Is It Time to Rethink the Operating Model for Search Applications?
Is it time to rethink the operating model for search applications?
"Make search work like Google" takes on new meaning with AI.
Search as a Service is emerging as the modern way to deliver enterprise search — but the need for powerful search capabilities has been around for decades. Since the 1990s, organizations have relied on search applications embedded in document management systems, content repositories, and intranets. For many years, users tolerated results that were slow, incomplete, or poorly ranked.
Then Google changed everything. By delivering fast, accurate, and consistent results, along with innovations like featured snippets, answer cards, and its Knowledge Graph, Google set a new benchmark for what search should be. The expectation became simple: search should “just work” — instantly surfacing the right answer, no matter the source.
Now AI is changing the experience yet again. Users no longer just type a couple of keywords — they ask full-text questions and expect full-text answers. With its new AI Mode, Google is experimenting with delivering Q&A-style results similar to ChatGPT, blending traditional search results with conversational responses.
While ChatGPT has captured enormous attention, Google remains the #1 search engine — at least for now. But for businesses running search applications or offering search on their websites, the challenge hasn’t changed:
The Challenge of Consistently Delivering Excellent Search
"How do you deliver the kind of experience users get from Google in search mode, or from ChatGPT in chat mode? "
Enterprise IT departments, committed to optimal search experiences, spend millions on commercial search engines, and often just as much to implement a solution. Open-source search engines like Solr and Elasticsearch revolutionized and now dominate the space. However, they still require hard-to-find search engine experts to build and run search applications. Without constant care and feeding of the applications, the user experience invariably degrades over time.
How “Working Like Google” Keeps Evolving
Google — and competitors like Microsoft and Amazon — keep redefining what it means for search to “work like Google.” It’s no longer just entering a few keywords and scanning a list of links.
Today’s search acts like a question answering system, interpreting natural language queries and returning AI-generated answers, related questions, and rich visuals. For example, asking “how does a heat pump work” produces an AI Overview with a clear explanation, a chat-like AI Mode, and traditional results below.
This mix of NLP, AI/ML, and knowledge graphs is now the norm — and it’s the experience users expect from any search application.
Perhaps an even more impressive example of Google’s evolution is when a user asks a highly specific, technical question — in this case, “explain it to me like I am an engineer” about heat pumps. Instead of returning a generic answer, Google’s AI Mode delivers an in-depth, structured explanation, complete with technical diagrams, step-by-step breakdowns, and related resources.
This transforms search from a simple fact-finding tool into a knowledge discovery and learning experience, capable of adapting to the user’s intent and expertise level. While there is still room for more personalization, this kind of tailored, AI-driven interaction is rapidly becoming what users expect.
For enterprises, the challenge is delivering similar depth and adaptability in their own search applications — often with limited expertise and resources. Many turn to cloud-hosted search services from major providers, but as we’ll see, hosting alone isn’t enough to replicate this level of understanding and engagement.
Cloud-Hosted Search Alone Is Not Enough
To address the shortage of search expertise, many enterprises turn to hosted services like Amazon OpenSearch Service, Amazon Kendra, Microsoft Azure Cognitive Search, Google Cloud Search or Vertex AI Search, and Elastic Cloud. These platforms simplify infrastructure and uptime, but they don’t solve the harder challenge — delivering a search experience that truly meets user expectations.
Modern search applications require expertise that goes beyond provisioning a hosted index. Teams need to:
- Ingest and enrich content from diverse sources for optimal indexing.
- Apply NLP and machine learning to interpret natural language queries.
- Extract entities, classify documents, and build knowledge graphs for Q&A.
- Select and integrate the right large language models (LLMs) — and swap them as technology evolves.
- Monitor model performance, control usage costs, and mitigate risks like hallucinations.
- Design retrieval-augmented generation (RAG) pipelines that combine AI and search for fact-based answers.
Without this blend of infrastructure, AI integration, and continuous tuning, even the most advanced hosted search platform will fall short of the Google-style and AI-driven experiences users now expect.
Different Use Cases Demand Specialized Expertise
While cloud-hosted search may be sufficient for basic website search, more complex scenarios require domain-specific expertise. Developing effective search for these cases often involves custom knowledge graphs, specialized dictionaries (acronyms, jargon), and tailored ontologies and taxonomies to ensure relevance and accuracy.
Examples include:
- E-commerce websites – product attributes, variants, and recommendations.
- Support portals – troubleshooting flows, FAQs, and related content linking.
- Government portals – multilingual access, policy indexing, and compliance filtering.
- Information publishers – topic hierarchies, author metadata, and content freshness.
- Search-based applications (e.g., ridesharing, mapping, reservations).
