Search that understands behavioral intent.
Semantic vector search fused with behavioral personalization. Every query returns results ranked by individual purchase probability — not just keyword relevance.
Key stats
- <15ms — Query response time (p95)
- 99.2% — Search relevance score
- 50M+ — Indexed items per deployment
- +165% — Search conversion uplift vs. platform-native search
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.
- 768-dimensional query-item embedding alignment
- Handles misspellings, synonyms, and intent variations
- Cross-language semantic matching support
- Learned relevance from click-through behavior
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.
- Per-user affinity scores injected into ranking
- Session-aware context (device, time, referrer)
- Behavioral embedding similarity between user and items
- Configurable personalization intensity per query type
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.
- Real-time facet computation from result sets
- Zero dead-end guarantee on all filter combinations
- Hierarchical category navigation with counts
- Custom attribute facets from any catalog field
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.
- Intent classification across 12 query types
- Named entity extraction (brand, color, size, price)
- Query expansion with behavioral synonyms
- Spell correction and phonetic matching
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.
- Pin, boost, bury, and hide rules per query pattern
- Promotional slot reservation in result grids
- Margin-weighted ranking for profitability optimization
- Scheduled merchandising rules for campaigns
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
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 | Uplift |
|---|---|---|---|
| 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
A/B test running in parallel "best of breed" SaaS vendor and Synerise AI Search
| KPI (search sessions) | SaaS vendor | Synerise AI Search | Uplift |
|---|---|---|---|
| Zero results rate | ~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 ranked first on every KPI measured — and the SaaS vendor underperformed the retailer's own native search on conversion.
vs. In-house tuned ElasticSearch
A/B test with equal traffic split between an in-house ElasticSearch tuned over years and Synerise AI Search.
| KPI (search sessions) | Tuned ElasticSearch | Synerise AI Search | Uplift |
|---|---|---|---|
| Zero results rate | ~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% |
Late-funnel conversion was 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 | Uplift |
|---|---|---|---|
| Conversion rate (30 min) | 5.18% | 6.32% | +22% |
| Conversion rate (24 h) | 8.16% | 11.12% | +36% |
| CTR | ≈ parity | ≈ parity | ≈ 0 |
| Zero results rate | ≈ 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.
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 completion
Visual Search
Upload an image and find visually similar products using CNN feature extraction
+18% discovery rate
Voice Search
Speech-to-text with NLU parsing for mobile and IoT search interfaces
12 languages supported
Federated Search
Search across products, content, FAQs, and categories in a single unified query
<20ms across 4 indices
Search Analytics
Zero-result queries, click-through rates, and conversion funnels per query cluster
Real-time dashboards
A/B Testing
Compare ranking algorithms, relevance models, and merchandising strategies with statistical rigor
Bayesian significance
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.
Frequently asked questions
How fast is Synerise AI Search?
Sub-15ms p95 query response time across catalogs of 50M+ items, with results re-ranked per user in real time.
How does AI Search differ from keyword search?
Queries and catalog items share the same 768-dimensional behavioral embedding space. Results are matched on intent and re-ranked by each user's behavioral profile — not just keyword overlap.
Can AI Search handle multiple languages?
Yes. Semantic matching works across languages and supports 12-language phonetic autocomplete out of the box.
How is personalization applied to search results?
Per-user affinity scores from real-time behavioral embeddings re-rank results so two users searching the same query see different orderings optimized for their individual purchase probability.
What kind of uplift can we expect?
Live A/B benchmarks show up to +233% click-through and +165% conversion versus platform-native search, with parity-or-better results against best-of-breed SaaS vendors and tuned ElasticSearch.
Does Synerise AI Search support merchandising rules?
Yes. Pin, boost, bury, and hide rules, promotional slot reservation, margin-weighted ranking, and scheduled merchandising are all available alongside algorithmic ranking.
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
Run semantic, personalized queries against your catalog. Pass user context, filters, and ranking signals — get back ranked results in milliseconds.
Listings
Power category and collection pages with the same behavioral ranking engine. Personalize listing order per user without rebuilding your catalog.
Configuration
Manage indices, synonyms, merchandising rules, and ranking weights programmatically. Ship search changes through your own deployment pipeline.
Analytics
Pull query, click, and conversion analytics into your own dashboards or LLM tools. Diagnose zero-result queries and measure ranking experiments.
Endpoints 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.
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.