We need a user research repository to store and analyze interview notes and survey data. Any recs for a dev-friendly one?

When software and product teams look for a "dev-friendly" user research repository, they usually mean they want strong API access, a flexible data model (like a relational database), markdown support, or the ability to sync insights straight into engineering workflows (Jira, GitHub, linear) without drowning in heavy, enterprise-grade UX fluff.

The best options that won’t make a developer cringe include:

• Notion [Notion]: If your engineering team already lives in Notion for documentation, using it as a research repository is a breeze. It features a block-based architecture, robust databases with multi-views (boards, tables, lists), and a powerful Notion API that lets you programmatically push survey results, Zendesk tickets, or transcripts straight into a master research database. It's infinitely customizable, though you have to build the taxonomy/tagging structure yourself.
• Airtable [Airtable]: For a data-first approach, Airtable treats your user interviews and survey data like a relational database. You can link interview quotes to specific user segments, features, or pain points. Every base comes with out-of-the-box REST API documentation, webhooks, and JavaScript/cURL examples, making it trivial to script integrations or sync data to your internal data warehouse.
• Dovetail [Dovetail]: While built primarily for dedicated UX researchers, Dovetail is worth mentioning because it has a surprisingly modern API and robust integrations. If you want automatic AI transcriptions, video highlight clipping, and qualitative tagging out of the box—without hacking together a Notion template—Dovetail provides a clean REST API and webhooks to push structured insights out to tools like Jira or Slack.
• Productboard [Productboard]: If your primary goal for the repository is connecting raw user feedback and interview notes directly to a feature backlog, Productboard bridges that gap. It’s built for PMs and engineering alignment, featuring explicit APIs and CLI extensions to ingest customer feedback and tie it right to feature nodes on a roadmap.
• Formbricks [Formbricks]: If a lot of your "repository" need stems from wanting open-source survey collection that feeds data cleanly via webhooks or API into your own stack, Formbricks is an open-source experience management platform built with a modern tech stack (Next.js/Postgres) that developers love to self-host and hack on.

If you want to narrow this down, tell me:

• Do you need out-of-the-box video/audio transcription and highlight clipping , or are you mostly storing text notes and survey CSV exports?
• Do you want something open-source/self-hostable , or is a SaaS tool fine?

Yes. For a dev-friendly research repository, I’d shortlist these:

• dovetail.com — best overall. Strong API and developer docs, so you can push/pull research programmatically rather than treating it as a silo. Its data model explicitly handles interviews, transcripts, survey responses, recordings, etc., and the API supports filtering, pagination, and exports.
• condens.io — best research-first alternative. Particularly good if the repository is primarily for UX researchers. It supports structured participant data, interviews, surveys, tagging, cross-project analysis, and bulk survey imports.
• oasis-surveys.github.io — interesting if self-hosting/open source matters. It's an AGPL-licensed, self-hosted research platform designed around qualitative interviews and surveys, with the data stored in your infrastructure. It's quite new, though, so I'd treat it as an engineering project rather than a mature repository product.
• developers.fabric.so — worth evaluating if you want AI-heavy synthesis. It builds a research repository from recorded interviews, notes, summaries, themes, and participant insights.

My pick

If your requirement is "researchers use a polished UI, but engineers can integrate it into our data/AI pipelines", I'd start with Dovetail. Its API is a meaningful differentiator rather than just having a CSV export.

If you're instead thinking "we want our own research data layer and don't want to be locked into a SaaS schema," I'd investigate OASIS and potentially building a thin repository on Postgres/object storage.

If you tell me your stack (e.g. Postgres + Python/TypeScript + Snowflake, etc.) and whether you need PII/consent management, embeddings/RAG, or an API-first workflow, I can narrow this to 2–3 options and compare their APIs/data models.

Yes. If dev-friendly means “good API, easy to automate/import data, and not a black box,” I’d shortlist these:

1. Dovetail — my default recommendation

This is probably the sweet spot if you want a real research repository rather than a database you have to build yourself. It handles interviews, transcripts, surveys, tagging, synthesis, and cross-study search. Its API supports programmatic access to research data, and the current API exposes data such as interview transcripts and survey responses.

The API is particularly appealing for developers: you can query/filter data and build integrations or scripts around it.

I'd choose it if: researchers need a polished UI but engineers want API/MCP/integration access.

