scid.aiResearch landscape
See major approaches, emerging branches, organisations and publication dynamics.
Move from a research signal to a shortlist you can verify
scid.ai collects publications around a task, reveals directions and organisations, compares reported findings and limitations, and prepares an evidence base for expert assessment.
Value
Scouting is valuable when it improves the next decision—not when it produces more findings
The platform structures open scientific sources. Supplier checks, pilot readiness and industrial applicability remain separate expert work.
Comparable evidence
Put the technology, reported result, source and limitation into one table.
Focused shortlist
Save promising candidates into groups and keep monitoring the direction through digests.
Choose a starting point
Choose the technology view you need
Start with scouting, an evidence matrix or recurring monitoring.
An evidence funnel instead of a catalogue of bold claims
Every candidate should keep a path to the papers and questions that still need checking.
Set the criteria
Define the function, operating environment, constraints and required maturity.
Build the field
Find approaches, materials, methods and organisations in research publications.
Compare
Record reported effects, experimental conditions and evidence limitations.
Hand off to experts
Prepare a source-backed shortlist for supplier, applicability and pilot checks.
Questions
Does scid.ai find ready suppliers?
It collects research publications and organisations around a technology; supplier and commercial-readiness checks need separate expertise.
Can scouting be updated regularly?
Yes. Save the directions and use digests to return to new publications.
Prepare a shortlist that can be defended with sources
Papers, comparisons and expert questions remain part of one research project.