AACSearch brings production analytics and offline relevance evaluation together.Open the Quality Lab
Judgments seeded from clicks and purchases, or entered by hand for the queries that matter most.
Every run scores nDCG, MRR, MAP, precision and recall against the same fixed yardstick.
Compare a candidate against the current baseline — a regression past your threshold blocks the promotion.
Save the queries that matter to the business. For each query, define the relevant results, the expected results and a graded relevance. Now a ranking change can be compared against a baseline.
No single metric answers every question, so AACSearch lets you look at quality from several sides.
Offline quality and production behaviour should complement each other.
Ranking A
vs
Ranking B
Rule set A
vs
Rule set B
Baseline run
vs
Run after a re-index
Get a measurable diff before the rollout.
After offline validation, a change can be tested on a share of production traffic. Measure guardrail metrics too, not only CTR.
A run that drops mean nDCG past a threshold is flagged before you promote it.
Sticky variant assignment and Wilson confidence intervals on every metric.
Every search can be traced end to end, and replayed against a new configuration.
Rules watch zero-result rate, engine error rate, p95 latency, click-through and no-click rate against a threshold you set, and fire the moment they cross it — no dashboard-watching required.
The platform proposes synonym, pin and boost suggestions from real query and click behaviour, with the evidence attached — you review and accept, nothing changes silently.
{ "k": 10 }
// -> {
// "ndcg": 0.84, "mrr": 0.79, "map": 0.71,
// "precision_at_10": 0.62, "recall_at_10": 0.88
// }Explore more
The best way to evaluate a search platform isn't reading one more comparison table.
Only then make the decision.