Can Searchplex work in our existing stack?

Yes, when the required changes are supportable in the current architecture and environment. Searchplex works with Elasticsearch, OpenSearch, Solr, Vespa, custom platforms, and multi-stage systems combining search engines, vector retrieval, external rerankers, and application logic.

Can the engagement focus on one query class or product surface?

Yes. A focused engagement may target known-item lookup, multilingual retrieval, filtered search, freshness, version handling, product discovery, RAG evidence retrieval, or another bounded failure mode. The scope includes enough regression coverage to protect adjacent behavior.

What data and access do you need to start?

This depends on the problem. Useful inputs can include representative queries, result examples, query logs, product analytics, relevance feedback, schema or index configuration, ranking logic, and access to a safe development or evaluation environment. Searchplex defines the minimum useful access during scoping.

Do we need an existing evaluation or judgment set?

No. Existing judgments, query logs, support evidence, product analytics, and subject-matter expertise can accelerate the work, but they are not prerequisites. The engagement can create the smallest defensible query set and evaluation method needed for the scope.

Relevance Engineering

Diagnose where relevance breaks. Improve retrieval and ranking. Measure what changed.

Start with an audit

When to use Relevance Engineering

The system works. Improvement has become the problem.

Use this engagement when a production system is delivering results, but further tuning is slow, risky, or difficult to explain. Searchplex identifies where quality is being lost and improves it without assuming a migration or rebuild.

Relevance has plateaued

The team is still tuning boosts, fields, prompts, or models, but meaningful gains are smaller. The constraint may sit outside the ranking rule being changed.

Changes create new regressions

A release improves one query class and damages another. The team cannot predict the effect across exact lookup, discovery, filters, languages, or long-tail queries.

The right results never make the shortlist

Relevant content exists, but widening rerank windows or changing ranking models produces little improvement. The problem may occur earlier in query processing, filtering, candidate generation, or fusion.

Results are difficult to trust

Older versions, duplicates, generic content, or weakly authoritative sources outrank the result the user should actually rely on.

Where Searchplex intervenes

Improve the layer that controls the result.

A result is the output of a system, not just a score. Searchplex traces eligibility, ordering, and production validity across four connected parts of the system, then implements the change where it will matter.

Content representation and query understanding

Inspect corpus coverage, metadata, versions, authority, freshness, retrievable units, normalization, language behavior, entities, identifiers, expansion, and filters. Improve the representation or query path when it prevents the system from expressing the right result.

Candidate retrieval

Separate lexical, vector, hybrid, filtered, and federated paths to find where relevant candidates enter or disappear. Improve recall, fusion, filtering, or candidate depth before asking ranking to compensate for missing eligibility.

Ranking and reranking

Review scoring logic, features, business rules, model stages, and candidate windows. Align textual, semantic, behavioral, authority, freshness, and business signals with the product's relevance contract.

Evaluation and production release

Establish the evidence needed to compare behavior, including query sets, judgments, stage-level metrics, and regression checks. Validate relevance alongside permissions, latency, cost, policy, and other conditions that determine whether a result is valid in production.

See how these layers fit together in the Retrieval Foundation →

How the engagement works

A measured improvement cycle.

The intervention follows the evidence. Searchplex does not start by assuming the answer is a new model, ranking method, or platform.

Investigate

Understand the problem, inspect representative failures and system behavior, and form a working hypothesis.

Establish the baseline

Create enough representative queries, judgments, metrics, and guardrails to test the hypothesis and distinguish improvement from regression.

Improve

Implement the smallest coherent change expected to address the diagnosed problem and improve the agreed relevance or product outcome.

Validate

Compare against the baseline offline and, where appropriate, confirm the result through an A/B test, interleaving, shadow evaluation, or monitored rollout.

Iterate or release

Continue from the new baseline when evidence supports another hypothesis, or release and hand over when the objective is met or another cycle is no longer justified.

Your team leaves with a measured improvement, a repeatable evaluation baseline, before-and-after evidence, and the rationale and release guidance needed to keep improving.

Client work

Selected relevance work

See all case studies →

Tax & Regulatory Technology

Nextens: relevance in versioned knowledge

Searchplex diagnosed data structures, metadata, ranking logic, and query handling in a production tax-research system. The work improved current-version handling, ranking predictability, duplicate control, query assistance, and the team's ability to inspect retrieval behavior.

Read case study→

Legal Technology

CuratedAI: hybrid multilingual retrieval

Semantic-only retrieval supported broad exploration but struggled with exact legal references and known-item lookup. Searchplex introduced lexical precision alongside semantic recall, native-language indexing, and finer retrieval units for English, French, and Dutch legal research.

Read case study→

Technical perspective: When reranking becomes a system boundary→

Choose the start point

Unclear root cause or known relevance scope?

The two engagements solve different problems. The Audit diagnoses the system and clarifies the decision. Relevance Engineering implements and validates a focused improvement.

Root cause unclear

Start with the Search Stack Audit

Use the vendor-neutral audit when relevance symptoms may come from data modeling, retrieval architecture, ranking, infrastructure, or evaluation, and the team needs to know where to act before committing engineering effort.

Review the audit →

Problem and scope are known

Discuss a focused relevance engagement

Use Relevance Engineering when the problem is sufficiently understood to implement and measure a focused improvement.

Discuss the scope →

Unsure which description fits? Use the diagnostic →

FAQ

Questions about relevance engagements

Scope follows the production problem, not a fixed platform package. These are the decisions teams most often need to clarify before starting.