Enterprise Search: Find What Your Business Already Has

A customer searches your website for something you definitely have. Your search returns nothing.

The short answer: in one enterprise catalogue we analysed, 53.4% of a week's searches returned no result. We took a random sample of 500 of those failed searches and ran them against a search index built around the same catalogue. 470 of the 500 returned a relevant report that already existed. For that sample, 94% of the failures were not content failures. They were retrieval failures.

The immediate assumption is usually that the customer searched for the wrong thing. But sometimes the information is present and the search itself is what failed.

We saw this while analysing a customer's catalogue containing more than 24,000 published reports. During one week, visitors ran 4,947 searches. More than half, 53.4%, returned no result at all.

So we took a random sample of 500 distinct searches that had failed and ran them against the search index we had built around that catalogue. 470 of the 500 failed searches returned a relevant report that already existed.

The customer already had the reports. Search was failing to connect the way people asked for information with the way that information was stored. This article is part of our Agents at Work series, where we publish the patterns behind our agentic deployments.

What changed when we fixed retrieval instead of the interface?

The existing search had surfaced only 3,578 of 24,624 available reports during the week we analysed. When we tested our search against the sample of failed queries, it also surfaced 1,049 reports that had not appeared in any search result during that week.

Search measure	Existing search	Projected with corrected search*
Searches returning no result	53.4%	about 3.2%
Reports reachable through search	3,578	more than 7,400
Catalogue surfaced	14.5%	more than 30%

*Corrected figures are projections from a random sample of 500 failed queries, not post-deployment production measurements.

The important part was not a new search box. It was identifying why apparently reasonable searches were failing.

• Words such as "latest" or "report" could become mandatory even when they carried little of the user's actual intent.
• Adding more words could make a search less likely to succeed rather than more precise.
• A general dictionary could treat specialist vocabulary as spelling mistakes.
• Short but important terms such as AI, 5G, EV and 3D could be absent from the index because of a configuration default.

Enterprise search does not have to understand the whole world. It has to become very good at understanding your world.

Why this matters to a business decision-maker

When search fails, the business rarely sees the missing result. The user does.

• A buyer may conclude that a product, report or service does not exist.
• An employee may spend more time searching, navigating repositories or asking colleagues.
• A support or research workflow may begin with incomplete information.
• An AI assistant or agent may retrieve the wrong context, or no context, before it reasons about the next step.

That is why search quality is not only a user-interface issue. It is a discoverability problem.

Could your search be hiding things people are already looking for?

A quick way to find out is to take things you know exist and deliberately search for them imperfectly. Misspell the name. Change the word order. Use only part of it. Use the terminology a customer or employee might use rather than the exact title in your database. If relevant content repeatedly disappears, the problem is measurable, and often fixable.

Send us a website, catalogue or searchable repository. We test a sample of real-world queries, identify where relevant content stops being retrievable, and show what a domain-customised search layer could recover. Test your enterprise search.

Why does enterprise search miss content that already exists?

A zero-result search appears straightforward: there is no matching content. In enterprise search, that conclusion can be wrong.

During the week we analysed, 2,237 distinct queries returned no results. There were also 281 sequences of three or more consecutive unsuccessful searches, accounting for 2,227 searches in total. Users were repeatedly changing their queries and trying again.

If the requested information exists and every variation still fails, the problem is no longer simply how the user phrased the query. It is a search-quality problem.

1. Structural words were being treated as mandatory

Consider a query such as "latest report on carbon capture". The important subject is carbon capture. But a search implementation that treats every word as equally mandatory can allow words such as "latest" and "report" to prevent the relevant result from matching.

In the query analysis, this class of problem accounted for 46% of measured failures.

The correction was deliberately catalogue-specific. A word could be demoted only when it occurred in seven or fewer report titles. Terms such as data, information, analysis and research occurred frequently enough in real titles that removing their influence globally could make legitimate subjects harder to find.

Relevance rules should reflect the corpus being searched.

2. Longer queries were becoming less likely to succeed

Users often add words because the first search did not work. Normally that should provide more context.

But when every word is required to appear, each additional word creates another way for the search to fail. In the customer logs, no-result searches rose from 17.7% for one-word queries to roughly 69% for searches containing three or more words.

The corrected behaviour relaxes requirements progressively while keeping rarer, more informative terms important for longer. The aim is to let a user describe what they want without being punished for adding natural language.

3. A general dictionary did not understand the customer's domain

Spelling correction becomes difficult when a catalogue contains specialist terminology.

Terms such as "virtopsy" and "abetalipoproteinemia" are valid in a specialist research catalogue but invalid in many general dictionaries. Correcting them against general vocabulary risks changing the query away from what the user actually intended.

So spelling corrections are drawn from the vocabulary of the customer's own catalogue. As the catalogue changes, that vocabulary can change with it.

4. Some valid terms were invisible to the index

One useful lesson had nothing to do with sophisticated ranking. The database indexed words of three characters or more by default.

That meant terms such as AI, 5G, EV and 3D were absent from the index. A query made only of one of those terms could never succeed, regardless of how relevant the underlying content was.

