Document & Knowledge Search

For intranets, help centers, and content platforms. Validate that your search engine surfaces the most relevant articles, PDFs, and internal documents—so people get answers fast and support tickets go down.

Why This Matters

When answers are hard to find, costs rise and satisfaction falls. Effective knowledge search pays off immediately across three fronts:

Ticket deflection: strong self‑service and relevant results can deflect a substantial share of incoming tickets, especially repeat FAQs.
Lower cost per resolution: self‑service interactions are dramatically cheaper than live support, so every deflected case compounds savings.
Productivity & satisfaction: employees spend meaningful time searching for information; faster findability boosts productivity and user satisfaction.

Bottom line: surfacing the right doc on the first page of results has measurable impact on cost, productivity, and CSAT.

Dashboards Show Outcomes, Not Relevance

Click‑throughs and case counts tell you what happened—but not whether the ranking itself was good. They miss rank‑aware IR signals and can’t explain regressions after tuning synonyms, fields, or semantic models.

The fix: evaluate relevance offline first—against a stable set of queries and ground truth—so you can compare configurations apples‑to‑apples before exposing changes to users.

Live A/B on Core Search = Real Risk

Testing in production can degrade agent and customer experience while you “wait for significance,” and novelty/change‑aversion can bias results.

With TestMySearch: run thousands of knowledge queries offline; ship only the winners. Then, if needed, do a small, guarded online check.

How TestMySearch Fits Knowledge Search Workflows

Bring your SharePoint/Confluence/Zendesk/Docs content via your existing engine (Solr, Elasticsearch, Coveo, Algolia or custom). We provide the lab to compare ranking strategies safely.

STEP 1

Upload & Configure

Add query sets (FAQs/search logs) and evaluation sets; connect search configs.

STEP 2

Run & Analyze

Batch runs across configurations; compute nDCG, Precision/Recall, overlap, significance.

STEP 3

Visualize & Decide

Side‑by‑side reports make trade‑offs visible so you can ship with confidence.

Expected Results (ground truth): define must‑find docs for critical queries (policies, SOPs, top KBs) and measure their rank.
Rank‑Aware Metrics: nDCG@k, MAP, Precision/Recall, rank correlation, overlap, and pairwise statistical tests to detect real wins/regressions.
LLM‑powered Virtual Assessor: when labeled relevance is scarce, use LLM judgments to approximate labels for long docs and PDFs (with chunking + embeddings) and expand coverage.

Deep‑Dive: Long Docs & Chunking

Answers often hide deep inside PDFs and wikis. Our workflow evaluates chunk‑level relevance and rewards configurations that surface the right section fast.

Deep‑Dive: Metadata & Semantic Drift

Tune title/tags/category boosts and monitor semantic models for “related‑but‑unhelpful” results. Rank overlap + IR metrics make risky displacements visible.

Business Outcomes You Can Expect

Ticket Deflection: direct users to answers in docs/KBs and decrease new cases—double‑digit deflection is a common benchmark.
Lower Cost per Resolution: shift routine questions from expensive live interactions to low‑cost self‑service wherever possible.
Faster Time‑to‑Answer: better ranking of “must‑find” articles shortens resolution for customers and agents alike.

Higher Satisfaction: effective self‑service improves CSAT by enabling instant answers and fewer escalations.
Productivity Gains: reduce time employees spend looking for info; accelerate onboarding with reliable findability.
Shared Language: IR metrics and reports align support, product, and engineering around evidence—not hunches.

View Sample Knowledge Search Report Schedule Your Strategic Demo