Search is the interface.
The index is the infrastructure.
One behavioral index powers search, listings, and category pages — and becomes the ground truth for Synerise Customer Agent. Built into behavioral AI infrastructure end to end: what customers do today reshapes what they find tomorrow.
<15ms
Query response time (p95)
99.2%
Search relevance score
50M+
Indexed items per deployment
+165%
Search conversion uplift vs. platform-native search
Interactive Showcase
See AI Search in action
Explore each search capability — from semantic understanding to visual product discovery.
Transformer-based query understanding that matches intent — not just keywords. Queries and catalog items share the same 768-dimensional embedding space for conceptual relevance scoring.
From query to conversion
Natural language decomposed into structured signals. Results re-ranked by individual behavioral affinity.
Query Decomposition
Re-Ranked Results
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Five layers of search intelligence
From semantic understanding to personalized re-ranking — each layer adds precision to every query.
Semantic Vector Search
Queries are embedded into the same behavioral vector space as catalog items using BaseModel.ai representations. Semantic matching retrieves conceptually relevant results — even when exact keyword matches don't exist.
Personalized Re-Ranking
Search results are re-ranked in real time based on each user's behavioral profile. Two users searching the same query see different result orderings — optimized for their individual purchase probability.
Dynamic Faceting & Filtering
Facets are generated dynamically from the result set — not pre-computed. Filter counts update in real time as users narrow their search, ensuring zero dead-end filter combinations.
Natural Language Query Understanding
An NLU layer parses complex natural language queries into structured intent, entity, and filter components. 'Red dress under $50 for summer' is decomposed into color, category, price, and seasonality signals.
Merchandising Controls
Business rules overlay algorithmic ranking — pin items, boost brands, bury out-of-stock, and apply promotional rules. Visual merchandising tools let non-technical teams control search results without code.
Query execution pipeline
Every query traverses five stages — from raw text to personalized, merchandised results — in under 15 milliseconds.
Query Processing
Tokenization, spell correction, NLU parsing, intent classification
Retrieval
Hybrid BM25 + vector search with configurable fusion weights
Scoring
Multi-signal relevance scoring (textual, behavioral, business rules)
Re-Ranking
Personalized re-ranking using real-time behavioral embeddings
Post-Processing
Faceting, deduplication, diversity injection, result decoration
Proof in production
Proven in head-to-head A/B tests
Live-traffic A/B tests on client e-commerce platforms. Baselines anonymized per client agreement; uplifts are relative to each baseline.
vs. Native search of a leading commerce platform
Full-funnel A/B test on live traffic; sessions tracked across engagement, conversion, and revenue contribution.
| KPI (search sessions) | Platform native | Synerise AI Search | Results |
|---|---|---|---|
| Click-through rate | 3.42% | 11.41% | +233% |
| Conversion rate | 0.61% | 1.62% | +165% |
| Share of revenue from search | — | 12.85% | — |
Platform-native search caps engagement and conversion. Behavioral AI ranking compounds both — more clicks, more orders, and a meaningful share of total revenue flowing through search.
vs. Best-of-breed SaaS search
Three-way A/B test running ~2 weeks — platform native search, SaaS vendor, and Synerise AI Search — with traffic split across all three variants.
| KPI (search sessions) | SaaS vendor | Synerise AI Search | Results |
|---|---|---|---|
| Zero results ratelower is better | ~1.8% | ~1.0% | −44% |
| Core revenue | undisclosed | undisclosed | +5.0% |
| Ads revenue | undisclosed | undisclosed | +4.6% |
| Total revenue | undisclosed | undisclosed | +4.99% |
| Orders | undisclosed | undisclosed | +5.36% |
Against a dedicated SaaS search vendor, Synerise delivered measurably better outcomes across every disclosed KPI — lower zero results rate, higher revenue, more orders.
vs. In-house tuned ElasticSearch
15-day A/B test with equal traffic split between tuned ElasticSearch (variant A) and Synerise AI Search (variant B).
| KPI (search sessions) | Tuned ElasticSearch | Synerise AI Search | Results |
|---|---|---|---|
| Zero results ratelower is better | ~2.7% | ~0.9% | −66% (3× fewer) |
| CTR | 36.1% | 36.4% | +0.8% |
| Add-to-cart rate (30 min) | 28.0% | 27.5% | −1.8% |
| Sales conversion rate (30 min) | 9.6% | 9.5% | −1.0% |
| Core revenue | undisclosed | undisclosed | +4.8% |
| Ads revenue | undisclosed | undisclosed | +1.0% |
| Total revenue | undisclosed | undisclosed | +4.2% |
| Revenue per session | undisclosed | undisclosed | +3.7% |
| Avg. order value | undisclosed | undisclosed | +4.4% |
A well-tuned in-house ElasticSearch is a serious baseline — late-funnel conversion rates were essentially at parity. But behavioral AI ranking unlocked upside the tuned system couldn't: 3× fewer dead-end queries, larger baskets, higher revenue per session. The gap isn't in the known queries — it's in the long tail.
vs. Global market leader (e-commerce search)
A/B test on a fashion marketplace across multiple markets measuring purchase conversion in 30-min, 2h and 24h windows after search.
| KPI (search sessions) | Market leader | Synerise AI Search | Results |
|---|---|---|---|
| Conversion rate (30 min) | 5.18% | 6.32% | +22% |
| Conversion rate (24 h) | 8.16% | 11.12% | +36% |
| CTR | ≈ parity | ≈ parity | ≈ 0 |
| Zero results ratelower is better | ≈ parity | ≈ parity | ≈ 0 |
Parity on relevance metrics, decisive wins on conversion — and a +36% relative uplift in the best market (conversion in 24h window). Outcome: full replacement of the incumbent engine across all tested markets.
