AI-Powered Search for Marketplaces: Lessons for U.S. SaaS

Most marketplace search is still stuck in the “keyword box” era. Customers type a few words, the platform matches strings, and everyone pretends that’s a good experience—even when the customer doesn’t know the right terms, misspells the neighborhood, or describes their needs in plain English.

That’s why Scout24’s push toward AI-driven real-estate search is a useful case study, even if you’re not in property tech and even if you operate in the United States. Real estate search is one of the hardest search problems in digital services: huge catalogs, constantly changing inventory, high-stakes decisions, messy data, and emotional intent (“safe area,” “good commute,” “bright apartment”). If AI can improve discovery there, it can improve discovery in retail, e-commerce, and nearly any U.S. SaaS marketplace.

This post is part of our AI in Retail & E-Commerce series, and we’ll treat Scout24’s approach as a blueprint: how AI changes search behavior, what you need in your data and architecture to make it work, and what U.S. digital service providers should copy (and what they should avoid) when they’re trying to drive conversion, retention, and lead quality.

Why AI search matters more than “better results”

AI-powered search isn’t just about relevance; it’s about turning vague intent into a confident next step. In marketplaces, that means fewer dead ends, fewer “no results found” pages, and more customers feeling understood.

Traditional search engines in platforms typically rely on a mix of:

• Keyword matching (including synonyms)
• Faceted filters (price, size, brand, shipping speed)
• Hand-tuned ranking rules

Those tools work fine when customers already speak your taxonomy. But customers don’t. They say “holiday-ready outfits for Miami,” “quiet laptop for grad school,” or “two-bedroom near good transit and daycare.” AI search—especially systems built on modern language models and embeddings—handles meaning, not just strings.

For U.S. SaaS providers, the business impact is straightforward:

• Higher conversion: better discovery increases the odds customers find a match quickly.
• Better lead quality: when the platform understands intent, leads arrive with clearer requirements.
• Lower support burden: fewer “I can’t find X” tickets and fewer manual interventions.
• Stronger retention: search is a habit loop; make it feel smart and people come back.

A line I use internally when evaluating search projects: Search is your highest-traffic salesperson. Train it like one.

What “next-generation search” looks like in practice

AI search is a bundle of capabilities, not a single feature. Scout24’s AI search direction (as described in the case-study framing, even though the underlying page content wasn’t accessible from the scrape) maps cleanly to what’s working across marketplaces.

Natural language search: customers speak normally

The most visible change is allowing customers to search in full sentences. That’s not a UI trick—it requires the system to translate messy human language into structured constraints.

Examples in retail and e-commerce:

• “Warm boots for Chicago winter under $180” → category=boots, insulation/warmth high, price<=180, boost waterproof
• “Gift for my dad who grills a lot” → intent classification + retrieval across grilling tools, spices, thermometers, bundles

Examples in property-style marketplaces:

• “Sunny apartment near a park with a short commute” → proxies for light exposure, proximity to parks, commute-time estimation

If you’re a U.S. platform, this is the unlock: customers can be imprecise, and your system can still be precise.

Semantic retrieval: stop losing customers to vocabulary mismatches

Semantic search uses embeddings (vector representations of meaning) to retrieve relevant items even when the query doesn’t share keywords with the listing.

This matters because sellers describe items differently than buyers search for them:

• A seller lists “mid-century credenza,” a buyer searches “wood sideboard cabinet.”
• A listing says “open-concept,” a buyer searches “no walls between kitchen and living room.”

A strong semantic layer increases recall without forcing you to maintain an ever-growing synonym list.

Ranking that learns: relevance tuned to outcomes, not opinions

Most platforms still rank results based on “what we think is good.” AI ranking uses behavioral feedback (clicks, saves, add-to-carts, inquiries, purchases) to learn what “good” actually means for different intents.

Two practical stances that help teams avoid mistakes:

1. Optimize for the next meaningful action, not raw clicks. Clickbait results create pogo-sticking and churn.
2. Segment by intent. “Cheap running shoes” and “marathon shoes” should not share the same ranking priorities.

Personalization without being creepy

Personalization works when it’s predictable and controllable. In marketplaces, the cleanest personalization signals are first-party and session-based:

• Recently viewed categories
• Filters used
• Price range affinity
• Brand affinity
• Location and delivery constraints

A good rule: personalize ranking, not truth. Don’t hide inventory; reorder it.

The data and architecture U.S. platforms need (and often don’t have)

If you want Scout24-level ambition for AI-powered search, the bottleneck usually isn’t the model. It’s your data.

Structured attributes: your catalog must be machine-readable

AI can infer some things, but marketplaces win by standardizing attributes:

• Retail: size, material, compatibility, warranty, condition, energy rating
• Real estate: floor level, elevator, parking, renovation year, noise proxies, transit proximity

Start by identifying your “top 30” attributes that drive conversion. Then measure coverage. If only 40% of listings have the attribute filled, your AI features will feel inconsistent.

Unstructured content: photos and descriptions are a goldmine

Most listings are messy text plus images. That’s not a problem—it’s fuel.

