Note: We make Atlas. This is a comparison written by the team that built it, not a neutral third-party review. Where Miro has the better answer for a given research job, the article says so plainly. See the table rows where Miro wins and the "When to choose Miro" section below. The goal is to give you the data you need to choose the right tool for the kind of work in front of you, not to convince you Atlas is the answer to every research job.

Atlas is a visual research workspace for people whose work depends on understanding a body of papers: a thesis, a treatment decision, a major-purchase teardown, a literature review. Miro is a collaborative whiteboard tool: infinite canvas, sticky notes, blocks, templates, and real-time multi-user editing, designed for teams doing brainstorming, mapping, and workshops. Both tools touch a researcher's daily work, the wedge is what happens after the first answer. Atlas deconstructs each paper into a Knowledge Map (a visual map of the argument), projects a whole corpus into a Semantic Map, runs every answer through claim-source-justification (the citation-grounded surface that explains why a passage supports a claim), and compounds prior work into a persistent knowledge graph so projects get smarter the longer you use Atlas. Miro's brand, integration with team workflows, and the collaboration ecosystem are genuinely best-in-class in the whiteboard category, the templates and the real-time multi-user editing are unmatched. If you need to trust the answers (for a thesis, a treatment plan, a brief, a hire), the visual maps, claim-source-justification, and compounding graph are where Atlas earns the comparison.

How is Atlas different?

Miro and Atlas overlap at the surface: both touch the work of reading and reasoning over sources. But they diverge on three capabilities that decide whether the output is shareable, defensible work. This section walks through the three differences, in order.

1. Visual maps of every paper and project

Atlas builds two kinds of visual map automatically as you read. A Knowledge Map deconstructs each paper into its argument structure: claims, evidence, definitions, and labeled relations between them (motivates, causes, enables, contradicts), laid out as a multi-level zoom. You see the paper's spine at the top level and drop into the supporting passages with a click. A Semantic Map projects your whole project (sources, notes, chats, citations) into a spatial canvas where related items cluster by topic, and you can re-project the same canvas under a new topic angle without re-reading anything. The Semantic Map is how 200 papers stop being a folder and start being a corpus.

"It's like an ultimate GPT. I can finally see what I've read." Kyle Lao, NUS researcher

Miro does not have a per-paper claim-evidence deconstruction or a topic-angle re-projection across an entire project. If you've ever spent an afternoon trying to recover the structure of a paper you read three weeks ago, the Knowledge Map is the surface that pays for itself first. Visual maps make a body of papers legible at a glance, and the multi-level zoom of the Knowledge Map is the surface Atlas is built around.

2. Every claim traces to a source, and Atlas explains why the source supports it

The hallucination problem in AI research tools isn't "the model made something up." It's "the model put a citation next to a claim that the cited passage doesn't justify." Atlas renders every answer as a claim-source-justification triple: the claim, the passage, and a one-sentence explanation of why the passage supports the claim. You can click into the source paragraph and read the highlighted sentences in context.

The benchmark Atlas runs internally is the H/V ratio: the proportion of generated sentences whose citation does not survive a passage-level re-check, divided by the proportion that does. Atlas targets H/V < 0.1 on the citation-grounding benchmark, and we publish how the benchmark is constructed in Verifiable AI Research (2026): What It Actually Means. Miro's answers may include citations or links to sources, but they're grounded at the sentence-citation level (or not at all), not at the claim-justification level. For most casual question-answering the gap doesn't matter. For a thesis sentence, a legal brief paragraph, or a treatment-decision summary, it does. The wedge in one sentence: every claim traces to its source, and Atlas explains why the source justifies it.

3. Your projects compound: the second month is 10× the first

Miro treats each session (or project, or workspace) as a separable container: work goes in, an answer comes out, and the next session starts fresh. Atlas builds a persistent per-user knowledge graph across projects: every citation you jump to, every annotation you make, every Knowledge Map and Semantic Map you generate accumulates into a four-layer graph (citations + mentions + KMs + SMs) that the next chat can draw from. Open a new project on a related topic and Atlas can pull in the relevant sources, prior annotations, and chat history without re-ingesting.

This is the capability we hear about most from long-term users: the second month is 10× the first because the graph has something to work with. John Tan, a postdoc using Atlas for a multi-year literature review, describes it as "the only tool where the work I did last semester is still doing work for me this semester." Put plainly: projects get smarter the longer you use Atlas. Miro does not have an equivalent persistent compounding graph across projects, which is the wedge for sustained, multi-month research.

