Rigorous depth, minus the busywork.
You bring the interpretation. Usedge handles the rest: live AI transcription, auto-tagged highlights, cross-study synthesis, and a governed repository where every finding compounds over time.
Trusted by enterprises that can't afford research slop
Why qualitative research doesn't scale without infrastructure
The bottleneck is not the researcher. It is the volume of administration between a participant's words and a decision-maker's action.
Drowning in data, starving for time
Twelve discovery interviews generate 18 hours of recordings, 140,000 words of transcripts, and hundreds of potential highlights. Most of that volume never gets properly analyzed because there simply is not time to read all of it carefully, repeatedly, and comparatively.
Manual transcription and tagging consumes the day
Transcription, even with existing tools, requires correction. Manual coding and tagging is subjective, inconsistent across researchers, and consumes hours that should be spent on interpretation. The work before the analysis is itself a project.
Cross-study synthesis misses patterns
Finding the signal across 50 interviews is qualitatively different from finding it across 5. At volume, patterns that emerge across participants in different studies are easy to miss without systematic tooling. The insight is there; the capacity to surface it is not.
Insights take time to translate to measurable outcomes
Research impact is real but diffuse and lagging. By the time a qualitative finding is connected to a product decision and that decision ships and the metric moves, the causal chain is hard to reconstruct. Research feels like a cost center because the link to outcomes is invisible.
Speed pressure threatens methodological quality
When stakeholders need answers quickly, rigor is the first thing to slip: smaller sample sizes, leading questions, less structured protocols. The researcher knows the finding is weaker; the stakeholder does not. The pressure to move fast and the commitment to moving carefully are in direct tension.
What changes when the busywork is automated
Researchers do more research, not more administration. The findings get sharper. The repository grows. The impact becomes traceable.
Time to insight
Session closed to themed, shareable findings, same day
Research throughput
Studies run per quarter when transcription and tagging are automated
Insight reuse
Past findings resurfaced and cited in new projects automatically
Synthesis time saved
Researcher time shifts from coding to interpretation and judgment
% findings linked to decisions
Insights traced from session to product decision to business outcome
Protocol quality score
Methodology scoring flags issues before data is collected, not after
Your role is interpretation. Usedge handles the rest.
Six capabilities that automate the administrative layer between a participant's words and a decision-maker's action.
Live AI transcription
Sessions transcribed in real time with speaker identification and timestamping. The researcher stays in the conversation; the transcript is complete by the time the call ends.
Auto-tagged highlights
AI extracts and tags the most significant moments from every session: pain points, insights, delights, blockers. The taxonomy is yours, the tagging is automatic, the review is fast.
Cross-study synthesis
Patterns that cut across dozens of sessions surface automatically. The Insight Synthesizer clusters themes across participants and studies, so the researcher sees the signal, not just the individual data points.
Methodology-scored protocols
Validated protocol templates with a methodology score before launch. The Protocol Evaluator flags leading questions, design gaps, and rigor risks before a single participant responds.
One repository for every study
All findings in one governed, searchable repository: atomic insights linked to their source session, tags, evidence, and trust level. Past research surfaces automatically in future projects.
Agentic workflows the researcher controls
Chain agents to automate post-session synthesis: Highlight Extractor to Taxonomy Manager to Insight Synthesizer. The researcher sets the workflow; the agents execute it; the output is review-ready.
The infrastructure layer for serious qualitative research
Four connected layers that take a research question from protocol to institutional knowledge, without losing the evidential chain at any step.
Protocol Builder
Methodology-scored templates built for every study type: discovery, usability, diary, concept test. The Protocol Evaluator checks rigor before launch. Start with structure, not with a blank page.
Learn more →Studies
Live moderated sessions with real-time transcription, and unmoderated tests at scale. Every session automatically captured, timestamped, and ready for the agent layer.
Learn more →Insights Repository
The institutional memory of your research program. Every atomic insight is searchable, reusable, and linked to its source session. Past findings surface in future projects. Research done once stays useful forever.