Search quality is often the cumulative effect of many small retrieval decisions. You find them by testing real searches.

What is domain-customised enterprise search?

Enterprise search retrieves information from a bounded body of content belonging to an organisation: product catalogues, reports, documentation, websites, knowledge bases, internal repositories or business applications.

A general search engine needs to work across an enormous universe of information. Enterprise search has a different advantage: it can become very good at understanding one particular corpus.

Domain-customised enterprise search uses the structure, terminology and observed search behaviour of that corpus to improve retrieval across:

• specialist vocabulary
• common misspellings
• partial titles
• reordered words
• abbreviations
• conversational phrasing
• catalogue-specific naming conventions
• relevance weighting
• recurring failure patterns found in real search logs

The goal is not to make search infinitely permissive. It is to relax the parts users commonly get wrong while preserving the signals that identify the right result.

How should enterprise search be tested?

It is easy to demonstrate search by pasting the exact title of something in the database. That is not a useful quality test. A better test deliberately damages the query while keeping its underlying intent intact.

Test	Question
Misspell the title	Can the intended item still appear near the top?
Change the word order	Does the right content disappear merely because the terms are arranged differently?
Use only part of the title	Can the search identify what the user probably means?
Use real domain terminology	Does spelling correction preserve legitimate specialist terms?
Add conversational words	Can someone type naturally without every word becoming mandatory?
Use short abbreviations	Are meaningful terms such as AI, EV or 5G actually indexed?

How do we measure whether search actually improved?

For the customer implementation, search quality is evaluated using both real traffic and controlled known-item tests.

Measure 1: searches returning no result

This is the proportion of real live searches that return nothing at all. It tells us how frequently users reach a dead end.

Measure 2: known-item lookup

Take a known published item, alter its name in ways real users alter queries, by misspelling it, changing word order or using only part of the title, and ask whether the intended item still appears within the first five results.

The two measures matter together. Zero-result rate alone can be improved by returning vaguely related content for everything. Known-item lookup alone may ignore live user behaviour. Together they test both coverage and relevance.

Over time, actual failed queries should be added to the test set so search quality becomes a repeatable engineering measure rather than a subjective impression.

Enterprise search vs AI search vs semantic search vs RAG

Capability	Primary job
Enterprise search	Retrieve relevant existing enterprise content.
Semantic search	Retrieve content partly by meaning rather than only exact lexical matching.
RAG	Retrieve information and provide it as grounding context to a language model.
Generative AI search	Retrieve information and generate an answer, summary or synthesis.
Agentic search	Allow an agent to invoke search while completing a larger task.

These capabilities can be combined, but they solve different problems. If the business problem is that the content exists and needs to be found despite an imperfect query, retrieval itself needs to work.

Why we did not solve this with generative AI

Generative AI can add value when a user wants synthesis, comparison or a direct answer. But generation is not required to solve every retrieval problem.

For this implementation, search returns existing records. It does not generate an answer about them. That gives the search layer a narrow and testable job: retrieve the most relevant existing content.

Generation can then be added where another job genuinely requires it, for example summarising several retrieved reports, answering questions from them, conducting research, or allowing an agent to use retrieved information in a larger workflow. That is the same design discipline we apply to AI agents inside business workflows: give each layer a job that can be tested.

Why make enterprise search available through MCP?

People are no longer the only consumers of search. AI assistants and agents also need reliable ways to locate enterprise information.

We therefore expose the same search capability through the Model Context Protocol (MCP), allowing compatible assistants or agents to invoke catalogue search as a tool.

Enterprise content -> Domain-customised search -> Human search interface

or

Enterprise content -> Domain-customised search -> MCP search tool -> AI assistant or agent

The separation matters. The agent does not need to know the entire catalogue. It can call a specialised retrieval capability when it needs information. We covered the wider commercial question in MCP for business.

Where can domain-customised enterprise search be useful?

Product catalogue search

Customers often know what they need without knowing the exact product name. Search can be tuned around catalogue terminology, attributes, user vocabulary and failed queries.

Spare-parts and component catalogues

Technical catalogues contain model identifiers, manufacturer terms, abbreviations and partially remembered names. Domain-specific retrieval can make those conventions part of search.

Research and report discovery

A buyer may know the market or subject they are researching without knowing the exact report title. Search should bridge that gap.

Enterprise websites

Large sites accumulate services, documentation, policies, specifications, resources and support content. Relevance can reflect the organisation's own content rather than generic string matching.

Internal knowledge search

Employees search from memory using acronyms, project language, partial phrases and organisational shorthand rather than exact document names. In most organisations that content sits inside the enterprise applications already in use, which is where the indexing question starts.

AI-agent retrieval

Agents performing research, support, procurement, finance or operational work need the right enterprise information before they reason or act. Where several agents share that retrieval capability, it becomes an orchestration concern, which is what Orchestrik handles for our agent deployments.

What usually goes wrong when enterprises try to improve search?

Treating "add AI" as the search strategy

If relevant records are not being retrieved, a conversational interface does not automatically fix the underlying problem.