Try Atlas: Sign up for an evaluation sample (10 sources · 5 lifetime AI chats) and run a Knowledge Map on one of your own papers. Used by researchers at NUS, NTU, SMU, and eight other universities.

Comparing Atlas and Miro

Both Atlas and Miro touch a researcher's daily work, but they live in different categories. Atlas spans paper deconstruction, project navigation, source-cited AI answers, and compounding context across a research corpus, Miro spans collaborative whiteboarding with blocks, sticky notes, and templates. Miro's integration with team collaboration on a canvas is broader, Atlas's research depth at the citation surface is deeper. The rest of this article walks through the five capability surfaces where the two tools differ: per-paper deconstruction, project-level navigation, source-cited answering, literature-grounded annotations, and compounding context across projects. Each section is a two-column table where every row is a real capability, and at least one row in each table is one where Miro wins or ties.

Paper deconstruction (Knowledge Map)

The Knowledge Map is Atlas's per-paper surface. It deconstructs a single paper into a multi-level argument structure with labeled relations between claims, faithful-to-source nodes (the node text comes from the paper, not from a generated summary), and hierarchical breadcrumbs that let you read down from the high-level thesis to a specific paragraph.

Good to know: The bottom row belongs to Miro. Atlas does not ship that surface. The Knowledge Map's payoff is recovering a paper's argument three weeks after you first read it, when topic chips alone are no longer enough.

Project / corpus view (Semantic Map)

The Semantic Map is Atlas's per-project surface. It projects all the sources, notes, chats, and citations in a project into a spatial embedding where related items cluster by topic. Re-project the same canvas under a different topic angle without re-ingesting anything.

Good to know: Miro's strength on that row is genuine. If your work depends on it, that's the boundary. The Semantic Map's payoff is when 200 papers stop being a folder and start being a corpus you can re-project under different topic angles without re-reading.

Citation-grounded answers

Atlas produces claim-source-justification triples: the claim, the passage, and a one-sentence explanation of why the passage supports the claim. You can jump to the source paragraph, read the highlighted sentences, and check whether the reasoning holds.

Good to know: Both tools have a citation surface, the wedge is whether the surface explains why a passage justifies a claim, not just which passage was cited. For everyday Q&A the gap is invisible, for a thesis sentence or a brief paragraph it's the whole game.

Literature-grounded annotations

Atlas auto-annotates each paper on ingest. Citations inside the paper become first-class objects: Atlas resolves the cited source (when open-access), pulls the relevant passage, and lets you see how a citation in the paper builds up its argument across multiple sources without leaving the document.

Good to know: Literature-Grounded Annotations resolve citations inside the paper you're reading. When a paper cites a source that's open-access, Atlas pulls in the cited passage. It is not a web-grounding feature, it is a way to see how a single paper builds its argument across the sources it cites.

Compounding context across projects

Atlas builds a four-layer persistent graph (citations + mentions + KMs + SMs) across all your projects, so chats, annotations, and maps from one project become context for the next.

Good to know: Compounding is the slowest capability to demonstrate in a demo and the biggest payoff in week eight. If your work is many small, unrelated projects, Miro's session-isolated design is the right choice, isolation is a feature, not a gap. Compounding pays off for sustained, multi-month research.

Price comparison

Atlas is a paid product. There is no perpetual no-cost plan, you get a short evaluation sample (10 sources · 5 lifetime AI chats), and after that you pay $20/mo or $204/yr for Atlas Pro. At the paid tier, Atlas is the only tool with Knowledge Map, Semantic Map, claim-source-justification, and compounding graph. You aren't paying for chat tokens, you're paying for capabilities that Miro doesn't have at any tier.

When to choose Atlas vs Miro

• Want paper structure deconstructed multi-level? Go with Atlas. (Knowledge Map)
• Want answers that explain how each citation justifies the claim? Go with Atlas. (claim-source-justification)
• Want your projects to compound over months? Go with Atlas. (4-layer graph)
• Want a collaborative whiteboard with infinite canvas and team facilitation? Go with Miro.
• Tied: mapping out a literature-review structure with a teammate on a canvas**: both work fine, Miro for the collaboration surface and Atlas for the citation grounding. The wedge only opens up once you're building a corpus you'll return to.