Learn more →Research Agents
Highlight Extractor, Taxonomy Manager, Insight Synthesizer, and Protocol Evaluator: chained or standalone. Post-session synthesis automated, under the researcher's control.
Learn more →Insights flow to where decisions are made
Push findings directly to Notion, Confluence, Slack, and Figma. The researcher does not copy-paste; the insight arrives where the team already works.
Agents built for the researcher's post-session workflow
Chain the first three together for fully automated post-session synthesis: Highlight Extractor outputs to Taxonomy Manager, which feeds the Insight Synthesizer. Review-ready output in minutes, not days.
Post-session agent chain
Highlight Extractor
Pulls verbatim highlights from transcripts and saves atomic insights to the repository.
Processes every session recording and transcript, extracts the most significant verbatims and moments, tags them against your taxonomy, and writes them as atomic insights into the repository over MCP. The researcher reviews, not transcribes.
Output
Tagged highlights · Atomic insights · Repository entries · MCP-synced
Taxonomy Manager
Audits the tag library, flags gaps and redundancies, keeps the taxonomy clean.
As the repository grows, tag drift happens: synonyms accumulate, categories overlap, orphan tags multiply. The Taxonomy Manager audits the full tag set, surfaces inconsistencies, and suggests merges and renames to keep the repository navigable at scale.
Output
Tag audit · Merge suggestions · Taxonomy health score
Insight Synthesizer
Clusters findings across studies and participants; surfaces themes and signals.
Works across the full repository (or a scoped set of studies) to identify recurring themes, patterns, and contradictions. Returns a structured synthesis with evidence citations, theme confidence levels, and a list of open questions for the next study.
Output
Theme clusters · Evidence citations · Confidence levels · Open questions
Protocol Evaluator
Methodology check before launch: flags leading questions and design risks.
Reviews draft protocols against established methodology criteria before a study goes live. Flags leading questions, insufficient participant criteria, missing consent language, and design gaps that would compromise the data quality.
Output
Methodology score · Specific flags · Pass / review / block status
Why Usedge, not a transcription tool that also does tagging
Generic AI tools accelerate the mechanics of research. Usedge changes the outcome: a growing, reusable, traceable repository of institutional knowledge.
Rigor-first AI
Methodology scoring, Protocol Evaluator, Trust Index on every insight. Speed that does not compromise the quality of the data it accelerates.
Quant and qual unified
Quantitative signals and qualitative evidence in one governed repository. One source of truth for the full picture.
The researcher stays in control
Agent workflows the researcher configures, reviews, and can override. Automation with judgment, not automation instead of it.
EU-hosted, GDPR-native
Participant data governed from day one: consent management, anonymization, right to erasure. Research done on real people, done lawfully.
12 discovery interviews to themed insight clusters, before Friday.
12 discovery interviews scoped and scheduled
The researcher picks the "Discovery interviews" template from the Protocol Builder. The Protocol Evaluator scores it 88/100 and flags one leading question for revision. Study is live within the hour, 12 participants scheduled over three days.
Q4 phrasing assumes prior experience; rephrase as an open question
From question to institutional knowledge in three steps
One workflow for moderated and unmoderated research, with a governed repository as the output, not a folder of files.
Step 1
Design with rigor
Pick a validated template from the Protocol Builder, or build your own. The Protocol Evaluator reviews your questions and design before a single participant responds, so the data you collect is worth analyzing.
Step 2
Run, capture, extract
Conduct moderated sessions or launch unmoderated tests. Live transcription and auto-tagging run throughout. When the session ends, the Highlight Extractor processes everything and writes atomic insights to the repository.
Step 3
Synthesize and share
The Insight Synthesizer clusters findings across participants and studies. The researcher reviews, refines, and publishes. Decision-linked, evidence-backed insights, shareable to any team or integrated tool.
Rigorous depth, minus the busywork
Live transcription, auto-tagged highlights, cross-study synthesis, and a governed repository where every finding compounds. The research infrastructure serious qualitative work deserves.