- Enterprise intranets – broad content aggregation with role-based access.
- Specialized research portals – scientific, legal, or technical datasets with precise search vocabularies.
For these use cases, success depends on blending technical search expertise with deep knowledge of the domain — something most generic hosted search platforms can’t deliver out of the box.
| Regular Hosted Search | Search as a Full Service |
|---|---|
|
Cloud infrastructure hosting & uptime |
End-to-end search operations managed by experts |
|
Basic indexing & API access |
Content ingestion & enrichment from all sources |
|
Customer is responsible for configuration, monitoring, and optimization |
Provider manages monitoring, performance tuning, and ongoing improvements |
|
No AI/ML integration out-of-the-box |
Integrated AI/ML, NLP, vector search and RAG pipelines |
|
Limited vendor support for domain-specific needs |
Domain-specific knowledge graphs, ontologies and dictionaries – as needed. |
|
Fixed capabilities tied to vendor roadmap |
Flexible, modular architecture using best-fit technologies |
|
Basic usage metrics (query, volume, uptime) |
Discovery Search Analytics dashboard measuring performance, relevance, click-through, zero-results queries, and AI answer effectiveness |
|
Pricing based on infrastructure usage |
Predictable monthly cost with performance metrics |
Evolution of “Search as a Service” to a Full-Service Operating Model
- How critical is search to our business outcomes?
- Is search truly one of our organization’s core competencies?
- Do we have — or can we afford to retain — the expertise to design, implement, and maintain a state-of-the-art, AI-ready search experience?
When a Full-Service Model Makes Sense
If search is critical to your business, yet not a core competency — and you lack the in-house expertise to keep it state-of-the-art — it may be time to move beyond basic hosting and adopt full Search as a Service. This means entrusting the entire search experience — from content ingestion to query understanding to delivering accurate, AI-powered answers — to a technology and services partner who can match the user expectations set by Google and modern AI search.
Other parts of the enterprise already operate this way: cloud infrastructure has replaced private data centers, SaaS platforms like Salesforce run entire CRM operations, and payroll, benefits, and accounting are often fully outsourced. Search can follow the same model — evolving into a full-service Business Process Outsourcing (BPO) approach.
A true full-service Search as a Service should include:
- Experts who run the software, manage ingestion pipelines, tune relevance, and continually improve the user experience.
- A flexible, modular architecture that works with both new and existing technology investments.
- Deep expertise in search, natural language processing, AI/machine learning, and knowledge graphs.
- Performance metrics and predictable monthly costs.
This is a different proposition from the typical “hosted search” provider. It’s a complete operating model for AI-ready, enterprise-grade search — delivered and optimized as an ongoing service.
Pureinsights on the Evolution of Search as a Service
Pureinsights sees full-service Search as a Service as the natural evolution of enterprise search — especially for organizations where search is mission-critical, but not a long-term core competency. We recognize that adopting a new operating model takes careful evaluation, and we’re ready to help you explore the path at your own pace.
You can learn more about our Managed Services for AI and Search Applications or begin with a Search Application Assessment. In this assessment, we’ll examine the current state of your search, identify gaps, and outline a roadmap for improvement. When and if that roadmap aligns with a shared vision for full Search as a Service, Pureinsights will be ready to deliver it.
Our approach is powered by platforms like the Pureinsights Discovery Platform, which enable us to deliver a scalable, flexible, and cost-efficient Search as a Service solution — often for less than it would cost a company to build, staff, and manage its own search application.
FAQ: Search as a Service
Q1: What is Search as a Service?
Search as a Service is a model where search capabilities are hosted and managed by an external provider, delivering advanced search features via APIs or UI components without requiring in-house infrastructure or engineering.
Q2: How is full Search as a Service different from Hosted Search?
Hosted search focuses on infrastructure and basic indexing, while full Search as a Service includes content ingestion, continuous tuning, AI/ML integration, analytics, and domain-specific optimization — all managed by experts.
Q3: Can Search as a Service include AI-Powered features?
Yes. Modern Search as a Service solutions often incorporate AI capabilities like natural language processing, vector search, knowledge graphs (GraphRAG), and retrieval-augmented generation (RAG) to deliver more accurate and conversational results.
Q4: What type of organizations benefit most from Search as a Service?
Any organization where search is critical but not a core competency — including e-commerce sites, support portals, government portals, publishers, and enterprise intranets — can benefit from outsourcing to a full-service provider.
Q5: How does Pureinsights deliver Search as a Service?
Pureinsights uses platforms like the Discovery Platform to deliver scalable, flexible, and cost-efficient search solutions — often for less than the cost of building, staffing, and managing search applications in-house.