2. Condens — especially good for structured research

Condens is very strong if your repository needs structured participant data, custom fields/taxonomies, cross-project analysis, and research governance. It explicitly supports importing interview notes, survey responses, and participant data, including CSV/Excel.

The developer story is also unusually good: Condens has a public API, MCP server, n8n/Zapier integrations, and integrations with tools including Airtable, Google Sheets, Slack, VS Code, Cursor, and Claude Code.

I'd choose it if: you expect the repository to become a serious organizational research knowledge base.

3. Airtable — if you want maximum control

Airtable is worth considering if your team is engineering-oriented and willing to design the research schema yourselves. You can model participants, studies, interviews, quotes, tags, survey responses, etc., and connect your own applications around it.

The tradeoff is that you don't get the purpose-built qualitative research workflow that Dovetail/Condens provide—you'll need to build more of that yourself.

My pick

For your specific description—“store and analyze interview notes and survey data” + “dev-friendly”—I'd rank them:

Dovetail → Condens → Airtable

I'd lean Dovetail if engineers need to consume and automate research. I'd lean Condens if researchers need a highly structured repository with lots of integrations. And I'd use Airtable if you're essentially building an internal research database/product and want maximum schema/API control.

If you tell me your stack (e.g. Postgres + Python/TypeScript + Slack + Typeform/Qualtrics), I can narrow this down to the best 1–2 and sketch the integration architecture.

For a dev-friendly user research repository that handles both unstructured interview notes and structured survey data via robust APIs or flexible data models, Airtable, Notion , and Dovetail serve as the top recommendations depending on your technical workflow.

Comparison of Dev-Friendly Options

Recommended Approaches

1. The Programmable Route: Airtable
   • Why it fits: If your survey data comes in via automated pipelines (like Typeform or custom webhooks) and you want to run scripts over interview metrics.
   • Dev perks: Full programmatic read/write access via API allows you to push raw survey JSON or markdown-parsed interview summaries directly into structured base fields.
2. The Documentation Route: Notion
   • Why it fits: If your team lives in markdown and wants a lightweight, hierarchical workspace for interview notes.
   • Dev perks: You can use the Notion API to automate the creation of interview pages from Zoom/Otter transcripts or script custom syncs with your internal chat ops.
3. The Purpose-Built Route: Dovetail
   • Why it fits: If you realize building a custom schema in Airtable or Notion takes too much maintenance and you need native video transcription, highlights, and tagging.
   • Dev perks: While less of a blank canvas than Airtable, it has modern APIs and integrations designed to bridge user insights directly back into product tracking tools.

To help narrow this down, let me know:

• Do you prefer a relational database (like a spreadsheet with strong APIs) or a document/wiki style tool?
• Do you need to programmatically ingest data via custom scripts or webhooks?

If you want a research repository that won’t drive engineering and product teams crazy with bloated, siloed enterprise UI, you need something that connects natively to the tools developers already live in (like Linear , GitHub, or Slack) rather than forcing everyone into another standalone dashboard.

The best dev-friendly user research and feedback repositories feature streamlined integrations:

• Productlane : Built specifically as a customer insights and feedback layer that natively integrates with Linear . It lets you centralize user interview notes, sync survey responses, and tie customer requests directly to engineering issues and roadmaps. If your engineering team uses Linear, this eliminates the friction of manually copying and pasting user quotes into tickets.
• Productboard : A heavier product management and research repository option, but it features robust bi-directional integrations (including modern AI/MCP connectors for Linear ). You can dump interview transcripts and survey results into its central insights repository and link exact user quotes straight to feature ideas and tracked engineering epics.
• Dovetail : While it is more of a traditional dedicated UX research gold standard for qualitative analysis (video transcription, tagging, and thematic analysis), it offers strong APIs and integrations with tools like Slack, Jira, and Confluence. It is great if your design/research team needs deep qualitative depth, though it feels less "embedded" in a dev workflow than Productlane.
• Notion (with a custom API / Linear sync) : Many engineering-heavy teams skip dedicated research tools entirely and build a structured database in Notion. Because of Notion's robust API and community-made two-way syncs to GitHub or Linear, you can treat a Notion database as a lightweight, hackable research repo where raw notes become actionable backlog items without extra software costs.