Recommendations by user type

• PhD researchers: Atlas. Lit-review-heavy years 1–2 benefit most from the Knowledge Map (deconstruct each paper without re-reading). Thesis-writing years 3–4 benefit from claim-source-justification (every thesis sentence anchored to a passage). Miro works for one-off tasks, the multi-year compounding graph is what makes Atlas the right tool here.
• Students doing literature reviews and thesis research: Atlas, scoped to research workflows (dissertation, thesis, literature review). The Knowledge Map is the largest time-saver in the lit-review phase, and the compounding graph keeps prior work accessible across semesters.
• Knowledge workers (consultants, analysts, PMs, journalists): Atlas when reading and citing papers is the core work, Miro when group brainstorming and workshop facilitation are the daily need.
• Personal researchers with stakes (medical, legal, major-purchase, deep autodidact): Atlas. Burst-usage research where the stakes are high (medical, legal, major-purchase, deep autodidact) is exactly where citation-grounded reasoning earns its keep. Miro is a fine starting tool, Atlas is the tool you graduate to once you realize you'll need to defend the answer.

The honest one-liner across all four segments: if the research compounds, Atlas is the bet, if each session is self-contained and the next one starts fresh, Miro's form is genuinely the better fit, and we'll say so plainly. The expensive mistake is using a session-isolated tool for compounding work (every project pays the re-ingestion tax) or using a corpus tool for one-off questions where simpler tools are faster. A useful diagnostic: ask whether you expect to come back to the same corpus in three months. If yes, the project-graph approach carries its weight, if no, lighter tools win on friction. Most research workflows we hear from at universities (Cambridge, Harvard, MIT, Stanford) sit firmly on the "yes" side: the corpus is the same corpus across semesters, advisors, and grant cycles, which is the cohort Atlas is built for. The corollary is that picking the right tool is mostly a question about your work pattern, not a question about which feature list is longer, both tools do their job well within the form they're built for.

Migrating from Miro to Atlas

The honest migration story is that Miro and Atlas store different things, so a one-to-one import is not the right mental model. Miro's primary data model is an infinite canvas of sticky notes, frames, shapes, connectors, and embeds, often arranged into templates such as customer-journey maps, affinity-cluster grids, retrospectives, or sprint-planning frameworks. Atlas's primary data model is a corpus of source documents (PDFs and pasted text) that get deconstructed into Knowledge Maps per paper and a Semantic Map per project, with annotations and chats hanging off the source. The structures are not interchangeable, what carries across is the underlying text and the source files that the canvas was organized around.

Miro exports in three useful formats for migration: a CSV per board with the text content of each sticky note (and frame label), a PDF or PNG of the rendered board for visual reference, and a board.json backup if your plan exposes it. The CSV is the practical handle. Export it for any board that points to research sources, then upload the original PDFs (or DOIs) to Atlas. On ingest each PDF is deconstructed into a Knowledge Map automatically, which is the surface that replaces the manual sticky-note clustering you were doing on the canvas. If you used Miro stickies as ad-hoc annotations on a paper, paste them into the corresponding Atlas note attached to the source so the text isn't lost.

What does not migrate is the spatial layout itself (where each sticky sat on the canvas), embedded objects (Figma frames, web embeds, video links), real-time collaborator cursors and comments, third-party integrations (Jira tickets, Asana cards, Slack threads), and the visual identity of any custom-designed template. These belong to the whiteboard form and don't have an analogue inside a research corpus. If the spatial layout itself was load-bearing for your work (a customer-journey map that is the deliverable), keep the Miro board as a reference export (PDF) and use Atlas for the underlying source corpus that fed the canvas. Most teams who run both tools end up with this split by default.

A worked example: literature-review section from 8 papers

Imagine you're writing the related-work section of a paper, with eight uploaded PDFs that span three sub-topics. In Miro, the typical workflow is to open a fresh board, drag a sticky note for each paper, group them visually by sub-topic into three clusters, and then add sub-stickies underneath each paper for the claims you want to cite. The clustering is manual: you skim each paper, decide which sub-topic it sits under, and place the sticky. If two papers disagree, you draw a connector and label it "contradicts" yourself. A diligent pass on eight papers usually takes a long afternoon, and the output is a board you can screenshot but cannot ask questions of.

