Sharpr for UX Research

A product manager proposes a study. You recognize the question, because a version of it was answered over a year ago by a researcher who has since moved to another team. The findings exist. They are in a deck, a set of session recordings, a Miro board, and a summary doc, spread across four tools the PM was never going to search.

So the study runs again. Six weeks and a recruiting budget to reach a conclusion the organization already owned.

We built Sharpr to give research a durable home that the rest of the company will use. Findings, session recordings, discovery reports, usability results, and the market context around them in one secure searchable hub, with AI summaries and automatic organization, pushed to the people who need them rather than waiting to be discovered.

Why research repositories usually underdeliver

Tagging debt. Most repository approaches ask researchers to manually tag findings against a taxonomy. It works for a few months. Then a deadline hits, tagging slips, the taxonomy drifts, and inside a year the thing is inconsistent enough that nobody trusts it.

It is a room nobody enters. Product managers, designers, and engineers do not log into research tools. They ask a researcher or they guess. A repository that requires people to come to it will serve the research team and precisely nobody else.

UX research sits apart from everything else. Usability findings live in a research tool. Market research lives with insights. Competitive analysis lives with product marketing. Support themes live in a service platform. Anyone trying to understand a user problem has four systems to visit, so they visit one and form an incomplete picture.

Researchers become search engines. A meaningful share of senior researcher time goes to answering "did we ever look into" questions. Expensive work that produces no new knowledge.

Impact is hard to prove. When headcount gets questioned you reach for anecdotes. Usage data would be more persuasive and most repositories do not produce it.

What we do differently

Organization happens on ingest, not through discipline

We automatically process, categorize, and connect content as it arrives, generating summaries, relevant tags, and contextual links between related material. A usability report, the session recordings behind it, and last quarter's discovery study get connected without anyone maintaining a taxonomy by hand.

That removes the dependency that kills most repositories. Organization does not degrade when your team gets busy, which they will.

Every artifact type in one place

Research output includes video as well as documents. We centralize documents, reports, videos, and articles, so session recordings and highlight reels sit next to the written findings, the survey results, and the competitive teardowns. Nothing has to be flattened into a summary in order to be stored.

External material comes in through Sharp It, our browser bookmarklet. One click and a page is in the hub. Researchers use it for competitor UX patterns, accessibility guidance, design system references, published studies, and benchmarks.

Natural language search, which is what non-researchers need

Our GenAI search takes plain questions. A PM can ask what users have said about the onboarding flow. A designer can ask whether the team has tested this navigation pattern. An engineer can ask what accessibility issues have come up in this part of the product.

None of them need to learn a taxonomy or guess a filename. AI summaries let them judge relevance in seconds. This is the mechanism by which research democratization happens, rather than getting announced in an all-hands and then not happening.

Push findings out instead of waiting

Our briefs, newsletters, and alerts send research to people, personalized by role. What UX teams run:

• A monthly research digest to product and design, cut by product area so each team sees what is relevant.
• Alerts to a product team whenever new research lands on their surface or feature area.
• A quarterly brief to executives connecting research themes to business outcomes, assembled from work already in the hub.
• A pre-kickoff brief for a new initiative, gathering everything the organization already knows about the problem space before the first planning meeting.

That last one is the highest leverage thing on this page. Delivering existing evidence at the start of a project is the most reliable way to prevent redundant research downstream.

Connect UX findings to the wider knowledge base

This is where we differ from a dedicated research repository. Because Sharpr is an enterprise knowledge platform, usability findings sit alongside market research, competitive intelligence, support analysis, and strategy documents. When a PM searches a user problem, they get the UX evidence, the market context, and the competitive picture together.

That changes how findings get received. Research presented with market and competitive context is much harder to wave off as a small sample, and it connects more directly to the decisions leadership is making.

Analytics that answer the headcount question

We report on what gets used, by which teams, how often. Research leaders use it to show which studies influenced which product areas, which beats a list of completed projects every time.

The same data feeds prioritization. Search terms that return little content tell you where the organization believes it needs knowledge and does not have it, which is a better roadmap input than a stakeholder request queue.

Three concrete uses

Pre-kickoff evidence packs. A team is starting on a checkout redesign. Before kickoff, the research lead runs a search and assembles a brief: three prior usability studies on the flow, support ticket theme analysis, the competitive teardown, the drop-off analysis, and the accessibility audit. The kickoff starts from what is known, and new research narrows to genuine gaps. Cheaper for the company and more interesting for the researcher.

Stopping a duplicate study. A PM requests research on feature discoverability. A search finds a study from eleven months ago that answered it, plus two sessions from a different study where the same behavior appeared. The answer arrives that afternoon rather than in six weeks. This is the scenario most teams hit first, and it repeats.

Onboarding onto an unfamiliar product area. A new hire joining a mature product reads the hub for that area: foundational discovery, persona work, usability history, open questions. Instead of a month of meetings, they arrive at their first planning session with real context.

Governance, access, and participant data

Research carries obligations around participant privacy and consent, and we take that seriously. Role-based access runs across hubs and individual content, so raw session recordings and identifying material stay restricted to the research team while findings and highlight reels circulate more widely.

GDPR compliant, ISO 27001 compliant, SOC 2 aligned cloud infrastructure, enterprise DPAs available. SSO through ADFS, Okta, SAML 2.0, or Google Workspace, plus custom SSO through our API. SCIM 2.0 provisioning, bulk import, and lifecycle and offboarding workflows, so access to participant material ends cleanly when someone leaves.

AES-256 at rest, TLS 1.3 in transit, logical tenant isolation, encrypted backups, DDoS protection, redundant servers, annual disaster recovery testing, and 24/7 monitoring.

How we compare, honestly

Against a dedicated research repository. Purpose-built repositories handle the research workflow well and they are designed for researchers, which is a real advantage. The limitation is scope: they hold UX research and nothing else, so the PM still has to visit other systems for market and competitive context, and in practice does not. We trade some research-specific workflow features for enterprise reach. If your goal is getting findings used outside the research team, that is the right trade. If your goal is a better tagging and analysis workflow for researchers alone, it may not be.

Against a wiki or shared drive. They store files without summarizing, connecting, distributing, or reporting on usage. Fine until the volume grows, then write-only.

Against general enterprise search. Those index everything, including a lot of low-signal material. We work over a curated environment, so the results reflect deliberate collection.

Against doing nothing. The status quo has a measurable cost: the studies run twice, the researcher hours spent on retrieval, and the decisions made without evidence that already existed.

For ResearchOps

Your questions are practical. How much maintenance does this create, how does the back catalogue get in, and will anyone use it.

Maintenance is low because organization happens on ingest rather than through a process your team has to sustain. The back catalogue comes across free of charge, with our migration team moving content, structure, users, permissions, and archives from whatever you use now. Adoption is handled by the push side, because stakeholders receive briefs and alerts without ever opening the platform.

Every client gets a dedicated customer experience manager and role-specific training, so researchers and stakeholders learn the parts they will use.

Try it against a real study

Take something your team is about to run and ask what the organization already knows about it. Request a demo and bring that study. If the answer already exists, you will find out within the hour.