Optimization overview

Measurement is only half the product. Once Lokrix knows your Lokrix AI Score and why it looks the way it does, the optimization engine turns that diagnosis into a prioritised, simulated plan — concrete changes, each with a predicted visibility uplift and the prompts and engines it moves.

The optimization loop

Optimization is a closed loop, not a one-off report. You measure the current state, read the feature-level reasons behind each score, apply the highest-impact recommendation, and re-scan to confirm the model’s prediction held up in the real engines. Every turn of the loop tightens the calibration and surfaces the next best move.

The Lokrix AI optimization loopFive stages arranged in a clockwise ring — measure, recommend, simulate uplift, apply, and re-measure — with arrows showing the cycle repeats.Measure01Recommend02Simulate uplift03Apply04re-measure05Improvecontinuously

From score to plan

The recommendation engine reads the same features the predictive scoring model uses, ranks candidate changes by predicted impact against effort, and simulates each one before you touch your site. Because the simulation runs against the calibrated model rather than live engines, it costs no credits — you only spend credits when you decide to verify a change with a fresh live scan.

  • Diagnose. Feature-level attribution explains which signals hold the score back and against which competitors.
  • Prioritise. Recommendations are ranked by predicted uplift, confidence and effort — not by a generic checklist.
  • Simulate. A what-if run predicts the new score and the prompts it moves, with a confidence interval, before any work is done.
  • Verify. A live re-scan confirms the uplift landed in the real engines and re-anchors the model.

In this section

  • Recommendations — how items are generated, scored and prioritised on the impact/effort matrix.
  • What-if simulation — predict a change’s uplift for free before you build it.
  • Path to #1 — the sequenced plan to drive a specific prompt to the top.
  • The prompt universe — the set of buyer questions optimization is measured against.
  • The levers — the evidence-backed content changes that actually move AI visibility.