RESEARCH DESIGN INTELLIGENCE
Find out if your study can work before you run it.
ForskAI tests whether your planned design can recover the signal you care about, before recruitment, fieldwork, or data collection begins.
THE PROBLEM
The failure usually appears after the money is gone.
A study can be well-intentioned, well-funded, and still unable to recover the thing it was built to measure. The model may not identify. The scale may saturate. The planned N may be too small. The analysis sheet may quietly alter the data. ForskAI moves those failures to the design stage, when they are still cheap to fix.
ForskAI starts before data collection: with assumptions, instruments, planned analyses, and failure modes still visible.
THE APPROACH
Specify the target. Stress-test the design. Fix what cannot recover.
ForskAI turns a research plan into a recoverability test. It checks whether the planned measurement and analysis can recover the intended signal, then shows what has to change before the study begins.
- STEP 01
Specify
Define the construct, measurement plan, sample, assumptions, and intended analysis.
The question is translated into testable design conditions.
- STEP 02
Stress-test
Simulate whether the planned design can recover the signal under realistic noise, missingness, and ceiling effects.
Failure is useful here because it becomes a design rule.
- STEP 03
Decide
Return a PASS / RISK / FAIL result with the assumptions, thresholds, and recommended fixes attached.
The result is design evidence, not a validity claim.
DESIGN PILOT
The expensive failure is not a negative result.
It is discovering too late that the design could never recover the signal.
Design Pilot
PER STUDY DESIGN€5k–15k
- Design specification
- Recoverability stress test
- PASS / RISK / FAIL result
- Assumptions and thresholds
- Recommended fixes
- Written design-risk report
One planned study, stress-tested before you spend it — a clear pass, risk, or fail result, the assumptions behind it, and the report to back it.
Final price depends on model complexity, measurement plan, number of scenarios, and whether evidence support is included.
OPTIONAL ADD-ON · EVIDENCE PILOT
An optional check on the assumptions behind the design.
A recovery test is only as good as the assumptions you feed it — the effect you expect, the variance you assume, the base rates you take for granted. Evidence Pilot checks what the current literature actually supports for those assumptions, so a design starts from defensible inputs instead of hopeful ones.
It is an optional add-on, not a literature-review tool. It grounds the inputs to a design test and never decides for you.
Secondary by intent: ForskAI leads with research design. Evidence support is there to strengthen the assumptions behind a Design Pilot, not to compete as a standalone review.
PRICING
From one study to an institution.
Design Pilot
€5k–15k
One study
Lab / group retainer
€20k–40k / year
Repeated designs
Institution
€60k–150k / year
Shared method support
Enterprise
Custom · €150k+
Pharma, CRO, private deployment
Optional evidence support add-on: typically €2k–8k, priced after scoping.
Institution and enterprise enquiries: [email protected]
THE BOUNDARY
What ForskAI is not.
ForskAI does not validate your study, guarantee results, give clinical advice, or turn a weak construct into a strong one. It tests whether the design you are planning has a reasonable path to recovering what you say you want to measure.
- A PASS is design evidence, not validity.
- A FAIL is not a failed project. It is an early warning.
- Roadmap capabilities must be marked as roadmap.
- Human statistical and domain review remain part of the process.
Bring us one planned study.
We will show where it can recover, where it is at risk, and what has to change.
Run a design pilot