Imagine stumbling upon a shiny platform that claims to offer “done-for-you” searches with AI-powered relevance and conversational chat, all rolled into one, perfectly working out of the box for any domain. It sounds too good to be true, doesn’t it?
Plug it in and your users will instantly find exactly what they need. In reality, though, these black-box solutions often feel more like Swiss cheese… full of holes.
When the results start to drift or your customer asks a question that the system hasn’t been configured for, you’re left scratching your head. You can’t see why the answer is incorrect, and you have to wait for the vendor to release a fix. This is a major concern if search is important to your business.
Why Proprietary “AI Search” Tools Can Let You Down
We understand the appeal: attractive marketing, minimal setup effort and an initially tempting price tag to solve “all your problems”. But once you’re locked in, those initial savings will likely disappear.
- Hidden workings: Without visibility of the internal ranking algorithms or data pipelines, it’s impossible to fine-tune or debug, you’ll always need to rely on external support.
- Generic relevance: A one-size-fits-all engine doesn’t understand the terminology of your industry or the subtle ways in which your users search in your specific data and domain. A search solution that magically works for everything is not there yet, and people working actively in the field are perfectly aware of it.
- Vendor lock-in: You’re bound to their upgrade cycle, and migrating away later means rebuilding your entire search setup from scratch.
For a small e-commerce website or small companies, however, these trade-offs might not matter. Basic search can be ‘good enough‘ in low-stakes cases. However, if your search pipeline is critical to customer satisfaction, sales or internal knowledge bases, cutting corners is a recipe for frustration.
When an ai search tool Is Enough, and When It Isn’t
Not every use case requires a fully customized, open, and deeply integrated AI system. There are plenty of scenarios where a plug-and-play solution can deliver real value, especially when the complexity is low and speed matters more than precision or flexibility.
1. Search is not central to your business
Search functionalities are a tiny and irrelevant part of your business/application/website. You added them just to give an additional functionality to your users, you don’t even know if they need it. So you are ok with having something that works in a basic way and don’t adapt that well to your domain or data.
2. Simple and not-domain specific data
Your data follows a simple structure, it has been explored in countless demo and public applications, and your users’ information need are extremely basic. In these scenarios, something very generic could work well enough.
3. Minimal scale
Potentially a small scale can hide many problems of turnkey solutions, so if your data and queries are a small enough set, you may be ok.
But if you need:
- large or changing content collections
- custom ranking rules based on your data
- Specialised vocabulary or multiple languages
- clear explanations of why results rank the way they do
…then a closed-box approach just won’t suffice. You need full control over how the search function works.
Alessandro Benedetti
Director + R&D Software Engineer @ Sease
“I’ve been doing research in information retrieval and building search solutions for many years now, and there’s no single scenario I’ve faced in the real world where going with a closed ‘all solving search solution’ proved to be a good idea.
Building information retrieval systems for specific domains and use cases is extremely complex already, that’s the reason search technologies such as Apache Lucene and Solr offer so many features: you want many tools and building blocks at your disposal to craft a solution that works for you.
Building a generic “all solving” search product is utopia (I partecipated to many such projects in the past, so I know quite well what it means) and won’t out-perform an ad-hoc solution for your domain (obviously re-using well known tech, not suggesting here to build everything from scratch).
Paying a fixed amount of money per year to see all your search problems magically solved is seducing, but we need to face the reality: your data is complex and evolves over time and your users’ needs evolve as well.
Having control over your software, understanding what you need to build and how is vital, unless you want to end up with a solution that behind the hood is just a default configured Apache Solr or vector DB, a minimal data extractor (probably another simple wrapper of other open source technologies) + a LLM around (just a basic prompt to do RAG), sold for an expensive fee.”
Is open source the smart path?
When you choose an open source search engine you’re regaining control over every aspect of how your users find information. Here’s what that actually means in practice:
COMPLETE VISIBILITY
With a closed-box solution, if relevance slips or performance degrades, you have to wait for the support team to resolve the issue. With open source, however, you have full access to logs, query profiles and configuration files. This allows you to see exactly how queries are parsed, which scoring algorithms are used, and why one document is ranked above another. This enables faster root-cause analysis and the ability to resolve issues at your convenience.
Tailored relevance at your fingertips
Every business has unique data sources, industry terms and user behaviours. Open source platforms, such as Apache Solr, OpenSearch, Elasticsearchor Vespa let you incorporate these specifics directly into your search pipeline.
rICH eCOSYSTEM OF PLUGINS AND EXTENSIONS
The open source community is vast and active. If you’re looking for a vector-search plugin for semantic embeddings, an A/B-testing extension for ranking pipelines or a connector to crawl an unconventional data store, chances are someone has already built and shared it for free (or has shared a tutorial to do it). If not, you can adapt existing code or contribute your own improvements back to the project, accelerating innovation for everyone.
No surprises, no lock-in
When you run open source software on your own infrastructure (be it in-house servers or your favourite cloud provider) you decide when to upgrade, which modules to enable and how to back up your data. There are no annual licence renewals or sudden price hikes. If you ever outgrow a particular version, you can fork the code, set up a cluster in a new region or migrate to a compatible managed service. The freedom is yours.
Investing in skill, not fees
The licensing costs of proprietary search platforms can quickly mount up, especially when you add on analytics, advanced relevance features or conversational interfaces. With open source, however, your primary investment is in people, namely training your team to understand index design, query tuning and monitoring best practices. This expertise remains within your company, creating long-term value and ensuring that you are never at the mercy of another vendor’s roadmap.
By centering your search strategy around open source, you turn search from a black-box expense into a strategic differentiator. You gain the transparency to understand every decision your engine makes, the flexibility to adapt it as your business evolves, and the community power to innovate faster than any single vendor could alone.
Start from building a solid search strategy
Before we dive into the specific steps, it’s helpful to understand why a structured approach matters. A solid search strategy ensures that your solution works not only today, but also continues to evolve alongside your content, user behaviour and business goals. By breaking the process down into manageable phases you can reduce risk, demonstrate early successes and develop long-lasting expertise within your team.
- Invest in know-how: train your team in search fundamentals, such as index structure, query parsing and relevance tuning, so that you can diagnose issues and optimise results in-house.
- Start small and scale up by deploying an open source engine in parallel with your existing search. Compare metrics on relevance and performance to validate improvements before switching over completely.
- Keep improving: Monitor user behaviour with analytics dashboards and run A/B tests on ranking tweaks. Use these insights to refine your search experience as user needs and content evolve.
Conclusion: Engineering Over Magic
Search is more than just a tick-box exercise; it’s a strategic tool that influences how people discover, engage with and make decisions about things. Although “magic” turnkey solutions may seem like an easy option, they often have more limitations than benefits. By choosing open source and building real in-house expertise, you can create a flexible, transparent and future-proof search experience that grows with your business.
