Everything between your question and the paper.
You were trained to design studies and defend findings. Somewhere in there, someone decided you should also be a database administrator, a data protection officer and a full-stack developer. That part is ours.
Eleven ways in, all held to the same discipline.
Four products of our own. License them, or let us host them where your policy requires.
Your interviews never leave
Research interviews and clinical audio transcribed inside your own perimeter: speaker separation, Danish and other languages, timestamps, and spoken names masked on the way in.
Explore TRANSCRIPTION → Viceron CORPUSCoding your team can do together
An alternative to single-seat desktop software: one shared codebook, the whole team coding at once, agreement calculated as you go, and AI first-pass coding you can overrule.
Explore CORPUS → Viceron STATLABAsk for the analysis. Get the code.
Describe what you want to know in a sentence. It returns the result, the method paragraph, and the R or Python that produced it — so you can check every number yourself before it reaches a manuscript.
See it work → Viceron COHORTParticipants who actually finish
Questionnaire studies at national scale: adaptive instruments, PROMs, thirty languages, a reminder ladder that knows when to stop, and per-item analytics that name the question losing you people.
Explore COHORT →Seven kinds of engagement, from a two-hour review to being the data management provider of record on an international trial.
The piece nobody sells
Participant apps, screening tools, study websites, clinician dashboards, wearable and device ingestion — the small software a protocol needs and no off-the-shelf product covers.
Explore custom builds → Teaching & trainingThe goal is that you need us less
Courses for research groups and departments in database design, applied statistics, reproducible analysis, governance, qualitative method and review method — taught on your own data.
Explore teaching → REDCap, databases & biobanksA database that fits the protocol
Instrument design, longitudinal events, branching logic, randomisation, e-consent, roles and audit trails — plus sample and biobank management, and registries built to outlive the grant.
Explore databases → Secure data handling & transferSensitive data, handled properly
Pseudonymisation, de-identification, synthetic data, audited access, encrypted transfer and hosting that can sit entirely inside your institution's perimeter. Written up so your DPO can sign it.
Explore data security → AI, big data & interoperabilityThe analysis that would have taken months
Prediction models reported to TRIPOD+AI, structured variables pulled out of clinical free text, imaging and annotation, federated learning, OMOP and terminology mapping, real-world evidence.
Explore AI & big data → Clinical trialsTrial machinery, without the vendor
Investigator-initiated and international trials: eCRF, randomisation and blinding, e-consent, monitoring views, GCP-aligned validation, and the CONSORT diagram falling out of the data.
Explore trials → Systematic reviews & evidence synthesisThe review that finishes this year
Protocol and registration, a peer-reviewed search strategy, AI-assisted dual screening with agreement statistics, risk of bias, meta-analysis, and a PRISMA flow generated from the screening record.
Explore evidence synthesis →Bring the awkward one
Our favourite briefs are the ones that do not fit a category. If your problem needs three of the above and a piece that does not exist yet, that is a normal Tuesday.
Describe it→Engage us the way your funding allows.
Academic money has rules, timing and reporting lines. We have four shapes that fit them, and we will tell you which one is cheapest for you.
Grant partner
We join the application. Work package, budget, methods text, and a named technical partner the panel can evaluate.
- Costed before submission
- Co-applicant or subcontractor
- No cost if the grant fails
Fixed-scope build
A single deliverable at an agreed price and date: a database, app, platform or analysis package, specified tightly enough to be quoted honestly.
- Fixed fee, fixed date
- Source code and documentation to you
- Optional operations afterwards
Retained support
A statistician and an engineer on call for a research group or department. Hours you can spend on whatever this month broke.
- Monthly hour pool, rolls over
- Named people you contact directly
- Cancel on a quarter's notice
Platform & operations
You run the science, we run the machine — for our own platforms: COHORT, STATLAB, CORPUS, TRANSCRIPTION and anything we built for you, with backups, patching, support and the compliance paperwork.
- Your datacentre, ours, or a partner's
- Per-study or per-institution
- Exit plan written on day one
From grant application to archive.
