Adapting to the New Era of GenAI-Powered Search

For enterprise organizations, effectively adopting AI-powered search can reduce decision-making time, increase conversion rates, and unlock insights from decades of proprietary data.

Large language models (LLMs) felt like a novelty when they first appeared a few years ago, but they’ve quickly become part of everyday life. This revolution has changed user expectations around search—shifting from search as a tool for finding information to a proactive assistant that helps users take action.

For businesses, that shift in expectation opens new opportunities—and new decisions. There are several ways to bring GenAI-powered search experiences to your website, each offering different levels of customization, depth, and cost.

In this post, we’ll explore the main approaches to using GenAI in search, the benefits these tools can deliver, and some of the common pitfalls to watch out for.

Before we get into it, we'll share a few key AI and search concepts below that we reference throughout this post.

What does GenAI-powered search look like today?

It’s useful to think of AI in search as a spectrum, ranging from improving search relevance to autonomously completing tasks for users, with trade-offs in latency, cost, and complexity.

At one end you have hybrid search, which combines the precision of traditional keyword search with the contextual understanding of semantic search. It ensures that exact matches aren’t missed, but at the same time it can understand what the user is searching for when the query is more vague or complex. With hybrid search, users don’t have to know exactly what they’re looking for to find it. 

The next step on the spectrum is generative answering, which uses GenAI to synthesize information and provide a concise, accurate summary. Google's AI Overviews are a good example of retrieval-augmented generative answering in action—relevant content gets retrieved first, then synthesized into a natural language summary. But enterprise implementations need to monitor for hallucinations and accuracy issues, since even well-designed systems can produce misleading outputs.

Agentic AI goes even further, combining multiple data sources, user behavior, and structured information to help users complete tasks or identify the best solutions for their needs. For example, enterprise customer-support chatbots can access user accounts, documentation, and product data to resolve complex issues end-to-end, while maintaining compliance and auditing trails.

How can you integrate GenAI into your search experience?

When it comes to leveraging these AI-powered search and discovery features on your own website, there are several options, with varying levels of cost, complexity, and flexibility. The two main routes are through built-in GenAI features offered by SaaS platforms, or by building custom GenAI integrations. The right choice depends on your goals, data readiness, and enterprise requirements.

SaaS platform features

Modern search platforms like Algolia, Coveo, and Constructor offer built-in GenAI features that you can use to quickly integrate GenAI onto your site.

Pros to modern search platforms with built-in GenAI features

• Quick time-to-value with minimal engineering overhead.

• Access to vendor-led innovation, including ongoing model updates, new connectors, and infrastructure improvements.

• Predictable cost and maintenance compared to custom solutions.

• Good for prototyping or enhancing existing search with hybrid or generative features.

• Easier to scale initially for high-traffic use cases.

Cons to modern search platforms with built-in GenAI features

• Limited ability to customize workflows or integrate deeply with proprietary systems.

• Potential vendor lock-in and dependency on feature roadmaps.

• Data governance, security, and compliance require additional configuration or oversight.

• Latency and performance tuning may be constrained by the platform.

At Foster Made, we help clients integrate these tools within composable, MACH-based architectures—bridging vendor capabilities with custom workflows and enterprise-grade governance.

Examples of how modern search platforms we leverage use GenAI

• Algolia: Agent Studio enables autonomous AI agents; Shopping Guides generates educational content and guides from catalog data.

• Coveo: Relevance Generative Answering synthesizes answers from indexed content in real time.

• Constructor: AI Shopping Agent handles multi-step ecommerce workflows autonomously.

Custom-built tooling

If your organization has a large amount of proprietary data or highly unique workflows, then fine-tuning an existing LLM or using RAG to connect it with your data sources might deliver more value.

Pros to custom-built GenAI tooling

• Fully customized AI agents and search experiences aligned to proprietary data and workflows.

• Integrates with enterprise systems (CRM, ERP, CMS, analytics) for seamless operation.

• Full control over data governance, compliance, and security.

• Supports complex workflows, multi-step decision-making, and agentic AI use cases.

• Allows you to measure success on your terms: measure impact through search relevance, time-to-answer, support deflection, and conversion lift.

Cons to custom-built GenAI tooling

• Significant upfront investment in infrastructure, AI/ML expertise, and ongoing maintenance.

• Longer deployment timelines; scope must be clearly defined upfront.

• Scaling is expensive and requires careful architecture decisions.

• Performance and latency depend on model optimization and infrastructure.

• Continuous monitoring required for hallucinations, bias, and compliance adherence.

Example of a project we did with custom-built GenAI tooling

• AtlasGPT: A medical knowledge platform built in partnership with Atlas Meditech and Fabriq, providing access to 35+ medical specialties. Users ask clinical questions and receive detailed, cited answers grounded in peer-reviewed content and specialized medical atlases—with full transparency showing exactly where information is sourced from, enabling verification and deeper research.

What makes a GenAI-powered search integration successful?

Whether you're looking at a platform-based GenAI feature or a fully customized solution, there are two keys to a successful integration: the quality, coverage, and structure of your data, and how thoughtfully you implement it.

Why data matters

The effectiveness of generative AI in search and discovery depends heavily on the quality, coverage, and structure of the data you're feeding it. Whether you're generating product summaries, powering generative answering tools, or building a custom-trained LLM, the data you're using needs to be accurate, consistently formatted, and comprehensive.

For example:

• Product descriptions, specifications, and feature lists need to be uniform and up-to-date; inconsistencies or errors can lead to misleading outputs that frustrate users and undermine trust.

• AI agents and chat experiences are only as helpful as the documentation they draw from, so outdated or contradictory content can result in incorrect answers or guidance—and very frustrated users.

Why implementation matters

Thoughtful implementation is critical for ensuring real business value. This is especially true for custom-built solutions, but even platform features are never entirely plug-and-play. Some aspects of implementation to consider include:

• Data segmentation: Large language models need information broken into chunks that preserve context while enabling fast responses. Get this wrong and you'll lose nuance or performance.

• Workflow design: Poorly designed workflows can frustrate users and create integration problems. Good design ensures seamless connections with your data sources and consistent, predictable outcomes.

• Monitoring & governance: You need regular audits of model outputs, compliance checks, and integration points. This ongoing oversight keeps your GenAI search accurate and trustworthy as it evolves.

At Foster Made, we align data architecture, content models, and user workflows before implementation—ensuring GenAI search fits naturally into your digital ecosystem and meets enterprise governance standards.

Do you need GenAI-powered search?

There's enormous hype around AI, but it's ultimately an investment. Start with your goals—not the technology.

Ask yourself

• What problems are you solving?

• What KPIs are you targeting (search relevance, task completion, conversion, support deflection)?

• Do you have clean, structured, compliant data to power an effective solution?

Potential use cases

• Research-heavy organizations: Custom RAG or fine-tuned LLMs surface insights from decades of content, saving time on research that would otherwise be too manual.

• Ecommerce with dynamic inventory: Auto-generated summaries and searchable attributes keep content current and enhance discoverability.

• SaaS with complex support needs: AI agents provide context-aware support drawing from documentation, ticket history, and usage patterns—improving resolution times and user satisfaction.

Turning AI Potential into Real Results

GenAI can significantly improve search and discovery—but only when applied strategically. Without careful planning around data, workflows, integration, and governance, even advanced tools can fall short.

At Foster Made, we help organizations navigate the AI landscape—from strategy and data readiness to custom implementation and optimization. Our goal: turning AI potential into measurable business outcomes while maintaining boutique, highly tailored service.

Ready to get started? Let's chat and explore how we can help you leverage GenAI to improve search and discovery for your organization.