Build or Buy Enterprise AI Search? The Real Cost of Doing It Yourself

Build or Buy Enterprise AI Search? The Real Cost of Doing It Yourself

Adding search to an AI application can appear straightforward. Choose an embedding model, create an index and connect it to your data. The first demonstration may work within weeks.

But the real investment begins after the demo.

The problem: a working first version is not a complete search product

A search prototype is usually tested on a limited and controlled document collection, or one business case only. Scalable production systems must work with information coming from different teams, formats and business contexts.

A system that performs well on HR documents may struggle when Finance introduces invoices, spreadsheets and contracts. Scanned documents add another challenge. Different departments use different terminology, while new applications create new search requirements.

The system must also return the right information to the right person and enforce user access. This means supporting user permissions, team workspaces, access controls and, in many cases, single sign-on for instance.

These are not small finishing touches. Adding them after the initial architecture has been built will require significant development work.

There's also a human factor to consider. Enterprise search systems are usually built by one or two engineers working alongside their other responsibilities. If they move to a different project - or leave the company - the knowledge needed to maintain and evolve the system often leaves with them, leaving the platform without a clear owner.

The hidden cost of building enterprise AI search

The initial version of the Search is only a small fraction of the total cost.

Once the system in production, engineering teams have to maintain the document-processing pipelines, evaluate retrieval quality, support new formats and adapt the system to new requirements and usage . They must also monitor how efficiently the search layer finds the evidence required by an AI agent.

Accuracy alone is not enough. If an agent needs too many searches to answer one question, latency and operating costs increase. Production-ready search must therefore balance relevance, speed and efficiency.

The build-versus-buy question is not :

Can our team create a working version ?

It is:

Should our team spend its time maintaining an entire search pipeline when it could focus on business value in our apps ? 

The solution: focus on your application, not the search stack

LightOn Console gives engineering teams ready-to-use APIs for parsing documents, extracting structured information and searching a knowledge base.

LightOn Search combines semantic search, exact-term matching and reranking to identify the passages most relevant to each query. Teams can integrate these capabilities into their own applications and focus on the business workflow and user experience.

This provides a faster path to production while reducing the amount of specialized search infrastructure your team must build and maintain. For European organizations, LightOn also provides a European alternative to US-based AI providers.

Your competitive advantage comes from the application you are creating, not from rebuilding components that are already available.

Want to evaluate the build-versus-buy decision with your own documents? Test LightOn Console.

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