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Phase 01
Grant & protocol
Deliverable — DMP, budget, methods sectionWe join before the money exists. Reviewers can tell the difference between a costed data management plan and a paragraph of hope.
- Power and sample size, with the assumptions written down
- Data management plan and FAIR annex
- Infrastructure budget lines that survive review
- Named co-applicant where it helps the application
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Phase 02
Approvals
Deliverable — an approvable annex setThe paperwork that decides whether you start in March or September. We have written it enough times to know which sentence the committee stops on.
- Ethics submission annexes: data flow, security, retention
- DPIA and Article 30 record of processing
- Data processor agreements and sub-processor lists
- Danish specifics: regional approval, Statistics Denmark project setup
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Phase 03
Build
Deliverable — a database your team signs offInstruments, database, randomisation, consent, app. Two weeks for a straightforward cohort; six for a randomised trial with a device in it.
- REDCap or COHORT project built from the protocol
- Validation at the point of entry, not in cleaning
- Roles, data access groups, blinding rules
- User acceptance test with your own coordinators
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Phase 04
Recruit
Deliverable — a funnel you can steerRecruitment is a conversion problem wearing a lab coat. We instrument it like one — and then fix what the numbers show.
- Screening logs and eligibility automation
- E-consent with witnessed and proxy flows
- Invitations by letter, e-mail, SMS and national digital post
- Weekly funnel by site, so a stalling centre is visible in days
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Phase 05
Collect
Deliverable — data arriving cleanLongitudinal events, patient-reported outcomes, wearables, lab feeds, imaging. All landing in one place with one identifier scheme.
- Adaptive questionnaires that skip what they can infer
- Device and wearable ingestion with gap detection
- Automated reminder ladders with a stop rule
- Live completeness dashboard per participant
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Phase 06
Clean & lock
Deliverable — dataset v1.0, immutableThe unglamorous phase that decides whether the analysis takes a week or a quarter.
- Range, logic and cross-form consistency checks
- Query workflow with a documented resolution trail
- Duplicate and identity resolution
- A frozen, versioned analysis dataset with a hash
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Phase 07
Analyse
Deliverable — results plus reproducible codeAnalysis plan first, models second. Then every number arrives attached to the code that made it and the assumption checks behind it.
- Statistical analysis plan, pre-registered where relevant
- Survival, longitudinal, mixed and Bayesian methods
- Prediction models with external validation designed in
- STATLAB for the fast questions, a statistician for the hard ones
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Phase 08
Publish
Deliverable — a submission packageJournal-spec tables and figures, method paragraphs written in your voice, and a supplement a reviewer can actually re-run.
- CONSORT, STROBE, TRIPOD+AI or PRISMA flow as generated output
- Figures to the journal's exact dimensions and colour rules
- Reproducible supplement: container, code, synthetic data
- Rebuttal support when Reviewer 2 wants it done differently
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Phase 09
Archive & re-use
Deliverable — a study that ages wellWhen the study ends, the data still has fifteen years of obligations left. We deliberately make them somebody's job.
- Retention and deletion schedules that actually execute
- FAIR metadata and controlled re-use requests
- Handover documentation the next PhD student can read
- Long-term hosting of our platforms, or migration into your institution
The clients agree.
Viceron has played a central role in transforming one of Denmark's most valuable cardiovascular research resources into a scalable, future-ready platform. Their expertise in data management, governance, and advanced analytics has enabled entirely new possibilities for clinical research.
We were impressed by Viceron's exceptionally high level of technical expertise. They didn't rely on buzzwords—they explained what would actually work for our organization and why. Their ability to translate highly technical concepts into language that leadership could understand made them an invaluable strategic partner.
Viceron became an extension of our engineering team. Their technical expertise, collaborative approach, and ability to deliver across hardware, software, AI, and embedded systems exceeded our expectations. The result is a platform that will support the future of our product ecosystem.
FAQ
Who owns the data, the database and the code?
