engineering2 papersavg year 2026quality 4/5strong evidence

Real-world deployment

Research gap analysis derived from 2 engineering papers in our local library.

The gap

There is a need to evaluate the performance, scalability, and robustness of AI-driven systems in real-world production settings across various environments.

Consensus across the literature

The papers collectively establish that current research lacks real-world deployment experiences but leave open how these systems perform under practical conditions.

Research trend

Emerging — attention growing, methods still coalescing.

Supporting evidence — 2 representative gaps

  • EventVenue: A Comprehensive Full-Stack Web Platform for Intelligent Event Venue Discovery, Booking, and Management (2026) · doi

    Performance evaluation was conducted only on a development environment; real-world deployment performance under varied infrastructure conditions is not evaluated.

    Keywords: performance evaluation conducted development environment real world deployment varied infrastructure conditions evaluated
  • Quorum Seal: Cross-Sensor Challenge and Response Attestation for Compromise Detection with Adaptive Multi-Surface Verification (2026) · doi

    The prototype was validated only in a local environment; deployment and evaluation in real-world production settings with actual users and threat scenarios is needed.

    Keywords: prototype validated local environment deployment evaluation real world production settings actual users threat scenarios needed

Explore this gap further

Search “Real-world deployment” across open scholarly engines for the latest related literature.

Working on this gap? Publish with us.

Science AI Journal reviews manuscripts in under 15 minutes with 8 specialised AI reviewers calibrated on 23,000+ real peer reviews. Open access, CC BY 4.0.

Related gaps in Engineering

Command palette

Jump anywhere, run any action.