reOS
Summary
Research findings rot in slide decks presented once, then never acted on — while the next team re-runs the same study a quarter later because nobody could find the first one. reOS is built to break that cycle, treating the report as the halfway point rather than the finish line.
The platform covers the full research lifecycle in one system: study planning with an AI-assisted moderation guide, participant recruiting from a CRM-connected panel, live and async interviews, multi-channel feedback ingestion, and a structured analysis pipeline that traces every insight back to its exact source. The differentiating bet is the Act stage — insights become tracked initiatives, the customers who requested a change get notified when it ships, and follow-up studies launch from the same record. That closed-loop model works well for teams already drowning in disconnected tools. The ceiling appears when teams need analysis logic or reporting structures that fall outside the DIVE™ pipeline's templates — customization is available but you're building within reOS's model, not around it.
Bottom line: Pick reOS if your research keeps dying between the readout and the roadmap — but plan for workarounds if your analysis workflow requires bespoke reporting structures the platform's templates cannot express.
Pricing Plans
Usage-Based- Free Tier
- Unlimited viewers always free
Researcher
For individuals and small teams. $15 usage credits/mo. Up to 20 projects, 3 seats, 500 panel contacts, 10k items & 20h video.
- Full research loop
- Top-up credits available
Team
For growing teams. $60 usage credits/mo. Unlimited projects, 15 seats, 5k panel contacts, 100k items & 200h video.
- Shared panel and repository
- Everything in Researcher
Enterprise
Custom credits and rates. SOC 2, GDPR, PII removal, SSO, white label.
- Tailored volumes
- Compliance features
View full pricing on reos.ai →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- End-to-end pipeline from study planning through participant notification, so research findings don't stall between the readout deck and the product team's backlog.
- Multi-channel feedback ingestion — support tickets, forwarded emails, forms, recordings, transcripts, and raw datasets all flow into the same repository — which means insights from five different sources can be compared and traced without manually merging exports.
- DIVE™ pipeline validates every AI-generated claim against its source before surfacing it, so you're not shipping insights that evaporate when a stakeholder asks where they came from.
- Model-agnostic analysis layer with custom template support, so teams can swap the underlying AI model or build analysis flows that match their own research methodology rather than adapting to a fixed one.
- Usage-based billing with free unlimited viewers, which means stakeholders who only read reports don't inflate your seat count the way per-seat pricing does on every competitor.
Cons
Sign in to edit- Analysis customization runs through reOS's own template and flow system — teams with reporting structures that diverge significantly from the platform's model end up building workarounds inside reOS rather than alongside it, which means the 'one system' value proposition starts to erode as requirements get more specific.
- No self-hosted option, so organizations with data residency requirements or policies against third-party cloud processing for research recordings cannot deploy reOS at all — that's the condition under which teams abandon it for self-hosted alternatives regardless of workflow fit.
- The Act stage's closed-loop value depends on CRM connectivity and participant panel data being accurate and maintained — teams without a clean CRM or structured panel get a research repository without the notification and follow-up features that distinguish the platform from a standard analysis tool.
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About
- Platforms
- Web
- API Available
- No
- Self-Hosted
- No
- Last Updated
- 2026-07-03T14:46:40.563Z
Best For
Who it's for
- Research teams needing structured workflows
- Organizations managing participant panels
- Teams requiring traceable analysis
- Users wanting metered usage billing
What it does well
- Planning and running customer interviews
- Analyzing feedback from multiple channels
- Tracing insights back to source data
- Generating reports and syncing initiatives
Integrations
Discussion Community
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Frequently Asked Questions
- Is reOS free?
- reOS has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is reOS open source?
- No — reOS is a closed-source tool. Source code is not publicly available.
- What platforms does reOS support?
- reOS is available on: Web.
Hours Saved & ROI Stories Community
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Curated lists that include this category
Research teams waste weeks stitching together recruiting tools, scheduling apps, transcription services, analysis repositories, and reporting decks — and after all of that, the findings still go nowhere actionable. reOS replaces that stack with a single workflow: an AI planning assistant turns a business question into a study plan and moderation guide; a panel recruiter handles outreach, scheduling, consent, and incentive payments; live and async interviews run inside the platform; and every piece of feedback — tickets, forwarded emails, forms, uploaded recordings, whole datasets — flows into a central repository that transcribes and structures everything automatically.
The core technical differentiator is the DIVE™ analysis pipeline: four sequential stages (Decode, Improve, Validate, Enrich) where each stage audits the last, designed to catch AI hallucinations before they reach an insight. Every validated claim is traceable to its source — click an insight and the platform lands you on the exact second of the interview or the exact line of the ticket. The vendor states you choose which AI models run the analysis and can build custom analysis templates, with the default models benchmarked continuously against each other.
reOS fits organizations where research currently ends at the report: teams that struggle to track whether a fix ever shipped, whether the customer who raised the issue was ever told, or whether a follow-up study was ever run. The Act stage converts insights into tracked initiatives and surfaces the customers who asked for the change — which is a workflow most research tools simply do not have. Where it breaks: teams with highly specialized analysis or reporting requirements will hit the boundaries of the platform’s template and flow system. At that point they are either requesting features or maintaining a separate layer, which is the condition under which teams historically move to a modular stack of best-of-breed tools stitched together by an internal data pipeline.