You do — all three. You are the data controller; we act as processor under a written agreement. The database structure, the instruments, the analysis scripts and any custom software we write for you are yours, delivered as source with documentation.
We keep no rights over your data and no claim on your results. Where we have built a platform component that is genuinely ours, such as STATLAB's engine or COHORT's delivery machinery, you get a licence to it for the life of the study, and an exit path if you ever want to leave. We agree that path in writing before you start.
Can you be a co-applicant on the grant?
Yes, and it usually strengthens the application. We provide a work package description, a costed budget, CVs, and the methods and data management text for the technical parts. Reviewers respond well to a named partner with a track record where the alternative is “a student will build a database”.
Danish and EU schemes we have costed work packages for include Innovation Fund Denmark, Novo Nordisk Foundation, Independent Research Fund Denmark, Horizon Europe and EU4Health. If the grant is not awarded, you owe us nothing for the application work.
Who hosts what?
REDCap is always hosted by your institution. It is licensed to non-profits, so we cannot host it and do not try to. We build inside the instance your university or hospital already runs. See REDCap for exactly where that boundary sits.
Our own platforms, and anything we build for you, are hosted wherever the study requires: your own datacentre, one of ours, a partner datacentre in Denmark or Germany, a partner datacentre in the United States where that is appropriate, or public cloud. We decide this with you for each study on policy grounds, never by product tier or by default.
Where data cannot leave the environment it lives in, we work inside that environment under your project authorisation. We have collaborated with Statistics Denmark, the CPR register and Statens Serum Institut, and one of our services runs inside the Capital Region of Denmark's own datacentre. For consortia where no site may share raw data at all, we run federated analysis instead: the models go to each site and the records stay there.
Is AI-assisted analysis publishable? What do journals say?
Yes. You publish the analysis, and the assistant is not part of it. STATLAB produces standard R or Python using standard packages. The output is the same code a statistician would write, arrived at faster, and you can read every line of it.
We give you a methods paragraph describing exactly what was run, a machine-readable record of package versions and random seeds, and a container that reproduces the numbers. Where a journal asks about AI tool use, that record is what you disclose. We do not let a number into a manuscript unless you can personally trace it to code you have seen.
We already have REDCap. Is that a problem?
It is the normal starting point, and the only one we work from. REDCap is excellent at what it does, and all of our REDCap work happens inside an instance the institution already holds the licence for: designing projects properly, fixing ones that grew sideways, and adding the pieces REDCap deliberately leaves to others.
Where REDCap genuinely runs out — a hundred thousand invitations a wave, adaptive item banks, a participant app, live device data — we bolt our own platform alongside it through the API rather than replacing it. Your coordinators keep the interface they know. See REDCap and the survey platform.
How fast, honestly?
A single-arm cohort database with clean instruments: two weeks. A multi-site randomised trial with e-consent, blinding and monitoring views: four to six weeks. A questionnaire platform for a national wave, including translations and a reminder ladder: four to eight weeks. A custom participant app in the stores: eight to twelve.
Analyses are faster: a well-specified survival or mixed-model analysis on a locked dataset takes days. What moves those dates is almost always approvals and the state of the existing data. We will tell you which of the two you are actually waiting for.
What does it cost?
Builds are quoted as fixed fee against a written scope, so a grant can carry them as a line item. Retained support is a monthly hour pool. Hosting of our own platforms is per study or per institution, priced on participants and storage rather than on how successful your recruitment turns out to be. Your REDCap costs you nothing extra, because it is already yours.
We will also say when you do not need us: some of what people ask for is forty minutes of REDCap configuration that a research assistant can do with a written instruction. We would rather send the instruction and keep the relationship.
Can you take over a study that is already in trouble?
That is a large share of what we do. The usual pattern: recruitment is halfway, the data is in three formats, the person who built it has left, and something needs to be defensible by a deadline.
We start with a two-week assessment of what exists, what is recoverable, what has to be re-collected, and what the honest options are. You get that as a written report with a recommendation, and it is useful to you whether or not you continue with us. Occasionally the recommendation is to change the analysis rather than the data, and we say so.