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AI-assisted evidence for academic research and technical validation

Make your next step
grounded in evidence.

Find relevant papers for research and suitable candidates for validation. Start with one question and let AI help clarify goals, evidence and next steps.

Check evidence alongside sourcesCompare conditions and gapsPreserve decisions and results

Start with your question

Start with a question. AI helps clarify your goal and discover sources, with options for ambiguous details. Add baseline and constraints as you go.

What are you preparing today?

01 →

Academic research

Research question → relevant papers → key findings → literature brief or method comparison.

02 →

Technical validation

Technical problem → candidates and conditions → evaluation plan → results and review.

See the complete workflow

These examples illustrate the workflow; they are not empirical claims about method performance.

Academic research example Example

Which training approach should we validate for small-sample classification?

Input: Seed papers + current experiment notes

Evidence: methods, data splits, reported results and limitations; retain missing fields.

Comparison: different data splits prevent direct ranking.

Output: tentative approach, experiment plan with a shared split and review questions.

AI technical validation example Example

Which candidates should we evaluate when long-document retrieval fails?

Input: Papers + code documentation + internal baseline CSV

Evidence: Recall@k, p95 latency, resource conditions and reproduction assets.

Comparison: keep mean latency separate from p95; without a baseline, compare external research only.

Output: candidates, success and stop conditions, experiment results and decision history.