Relevance Engineering

We improve retrieval and ranking for search, RAG, and AI agents. We trace where useful results or evidence are lost, fix the stage responsible, and evaluate what changes in production.

Your team gets a focused engineering improvement, before-and-after evaluation, and clear evidence of what changed.

Start with an audit

20 minutes to discuss where search quality is falling short and whether a focused engagement fits.

When to use Relevance Engineering

The system works. Improving it has become difficult.

Your search, RAG application, or AI agent is already in production, but quality is hard to improve or measure. Search might miss useful results, a RAG answer might rely on an outdated policy, or an agent might use the wrong source for a decision. Searchplex traces these failures and makes focused improvements within your existing system.

Relevance has plateaued

Your team has tuned fields, boosts, queries or models, yet important searches still underperform. The constraint may be upstream of the ranking logic being adjusted.

Changes create regressions

An improvement for one query class harms another. Exact lookup, exploratory queries, filters, languages and long-tail traffic need different checks before a broad rollout.

Useful results never reach the shortlist

Relevant products or documents exist but are excluded by query processing, indexing, candidate selection or filtering. A better reranker cannot recover candidates it never receives.

Ranking is difficult to explain

Relevant candidates are retrieved but appear too low, while older, duplicated or heavily interacted-with items dominate. The next step is to inspect the evidence and constraints behind their placement.
Where Searchplex intervenes

Improve the stage that limits relevance.

A search result is shaped by a sequence of decisions: what data is searchable, which candidates qualify, how they are ranked, and what users can encounter. We investigate the relevant stages, then change the part supported by the evidence.

Content and query representation

Inspect catalog or corpus coverage, searchable fields, metadata, document units, identifiers, normalization, synonyms, languages and query routing. Address gaps where useful evidence exists but cannot participate in retrieval.

Candidate generation and admission

Trace lexical, semantic, hybrid and filtered retrieval paths. Determine where useful candidates are lost or noisy matches are admitted; test field-specific matching, query construction, fusion, filtering and candidate depth.

Ranking and reranking

Inspect scoring features, behavioral signals, business rules, rank windows and product grouping. Test whether newly admitted candidates can reach useful positions without destabilizing established results.

Evaluation and production experiments

Build the evidence needed for the decision: representative queries, targeted relevance judgments where useful, offline comparisons, regression checks and production experiment design. Separate retrieval changes from ranking, exposure and business outcomes.

See how these layers fit together in the Retrieval Foundation →. For a practical diagnostic sequence, read The Search Evaluation Ladder →. For the query-understanding layer, read Types of Search Queries: A Practical Map →

How the engagement works

Diagnose. Improve. Validate.

We work from observed behavior and testable hypotheses, not an assumed model or platform replacement. Investigation and implementation can overlap; evidence determines the next step.

01

Investigate

Agree on the business decision and inspect representative failures. Trace the query, data, candidate and ranking paths to identify plausible causes and constraints.

02

Establish the baseline

Use existing queries, logs and feedback; add targeted judgments and metrics where needed. Record current behavior, affected query classes and regression guardrails.

03

Improve

Implement the smallest coherent change supported by the diagnosis—whether in representation, candidate admission, retrieval, ranking or serving logic.

04

Validate

Compare behavior offline, then use A/B testing, interleaving, shadow evaluation or controlled rollout when suitable. Check what changed, which users could encounter it, and what the available metrics can establish.

05

Decide and hand over

Recommend release, iteration or further investigation based on the evidence. Document results, trade-offs, unresolved questions and operational guidance for the team.

We document trade-offs, production results, and the next steps needed to continue improving relevance.

Evaluation for RAG and AI agents

RAG · Evidence and answer quality

We check whether the right source and version reach the model and whether the evidence supports the answer. This tells us where to fix a retrieval miss and where the model had the evidence but used it incorrectly.

AI agents · Decisions across steps

We trace which sources the agent consults and test whether it answers, asks for clarification, or hands off at the right point. A rental concierge, for example, should check the guest’s reservation before saying whether breakfast is included.

You get a reusable set of test scenarios with expected outcomes, a before-and-after comparison, and a record of the failures your team should keep testing for.

AI agent evaluation in practice →Explore Evaluation & Operations →
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

Need a diagnosis or an implementation partner?

If the root cause is still unclear, start with a bounded Search Stack Audit. If the problem is sufficiently understood, scope a relevance improvement and the evidence needed to evaluate it.

Root cause unclear

Start with the Search Stack Audit

Investigate whether the constraint lies in data representation, retrieval, ranking, architecture or evaluation. Leave with supported findings and a practical next decision.
Review the audit →
Problem and scope are known

Scope a focused relevance improvement

Implement and evaluate a targeted improvement within the existing system when feasible, with explicit assumptions, dependencies and success measures.

Unsure which description fits? Use the diagnostic →

FAQ

Questions about relevance engagements

Scope follows the production problem, not a fixed platform package. These questions help identify a realistic starting point.

Related resources

Use case

Precision Search with Complex Query Constraints

How professional retrieval preserves Boolean logic, proximity, fields, exclusions, and structured restrictions as systems evolve.
Explore use case →
Ways of working

Work with us the way that suits your team

Either way, you own the result, and the aim is a team that can run it without us.

Project delivery

Searchplex delivers the project

Our engineers handle day-to-day delivery within Searchplex. We meet regularly with your business and leadership stakeholders to share progress and agree on key decisions.
Embedded

Specialists paired with your engineers

We lead the retrieval work inside your team and processes, so the expertise stays with you.
Start here

Relevance not where it should be?

Tell us what is failing and what you have tried. We will help you decide between an audit and a focused improvement.

Explore the Search Stack Audit