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Users expect search not just to return accurate results, but to do the heavy lifting: Answer a question, summarize research, or even solve a problem.
Originally posted 29th Aug. 2025 on The New Stack
A few years ago, my daughter told me that her school research project was so deep she had to venture all the way to Page 3 of Google. That moment stuck with me because it shows just how ingrained search has become in our lives. “To google” quickly turned into a verb. Search was built for human speed: deliver a shortlist of results, let the user scan, interpret, decide and even go to Page 3 if required. This same foundation has powered e-commerce, content discovery, compliance and countless other applications.
Generative AI has changed expectations almost overnight. Instead of typing keywords, people ask questions in plain language and increasingly complex ones. They expect search not just to return accurate results, but to do the heavy lifting: answer a question, summarize research or even solve a problem.
Generative AI Is Maturing Fast
Despite being a relatively recent phenomenon, there are already at least three levels of GenAI maturity:
- Level 1: Chatbots – “Answer my question.”
- Level 2: Deep research – “Research this and report back.”
- Level 3: Agentic systems – “Solve my problem.”
At Levels 2 and 3, retrieval becomes challenging. Systems may run dozens of searches for a single task. A sluggish retrieval layer doesn’t just slow things down; it can cripple the whole experience.
The Challenge: Delivering Retrieval Accuracy at Scale
Vector databases made similarity search possible, enabling large language models (LLMs) to ground answers in large unstructured data sets. But vector search alone isn’t enough. Production-grade AI search needs more: combining semantic, keyword and metadata retrieval, applying machine-learned ranking and handling constantly changing structured and unstructured data, all at scale.
Trying to bolt these components together across multiple systems quickly hits its limits. Bandwidth, integration overhead and shallow connections create bottlenecks and erode accuracy, which is key since people rarely question the answers the AI provides.
Enter the AI Search Platform
The AI search platform is a new class of infrastructure that makes retrieval smarter, faster and more scalable by uniting classical search techniques with modern AI: vector and tensor search in embedding spaces, full-text search for precision, multistep ranking and real-time inference, using machine-learned models and tensor math. It enables accurate search at machine speed with filtering and ranking to ensure only the most relevant answers surface instantly. The AI search platform is critical in simplifying the development and deployment of generative AI at every maturity level.
Why This Matters for Enterprises
Mainstream data platforms, such as Snowflake or Postgres, now include basic vector search capabilities. That’s fine for entry-level GenAI chatbots, but not for customer-facing deep research or agentic AI use cases where speed, scale and accuracy deliver competitiveness.
For CIOs, this has created a split:
- Basic enterprise GenAI: supported by incumbent platforms, “good enough” for simple internal tasks.
- Advanced enterprise GenAI: for demanding customer-facing use cases, where only AI search platforms can keep up.
In this landscape, pure-play vector DBs risk being marginalized, sandwiched between incumbent data platforms for simple use cases, and AI search platforms that deliver scale, performance and accuracy.
Companies that adopt AI search platforms early will set the pace in this new era. Search is no longer just a utility; it’s becoming the backbone of AI-driven business. And no doubt, the backbone of my daughter’s research.
