databases · surveys · statistics · qualitative · trials · AI

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.

● What we doFour products · seven services

Eleven ways in, all held to the same discipline.

Products — systems we built and run

Four products of our own. License them, or let us host them where your policy requires.

Services — work we do, in your systems or ours

Seven kinds of engagement, from a two-hour review to being the data management provider of record on an international trial.

Custom apps, software & websites

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.

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Teaching & training

The 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.

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REDCap, databases & biobanks

A 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.

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Secure data handling & transfer

Sensitive 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.

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AI, big data & interoperability

The 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.

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Clinical trials

Trial 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.

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Systematic reviews & evidence synthesis

The 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 →
Not on the list

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→
● How this worksFour ways in

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.

Model 01

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
Model 02

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
Model 03

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
Model 04

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
● Where we can helpNine phases · one partner

From grant application to archive.

  1. Phase 01

    Grant & protocol

    Deliverable — DMP, budget, methods section

    We 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
  2. Phase 02

    Approvals

    Deliverable — an approvable annex set

    The 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
  3. Phase 03

    Build

    Deliverable — a database your team signs off

    Instruments, 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
  4. Phase 04

    Recruit

    Deliverable — a funnel you can steer

    Recruitment 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
  5. Phase 05

    Collect

    Deliverable — data arriving clean

    Longitudinal 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
  6. Phase 06

    Clean & lock

    Deliverable — dataset v1.0, immutable

    The 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
  7. Phase 07

    Analyse

    Deliverable — results plus reproducible code

    Analysis 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
  8. Phase 08

    Publish

    Deliverable — a submission package

    Journal-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
  9. Phase 09

    Archive & re-use

    Deliverable — a study that ages well

    When 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
● Sworn statements

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.
Chief PhysicianThe Capital Region of Denmark
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.
Senior ExecutiveGlobal Pharmaceutical Company
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.
DirectorVidex
● Straight answers

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.

Interested? Take the next step

Bring us your impossible.