Zero results rate is a lower-is-better metric: a negative value means fewer searches ending with no products — the intended outcome. All other rows follow the usual direction, where a negative value is a decline against the baseline.
“Match on relevance. Win on conversion.”
Beyond keyword search
Autocomplete, visual search, voice search, and federated search — all powered by the same behavioral intelligence layer.
Autocomplete
Predictive suggestions with behavioral popularity weighting and typo tolerance
+42% search completionVisual Search
Upload an image and find visually similar products using CNN feature extraction
+18% discovery rateVoice Search
Speech-to-text with NLU parsing for mobile and IoT search interfaces
12 languages supportedFederated Search
Search across products, content, FAQs, and categories in a single unified query
<20ms across 4 indicesSearch Analytics
Zero-result queries, click-through rates, and conversion funnels per query cluster
Real-time dashboardsA/B Testing
Compare ranking algorithms, relevance models, and merchandising strategies with statistical rigor
Bayesian significanceWhy search matters
Search punches above its weight
Aggregate performance across Synerise AI Search deployments. Quality metrics computed at the query level; engagement and revenue at the search-session level.
| KPI | Average | Best observed |
|---|---|---|
| Zero results rate | ~4.8% | ~0.9% |
| Click-through rate | ~22.5% | ~26.7% |
| Add-to-cart rate (30 min) | ~9.2% | ~11.7% |
| Purchase conversion (30 min) | ~2.8% | ~3.5% |
| Share of sessions using search | ~9.6% | ~10.5% |
| Share of revenue from search | ~16.7% | ~29.7% |
~10% of sessions generate ~17% of revenue — and up to ~30% in the best-performing deployments. Improvements to search compound disproportionately on the bottom line.
API-first · AI agent ready
Built for developers and AI agents
Every Synerise AI Search capability ships as a documented HTTP API — ready to be consumed by your storefront, your services, or autonomous AI agents using LLM tool-use and MCP-style integrations.
Search
APIRun semantic, personalized queries against your catalog. Pass user context, filters, and ranking signals — get back ranked results in milliseconds.
View docsListings
APIPower category and collection pages with the same behavioral ranking engine. Personalize listing order per user without rebuilding your catalog.
View docsConfiguration
APIManage indices, synonyms, merchandising rules, and ranking weights programmatically. Ship search changes through your own deployment pipeline.
View docsAnalytics
APIPull query, click, and conversion analytics into your own dashboards or LLM tools. Diagnose zero-result queries and measure ranking experiments.
View docsEndpoints are stable, predictable, and easy to expose as tools to LLM agents — from autonomous shopping assistants to internal copilots that query your catalog and analytics on demand.
Open developer docsMeasure what you ship
Analytics for AI Search, Listings & Rankings
From out-of-the-box KPIs to fully custom reports — measure search performance and the effectiveness of any use case you build on top of it.
Predefined statistics, ready via API
A standard library of search, listings, and rankings KPIs is computed continuously and exposed through the Analytics API. Pull them into your BI stack, internal dashboards, or LLM tools without building a tracking pipeline.
- Total searches & sessions with search
- Click-through rate on search results
- Conversion rate from search (orders & revenue)
- Revenue and AOV attributed to search sessions
Custom metrics, reports & dashboards
Define your own conversion events, funnels, and KPIs over arbitrary time windows. Build dashboards and scheduled reports that match how your team measures search — not a fixed template.
- Arbitrary conversion definitions (orders, signups, content engagement)
- Custom time windows — 30 min, 24 h, 7 d, or any range you need
- Cohort, segment, and device breakdowns out of the box
- Reusable dashboards with drill-down to query and session level
Effectiveness of any search, listings & rankings use case
Every scenario built on top of AI Search, Listings, or Rankings — autocomplete tweaks, merchandising rules, personalization intensities, A/B tests — can be measured end-to-end against the conversion definition that matters to you.
- Per-scenario uplift on conversion, revenue, and AOV
- A/B test results with statistical significance
- Side-by-side comparison of ranking and merchandising variants
- Attribution across search, listings, and rankings touchpoints
Statistics are computed continuously and exposed via the same API surface as the rest of AI Search — easy to feed into your BI stack, your warehouse, or an LLM agent that reports on search health on demand.
See full statistics referenceUse cases
Real scenarios marketers ship with AI Search
Eight production-ready playbooks from the Synerise Hub — from abandoned search recovery to multilingual catalogs and shoper context search results.
Abandoned search scenario
Send your customers an email offer with products from their abandoned search
Promoting customer favourite brands in search results
Filter the products returned by AI search to the product's of customer's favorite brand
Suggest items more expensive than customer's average purchase
Filter the search results to products that are more expensive than customer's average value of products bought
Product-recipe matching for enhanced culinary experience
Increase average order value with smart product and recipe integration
Handle non-existing phrases in search engine
Create custom query rules to handle phrases that don't exist in the search engine catalog
Visual search
Boost your search engine with visual search possibilities
Using Rules to Boost Specific Brands and Products in Search Results
Create rules to promote brands and specific items
Search engine for a brand with multiple languages and currencies
Use Synerise AI Search in multiple languages and currencies
Search that converts.
See how behavioral AI search delivers up to +165% conversion uplift over platform-native search — with sub-15ms response times across 50M+ item catalogs.