Practical uses:

• Extract missing attributes from descriptions
• Auto-generate improved listing summaries (with strict guardrails)
• Detect policy violations or low-quality listings
• Power multimodal search (“show me sofas like this photo”)

For retail and e-commerce, multimodal search is especially valuable during Q4 gift seasons and post-holiday exchanges, when people often have a screenshot, not a product name.

Feedback loops: you can’t improve what you don’t log

If you’re not already collecting the following, you’re flying blind:

• Query → results shown
• Query reformulations (what users type next)
• Click depth (how far they scroll)
• Saves/wishlists, add-to-cart, purchase/inquiry
• “Hide this” / “not relevant” signals
• Zero-result queries and near-zero-result queries

In my experience, zero-result queries are a product roadmap. They tell you what customers want that your catalog or taxonomy can’t express.

From real estate to retail: where the playbook transfers cleanly

Scout24’s domain is property search, but the mechanics map directly onto U.S. digital services.

1) Conversational discovery improves conversion on complex catalogs

When products or listings have many constraints, conversational or guided AI reduces abandonment.

Retail examples:

• Electronics: compatibility (“works with my laptop model”), use-case (“for video editing”), constraints (“quiet fans”)
• Apparel: fit preferences and occasion (“wedding guest dress, not too formal, sleeves”)

The win is not that the AI “talks.” The win is that it turns fuzzy preferences into filters and ranking signals.

2) “Search + recommendations” becomes one system

Most teams run search and recommendations separately. Customers don’t experience them separately.

A unified approach means:

• Search results that include “similar items” and “better matches” explanations
• Recommendation carousels informed by the active query intent
• A consistent understanding of attributes and synonyms across the site

3) Lead qualification and routing becomes part of the product

Real estate platforms care about inquiries; retail platforms care about checkout; B2B marketplaces care about qualified leads. The AI search layer can pre-qualify and route.

Examples for U.S. SaaS marketplaces:

• Route high-intent shoppers to live chat or concierge
• Ask one clarifying question when the query is ambiguous
• Detect “research mode” vs “ready to buy” and adjust UX accordingly

A practical implementation roadmap (what I’d do first)

If you’re running a U.S. marketplace or a SaaS platform with heavy discovery needs, here’s a sequence that avoids the common “big AI launch that underdelivers.”

Step 1: Fix the boring foundations

Do this before you ship shiny AI features:

1. Create an attribute schema and measure coverage
2. Clean duplicates and normalize units (inches vs cm, “M” vs “Medium”)
3. Improve listing quality guidelines and enforcement
4. Instrument search logs end-to-end

Step 2: Add semantic retrieval as a safety net

Semantic retrieval helps immediately with:

• Synonyms
• Misspellings
• Long-tail queries

It’s also the best way to reduce “no results” experiences.

Step 3: Add an intent layer (classification + extraction)

Teach the system to identify intent types such as:

• Price-sensitive
• Brand-specific
• Use-case-driven
• Urgent/near-me
• Comparison shopping

Then extract constraints into structured filters.

Step 4: Tune ranking to business outcomes

Define success metrics that align with revenue, not vanity:

• Add-to-cart rate per query group
• Inquiry-to-close rate (for lead marketplaces)
• Return rate impact (bad matching often increases returns)
• Time-to-first-good-result (measured as the first save/purchase)

Step 5: Introduce controlled personalization

Start with session signals, then expand to user profiles if you have clear consent and value.

Snippet-worthy stance: Personalization should feel like “the platform remembers,” not “the platform watches.”

Common questions leaders ask (and the honest answers)

“Will AI search replace filters?”

No. Filters are still the most efficient way to express hard constraints. AI search should set filters automatically, recommend them, and reduce the number of steps.

“Do we need a chatbot interface?”

Not necessarily. Many of the best AI search improvements are invisible: better retrieval, smarter ranking, and clearer query understanding. A chat UI is optional.

“How do we keep AI from hallucinating?”

Don’t let it invent facts about listings. Use AI to retrieve and summarize what exists in your catalog and metadata. If an attribute isn’t known, the system should say so or ask a clarifying question.

“What’s the fastest win?”

Improve discovery for long-tail queries. That’s where semantic search and intent extraction pay off quickly, and it’s where your competitors are often weakest.

Where this is heading in 2026 for U.S. digital services

AI-powered marketplace search is shifting from “find results” to design the decision: summarize tradeoffs, highlight constraints, and help customers commit.

For retail and e-commerce, expect more:

• Multimodal search (photo + text + preferences)
• On-page comparison summaries (“why this matches your request”)
• Post-purchase intelligence (fit feedback, exchanges, reordering)
• Inventory-aware personalization (recommend what’s actually available in your region)

If Scout24’s direction tells us anything, it’s that the winners won’t be the companies with the fanciest demos. They’ll be the ones that do the unglamorous work: catalog structure, feedback loops, and ranking tied to real customer outcomes.

If you’re building a marketplace or a SaaS platform in the U.S., the question to ask your team isn’t “Should we add AI search?” It’s “Where are customers getting lost, and how quickly can we make search understand intent the way a great salesperson would?”