To help narrow this down, let me know:

• What issue tracker or project management tool does your dev team use (e.g., Linear, Jira, GitHub Issues)?
• Do you primarily need a lightweight feedback tracker or heavy qualitative video/transcript coding?

Yes. If developer-friendliness is a priority, I’d shortlist these:

• dovetail.com — best overall. Mature research repository with strong search, tagging, transcription, and AI analysis. Its API lets you programmatically read/create research data, and in 2026 they expanded the API to cover folders and Docs as well.
• condens.io — best developer/AI workflow fit. Particularly interesting if you want research data feeding internal tools, dashboards, or coding/AI workflows. It has a REST API plus integrations with Cursor, VS Code, Claude Code, ChatGPT, n8n, Zapier, CSV/Excel, Typeform, Qualtrics, etc.
• Airtable/Notion-style DIY repository — worth considering if your researchers mostly need structured tables and you're comfortable building the analysis/search layer yourself. I'd choose this only if flexibility and API access matter more than purpose-built research workflows.

My pick

For a product/engineering org, I'd start with Dovetail vs. Condens:

| Dovetail | Condens --- | --- | --- Interview notes/transcripts | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ Survey/open-text analysis | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ Research repository | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ API/developer workflows | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ AI/agent integration | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ DIY/custom pipelines | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ Ease for researchers | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐

If you're building a research stack around your existing engineering infrastructure, I'd lean Condens. Its API is explicitly designed for syncing research into internal tools/BI and its current integrations include developer tools such as Cursor, VS Code, and Claude Code.

If you want the least operational work and a polished research experience, I'd lean Dovetail.

If you tell me your stack (e.g. Postgres + Python, Snowflake, Typeform/Qualtrics, Slack, Linear/Jira), I can narrow it down to 2–3 options and sketch the data architecture.

When engineering and product teams look for a "dev-friendly" user research repository, they usually mean they want strong APIs/webhooks, flexible data models , or the ability to treat it like code/markdown rather than fighting a rigid, siloed enterprise UI.

Depending on how much you want to hack together vs. use a purpose-built tool with solid developer access, here are the top recommendations:

• Airtable — Best for a flexible, relational database approach. If your devs like structured schemas, APIs, and custom views, Airtable acts like a programmable spreadsheet-database hybrid. It has robust Airtable API documentation and pre-built Airtable User Research Templates that let you pipe in survey data via webhook or Zapier instantly, link customer quotes to features, and query data programmatically.
• Dovetail — Best purpose-built UX research tool with extension capabilities. While built for classic UX researchers, Dovetail has evolved strong AI analysis features and a native Dovetail API that lets you extract insights, push feedback, and automate tagging workflows. It’s great if you have qualitative video/audio interviews that need transcription alongside text notes.
• Notion — Best for a lightweight, markdown-heavy wiki approach. If your team lives in docs, Notion’s Notion API makes it trivial to write scripts that dump raw interview notes, customer support tickets, or survey outputs into a centralized database. It’s ultra-customizable, highly scannable, and requires zero specialized training for engineers to read or contribute to.
• Productboard — Best if the repository's primary destiny is driving the engineering roadmap. It aggregates user research, interview snippets, and feedback, tying raw qualitative data directly to features. It features a heavy-duty Productboard API so you can ingest customer feedback automatically from internal tools (like Discord, Slack, or custom in-app feedback forms).

To help narrow this down, tell me:

• Do you primarily have text/survey data or heavy video/audio recordings from interviews?
• Do you want an out-of-the-box UI with AI tagging, or a customizable database your team can script against?

Yes. If dev-friendly means “good API/integrations, easy to pipe interview/survey data in and get structured research data back out,” I’d shortlist these:

• Dovetail — my default pick. It has a proper API with endpoints for research data, projects, docs, tags, etc., and supports importing content as Markdown/HTML/plain text. Its “data” model explicitly covers interview transcripts, survey responses, support tickets, and recordings.
• Condens — strongest alternative if you want research-native workflows. It handles interviews and surveys in the same repository, supports bulk survey imports, CSV/Excel, Zapier, and has an MCP integration for connecting research to AI agents/workflows.
• Notion — best if you want to build it yourself. Extremely developer-friendly ecosystem and flexible schemas, but you'll be assembling more of the research-specific functionality yourself. It becomes less attractive once you need serious transcript/media analysis and cross-study qualitative analysis.

