Move your team from Dovetail to Notably.

Both tools automatically extract themes and patterns from raw qualitative data, making this the most direct capability swap. In Dovetail, AI analysis runs inside Projects and Channels: it classifies feedback, clusters themes, and quantifies frequency — no manual tagging required. In Notably, the same job is done by Posty (the built-in AI assistant), which proposes highlight and tag suggestions on transcripts or imported data, then clusters accepted tags into themes. To move from Dovetail to Notably: export your Dovetail project data (CSV or JSON via API), import raw transcripts or notes into a Notably project, then let Posty run its initial tagging pass; review and accept/edit suggestions to train the model. To move from Notably to Dovetail: export highlights and tags from Notably, create a Dovetail project, upload the source documents, and trigger AI analysis to re-derive themes independently. Because Posty learns from user corrections over time, teams switching to Notably should budget a training period of 3-5 studies before tag accuracy stabilizes.

Both tools let researchers surface patterns and connections across accumulated research data, though through different interaction models. Dovetail's AI chat lets any team member ask natural-language questions ('What do customers say about onboarding?') and get cited answers drawn from all stored feedback and research sessions. Notably's pattern analysis canvas achieves a similar goal visually: tagged excerpts from across studies appear as sticky notes that can be clustered, filtered, and recolored to expose patterns. Migrating from Dovetail AI chat to Notably canvas: export Dovetail data, import into Notably projects (one project per original study), then use Notably's cross-project global search (Pro and above) to pull relevant excerpts onto a new canvas and cluster them. Migrating the reverse: export Notably highlights as a report or CSV, upload to Dovetail, and use AI chat to query across the imported documents. Neither product's cross-study search requires re-tagging if you migrate raw source files alongside their metadata.

Both tools centralize multi-source qualitative data, but with different emphases: Dovetail Channels are aimed at ongoing, high-volume feedback streams (support tickets, reviews, surveys), while Notably's research repository handles project-based uploads (interviews, audio, video, whitepapers, notes). Migrating from Dovetail to Notably: stop or pause active Channels, export historical data from each Channel as CSV or JSON, then create matching Notably projects and import the exported files as documents or notes. Audio/video files that were linked in Dovetail should be uploaded directly into Notably for transcription. Migrating from Notably to Dovetail: export project data from Notably (highlight reports or raw files), upload to a Dovetail project, then configure Dovetail Channels pointing at the same upstream sources (Intercom, Zendesk, etc.) going forward. Because Dovetail Channels ingest live and auto-classify, teams moving there will benefit from configuring segments and initiatives immediately after connecting a source.

Both tools allow researchers to package findings into a polished, shareable deliverable for stakeholders who do not need full product access. Dovetail AI Docs generate structured documents (summaries, briefs, reports) automatically from feedback inside a Project, shareable with unlimited read-only viewers at no extra seat cost. Notably's insight sharing compiles highlights, tags, and summaries into interactive insight collections — shareable via link to external viewers — with AI-generated images for visual polish. To migrate from Dovetail AI Docs to Notably: export Dovetail-generated documents as PDF or copy content into Notably as notes, then build an insight collection by pulling in the relevant highlights and tags from the imported research; share the collection link with stakeholders. To migrate from Notably to Dovetail: export Notably insight collections or highlight reports, import as documents into a Dovetail project, and use AI Docs to regenerate a structured brief from the underlying data. Dovetail is better for automated report generation at scale; Notably is better for visually rich, client-facing presentations.

Both tools provide mechanisms for teams to standardize how research data flows into and through the platform. Dovetail does this via integrations: connecting Slack, Salesforce, Microsoft Teams, and other sources so data arrives consistently structured and digests are distributed automatically. Notably does this via research templates: curated (and customizable) templates that govern how data is collected, coded, and analyzed within each project, ensuring consistent tagging frameworks across studies. When moving from Dovetail to Notably: document the Dovetail integration configuration (which sources feed which channels, what segments exist) and translate these into Notably project templates that pre-define tagging categories matching those segments. Custom AI templates in Notably (Teams/Enterprise) can encode the same classification logic Dovetail was applying at ingestion. When moving from Notably to Dovetail: review existing Notably templates to understand the tagging taxonomy, then configure Dovetail Channels and integrations to map to the same themes and segments, and set up Slack/email digests to replace Notably's sharing workflow.