Testing only queries that already work

Exact titles make poor search-quality tests. Use misspellings, partial names, abbreviations, longer phrasing and actual historical failures.

Using generic language rules for specialist data

A general dictionary or stop-word list does not know which unusual terms matter in your business.

Maximising recall without protecting relevance

Returning something for every query is not success if irrelevant results overwhelm the intended result.

Treating search as a one-time implementation

Catalogues change, terminology changes and real query logs expose new failure patterns. Search needs ongoing measurement.

How should an enterprise evaluate its existing search?

Start with something simple: search for things you know exist, then deliberately search badly.

Test	What you are trying to learn
Exact name	Does basic retrieval work?
Misspelling	Is search resilient to realistic typing errors?
Changed word order	Does phrasing unnecessarily control matching?
Partial name	Can users find something they remember incompletely?
Domain terminology	Does search understand your vocabulary?
Longer query	Does added context improve or destroy retrieval?
Abbreviation	Are important short terms indexed?
Historical failed search	Have known failures actually been fixed?

For every test, ask three questions: did the right result appear, where did it rank, and what irrelevant results appeared before it?

Does improving search require replacing the existing application?

Not necessarily. Search can often operate as a layer around existing enterprise content. The scoping questions are:

1. Where does the searchable information live?
2. Which fields can be indexed or queried?
3. How frequently does the content change?
4. Which fields carry the strongest relevance signals?
5. What kinds of queries currently fail?
6. Are users searching titles, attributes, summaries or full documents?
7. Should ranking be purely relevance-based, or are controlled business rules also required?
8. Who is allowed to search which information?
9. Does the search need to serve people, AI assistants or both?

The important distinction is between improving retrieval and replacing the source application. They are not necessarily the same project, and separating them is usually the difference between a scoped engagement and a platform migration. Our data and AI practice treats the first as a measurable engineering problem.

Enterprise search readiness checklist

• Do we know what percentage of searches currently return nothing?
• Do we know whether those searches fail because content is absent or because retrieval failed?
• Do we retain real search queries for analysis?
• Do users use terminology different from our content titles?
• Do misspellings materially affect results?
• Does changing word order change the intended result?
• Do longer queries perform better or worse?
• Are important abbreviations and specialist terms indexed correctly?
• Can relevance rules be customised to our corpus?
• Do we have a repeatable search-quality test set?
• Do we continue testing failed queries after launch?
• Could AI assistants or agents also need access to this search capability?

If you cannot answer several of these questions, the first step probably is not choosing another search technology. It is finding out where your existing retrieval breaks.

Frequently asked questions about enterprise search

What is enterprise search?

Enterprise search retrieves information from content belonging to a specific organisation. It can cover internal documents and applications as well as customer-facing websites, catalogues, reports, documentation and knowledge bases.

Does enterprise search need AI?

No. Many search problems can be addressed using information-retrieval techniques, relevance tuning and domain-specific rules. AI becomes useful when the requirement extends beyond finding information into understanding, synthesising or acting on it.

How can enterprise search handle misspellings?

Search can compare imperfect query terms with vocabulary in the indexed corpus and tolerate appropriate spelling differences. In specialist environments, using the enterprise's own vocabulary helps prevent valid domain terminology from being incorrectly corrected.

What is domain-customised enterprise search?

Domain-customised enterprise search tunes retrieval behaviour around a specific organisation's content, terminology and observed query patterns rather than relying entirely on generic search behaviour.

What is the difference between enterprise search and semantic search?

Enterprise search describes the business scope of the content being searched. Semantic search describes a retrieval method that matches partly by meaning. An enterprise search implementation may use lexical, fuzzy, semantic or combined retrieval techniques.

What is the difference between enterprise search and RAG?

Enterprise search returns relevant information. RAG retrieves information and supplies it to a language model as grounding context for generating a response. Reliable RAG therefore still depends on reliable retrieval.

Can AI agents use enterprise search?

Yes. Search can be exposed as a tool that an agent invokes when it needs enterprise information. In our implementation, the same catalogue search capability can also be exposed through MCP for compatible AI assistants and agents.

How should an enterprise measure search quality?

Use real search logs together with a controlled test set containing misspellings, partial titles, changed word order and domain terminology. Measure whether the intended content appears, where it ranks and how much irrelevant content appears above it.

Key takeaways

• Content availability and content discoverability are different problems.
• In one real enterprise catalogue, 53.4% of searches returned nothing even though a random test of failed queries found relevant existing content for 470 of 500 searches.
• Search failures can come from small implementation decisions: mandatory words, long-query behaviour, inappropriate spelling correction or indexing defaults.
• Enterprise search can be tuned around a bounded corpus and its specific vocabulary.
• Not every retrieval problem requires generative AI.
• Search should be measured using difficult queries and actual historical failures, not only exact titles.
• A strong search layer can serve both human users and AI assistants or agents.

Source note

Customer identity is intentionally omitted. Search figures are derived from one week of customer search logs, 10 to 17 August 2026. The projected corrected figures are extrapolated from a random sample of 500 distinct failed queries and remain labelled as projections until equivalent post-deployment production measurements are available.