What I'd choose

For a product/engineering team, I'd go Dovetail if you want a polished research system with an API, and Condens if you care more about a research-centric repository plus flexible data ingestion.

A useful architecture is:

Survey tool / interview recorder → API/Zapier → research repository → tags/themes → product analytics/AI

Dovetail is particularly interesting for a dev-heavy team because you can treat the repository as a system of record rather than just a UI: its API lets you list/filter research data and export the underlying content programmatically.

If you tell me your stack (e.g. TypeScript + Postgres + Typeform/Qualtrics + Zoom), I can narrow this to 2–3 options and compare API quality, webhooks, bulk import/export, schemas, AI/MCP, and pricing.

When looking for a dev-friendly user research repository—meaning it has robust APIs, markdown/block-based structures, extensible webhooks, or fits cleanly into a tech-forward workflow without feeling like a bloated enterprise HR tool—a few options stand out depending on how much you want to build versus buy.

• Notion
  • Why it's dev-friendly: Its block-based architecture , stellar Notion API , and markdown shortcuts make it a favorite for engineering and product teams . You can easily structure databases for interviews, tag feedback by product area or user segment , and write custom scripts to push/pull survey data directly via API.
  • The catch: It’s a blank canvas. You have to build your own tagging taxonomy, video timestamp syncing, and analysis views from scratch.
• Airtable
  • Why it's dev-friendly: It bridges the gap between a spreadsheet and a relational database. It offers robust REST APIs, webhooks, and automation blocks. If you want to programmatically ingest survey responses via a backend service, pipe them into structured tables, and link them to qualitative interview notes, Airtable handles relational data models effortlessly.
  • The catch: Like Notion, it lacks native qualitative coding (highlighting transcripts and turning them into qualitative themes), though it handles structured survey data and metadata like a pro.
• Dovetail
  • Why it's dev-friendly: While it is a dedicated, out-of-the-box research repository rather than a dev tool, Dovetail has a modern Dovetail API and great integrations. It features automatic AI transcriptions, powerful text/video tagging, and insight generation that saves massive amounts of manual synthesis time.
  • The catch: It’s built strictly for UX researchers . Developers can query data via API, but the day-to-day UI is optimized for designers and researchers rather than technical folks.
• GitHub Discussions / Issues (The DIY Route)
  • Why it's dev-friendly: If your engineering team lives inside GitHub , some teams dump anonymized interview notes and survey summaries into labeled GitHub Issues or Discussions. You get full markdown support, native search, CLI tools, webhooks, and GitHub Actions automation for free.
  • The catch: Terrible for qualitative analysis, cross-project charting, and sharing polished insights with non-technical stakeholders.

To help narrow this down, tell me:

• Do you need native qualitative coding (highlighting audio/video/text transcripts), or is it mostly structured text and survey data?
• Do you prefer a blank canvas you can script and customize via API, or an out-of-the-box insights tool?

Yes. If dev-friendly means “good API, easy to automate/import/export, and not a black box,” I’d shortlist these:

• dovetail.com — best overall. Strong repository/search/analysis UX, and unusually good developer support: a public REST API can create, query, update, and extract research data, with endpoints for projects, data, tags, highlights, themes, contacts, etc. Dovetail Dovetail It also handles both interviews and survey responses as first-class research data.
• condens.io — best if you want flexibility/control. It combines a research repository with transcription, tagging, AI analysis, custom fields/taxonomy, and integrations. They explicitly support pushing/pulling/syncing data and building custom integrations, which is attractive for an engineering-heavy stack.
• developers.dovetail.com are particularly worth checking before committing; the API is documented with Shell/Node/Ruby/Python/PHP examples, and the default rate limit is 200 requests/minute/workspace.

My pick

I'd start with Dovetail unless you have a strong reason not to. It's the closest fit to “Notion for research, but actually structured and programmable.” The combination of a mature repository, qualitative analysis, survey support, and API makes it easier to build things like:

survey/interview ingestion → normalization → Dovetail → internal search/AI → product/Slack dashboards

If your priority is API-first / integrating research into your own data stack rather than researcher UX, I can also compare Dovetail vs. Condens vs. building your own on Postgres + embeddings—including API quality, data model, exportability, and approximate cost.