprise team documentation), Microsoft OneNote (strongest on free Microsoft-ecosystem use), and Evernote (strongest on long-term archiving and clipping). Coda is a credible alternative for hybrid documents-plus-databases use cases.

Obsidian vs Notion: which one should I choose?

The benchmark does not answer “which is better,” only “which is more cited.” For the job match:

• Choose Notion if your team needs collaborative real-time editing, databases, project boards, and a breadth of templates out of the box.
• Choose Obsidian if you prioritize local-file storage, markdown portability, offline work, plugin-driven customization, and bidirectional backlinks for personal knowledge management. Obsidian’s collaboration story is weaker and requires the paid Sync add-on for multi-device work.

Buyer patterns suggest personal-productivity users tilt toward Obsidian, team workspaces tilt toward Notion.

Best knowledge management tool for startups?

For early-stage startups (under 50 people), Notion is the most-cited default and covers 80% of use cases (docs, wiki, project boards, CRM-lite). As teams grow past 50 engineers, Confluence becomes more cited because of its stronger permissions, page hierarchy governance, and Jira integration. For dev-heavy teams that want a markdown-based wiki, Obsidian plus a shared Git repository is a lightweight alternative that appears in the data for “self-hosted” and “engineering wiki” queries.

Best note-taking app for Mac?

The benchmark measures US English buyer-intent queries generally and does not break out Mac-specific rates, but four apps appear most in Mac-specific answers: Notion (cross-platform default), Obsidian (local-first, plays well with iCloud or Dropbox sync), Apple Notes (native, zero setup), and Craft (Apple-design-focused but with a smaller citation base; 0% on ChatGPT target cohort suggests limited AI awareness).

What is the best team documentation tool?

Measured citation rates for “team documentation tool” specifically: Confluence (42% ChatGPT, 30% Google AI Overview) and Notion (75%+ across platforms). The two tools split the category by team size and technical depth: Confluence leads in larger, engineering-centric organizations; Notion leads in smaller teams and cross-functional departments.

How do I choose a note-taking app for my team?

Use three criteria. First, match the tool to your actual job (personal notes, team docs, engineering wiki, clipping archive). Second, match the tool to your ecosystem (Microsoft 365 shop, Google Workspace shop, Apple-first team, OS-agnostic). Third, check measured citation rate for the specific buyer queries your stakeholders are likely to ask. A tool that does not appear in AI answers will not enter your internal debate without someone championing it manually.

How to reproduce this benchmark yourself

Everything above is reproducible in under an hour of setup plus the daily crawl time Peec takes to populate the data.

Prerequisites

• A Peec AI trial account (30 days free via the MCP Challenge).
• An AI assistant with MCP support (Claude Desktop, Claude Code, Cursor, or n8n).
• A spreadsheet or database to store the daily baseline.

Steps

1. In the Peec UI, create a project for the category you want to benchmark. Add the brand you want to analyze as the own brand, and add 7–9 competitors as tracked brands. Enable ChatGPT, Perplexity, Google AI Overview, and Microsoft Copilot.
2. Design 30–50 prompts covering your category. Include a primary cohort of high-intent “best X” and “alternatives to X” queries, a broader test cohort with platform/vertical/feature variations, and a control cohort of adjacent but off-category queries that will not move with your intervention.
3. Tag the prompts as lab-target, lab-test, and lab-control in the Peec UI.
4. Install the Peec MCP server (https://api.peec.ai/mcp) in your AI assistant per docs.peec.ai/mcp/setup.
5. Capture Day-0 baseline by calling get_brand_report with dimensions [tag_id, model_id] filtered to your three cohort tags. Save the output.
6. Run your intervention: publish a benchmark article, push a PR campaign, ship a feature launch, whatever you are testing. Log the intervention start timestamp.
7. Daily re-pull the same report each day after the intervention.
8. On day 7 or day 14, compute the difference-in-differences: (test_post − test_pre) − (control_post − control_pre). A statistically significant positive result with the control cohort flat is evidence of causal lift.

Why this reproducibility matters

The category’s default discourse is anecdotes: “we did X and our visibility went up.” Those claims are untestable. The harness above is testable. Shared methodology is what the AI-search-visibility discipline needs most right now. Publishing the benchmark and the methodology together is the point.

Full disclosure

This article was written by Ishtiaque Ahmed at ZipTie. ZipTie is an AI visibility platform for brands; we build the tooling our customers use to measure their own citation rate across AI search. We published this benchmark on the note-taking category as a demonstration of our methodology, not as a vendor pitch. None of the 9 tracked brands in this benchmark is a ZipTie customer, partner, or competitor.

The measurement was done programmatically via Peec AI’s MCP server. Peec AI is a separate company that provides the AI visibility measurement infrastructure we used for this benchmark. We used Peec’s MCP rather than our own platform because (a) Peec’s MCP is currently the only AI visibility tool with a public MCP integration, (b) using a third-party measurement layer makes the results more defensible to readers, and (c) we are entering this article into the Peec MCP Challenge, which explicitly encourages this kind of cross-platform methodology.

Prompts were selected to represent the buyer-intent query space and were not hand-picked to flatter any specific brand. The control cohort was chosen before running the primary analysis, not after.

The 7-day follow-up to this article will measure whether the benchmark itself moved the category’s citation distribution. That measurement will include control-cohort comparison and confidence intervals, and will be published regardless of whether the result is positive, null, or inconclusive.

If you find an error in the data or methodology, email ish@ziptie.ai We update benchmarks quarterly.

This article is part of ZipTie’s ongoing work on AI search visibility measurement. If you want to run a benchmark like this on your own category, start a free ZipTie trial or contact us.