In Atlas, the same eight PDFs land in a project, and each is deconstructed into a Knowledge Map on ingest. The Semantic Map projects the eight papers into a spatial layout where related claims cluster automatically by topic, you can re-project the same canvas under a different topic angle (for example, "methodology" instead of "findings") without re-clustering by hand. When you ask "what are the three sub-topics covered across these eight papers, and which papers belong to each?", the answer comes back as a claim-source-justification triple per sub-topic: the cluster label, the papers it contains, and a one-sentence reason each paper belongs there, with jump-to-source links into the relevant passages. You verify each link in the source, accept or reject the grouping, and the next question ("which two papers disagree on the central claim?") draws on the same map.

The deliverable difference is that the Miro board is a static artifact: useful for a single read, hard to update when a ninth paper arrives. The Atlas project is queryable: drop the ninth paper in, regenerate the Semantic Map, and the new paper appears in the cluster it belongs to with its own Knowledge Map already built. The compounding payoff arrives later: when you start the next related-work section three months on, the eight papers and their Knowledge Maps are still there, citations resolved, prior annotations attached, ready to feed the next chat. Miro's session-isolated form means the next related-work section starts from an empty board.

The trade is honest. Miro's whiteboard is faster for a synchronous brainstorm with two collaborators sketching out a structure together at 3pm on a Tuesday, Atlas's structured surfaces are faster for the solitary read-and-cite pass that produces the actual paragraph of prose. Many researchers do both: brainstorm the structure with a teammate in Miro, then do the citation work in Atlas.

When Miro is the right call

Miro is the right tool when the work is fundamentally collaborative facilitation rather than corpus reasoning. Workshop facilitation with a distributed team, where the deliverable is the workshop itself (a brainstorm, an alignment session, a strategy offsite), lives on a whiteboard. The infinite canvas, voting, timers, and frameworks like 6-3-5 brainwriting or Lightning Decision Jam are first-class in Miro and absent in Atlas. Sprint planning and retrospectives are a similar story: the Start-Stop-Continue board, the sailboat retro, and the planning-poker template are designed for synchronous team rituals, and Atlas does not run team rituals.

Design-thinking sessions (empathy maps, journey maps, service blueprints) belong on a canvas because the spatial arrangement is the artifact: where the customer pain points sit relative to the service touchpoints matters for the reading. Large-team realtime collaboration with dozens of simultaneous cursors and live commenting is what Miro was built for, and Atlas's structured surfaces (per-paper Knowledge Maps, per-project Semantic Maps) don't compete on that surface at all. Customer-journey, business-model-canvas, SWOT, and Wardley-mapping templates are pre-built in Miro and are the right form for the strategy work they support.

The diagnostic: if your output is a canvas that a team will look at together (or a workshop that produces a canvas as a record), Miro is the better tool. If your output is a piece of prose backed by sources, Atlas is. Most researchers we hear from end up using both at different points in a project, which is the honest recommendation.

Common objections and edge cases

"We already use Miro for the entire research team. Won't introducing Atlas fragment our tooling?" A reasonable concern. The pragmatic split: Miro stays the team's collaborative canvas (workshops, planning, customer-journey maps), and Atlas becomes the corpus underneath when individual researchers are reading and citing. The two tools don't integrate directly (sources have to live in each separately for now), but the workflows don't conflict because they operate on different surfaces. Teams that have run both for six months tend to settle into "Miro for synchronous group work, Atlas for the citation-grounded reading and writing" without forcing a hard switch.

"Can Atlas's Knowledge Map replace the affinity-cluster boards we run in Miro after user interviews?" Partly, and only for a specific case. If the affinity clustering is happening over text sources (interview transcripts, support tickets, survey free-text), uploading those to Atlas and letting the Semantic Map cluster them by topic does replace a manual affinity board. If the affinity clustering is the workshop itself (a team in a room moving stickies together), Atlas doesn't run that ceremony and Miro is still the right tool. The wedge is whether the value is in the synchronous ritual or the eventual taxonomy.

"What about visual artifacts (diagrams, mood boards, design references) that aren't text?" Atlas is text-first. PDFs and pasted prose are first-class, images that carry semantic content (a system diagram, a process flow) can be uploaded but Atlas reasons over them less richly than over text. If your research is mostly visual reference material (design inspiration, screenshots, video stills), Miro's canvas is the better fit. The honest line: Atlas earns its keep when you're reading and citing, not when you're collecting and arranging visual references.