AI site search

Your catalog speaks product. Buyers speak problems.

We connect both. Hybrid search understands natural-language intent without losing exact SKUs, technical terms, customer-specific catalogs or business rules.

Review my search use case
  • Semantic + exact matching
  • Permission-aware retrieval
  • Measurable ranking quality
Relevance preview
Food-safe hose for hot alkaline cleaning
Intentmaterial + temperature + chemical resistance
01
PTFE process hose 12 mmFood-grade · 180 °C · alkaline resistant
96%
02
EPDM cleaning hose 16 mmFood-grade · 140 °C · CIP suitable
91%
03
Hose fitting, stainless steelCompatible accessory · 316L
84%
Illustrative example—not a fabricated customer interface or performance claim.

What changes

Better discovery without giving up control.

The model assists interpretation. Your catalog, permissions and ranking rules remain authoritative.

Intent, synonyms and jargon

Match how buyers describe a job, even when the catalog uses a different technical term.

Exact identifiers stay exact

SKU, model and manufacturer-number queries keep the precision of keyword search.

Grounded answers

Optional summaries can cite the product data and documents used to form the answer.

Behavior as a signal

Clicks, carts and orders inform tuning without silently overriding commercial rules.

Reference architecture

A pipeline you can inspect, test and tune.

We avoid a black-box search replacement. Each stage has a clear job and a measurable output.

  1. 01

    Prepare

    Normalize products, attributes, documents and domain vocabulary.

  2. 02

    Retrieve

    Combine semantic candidates with exact keyword and SKU matches.

  3. 03

    Constrain

    Apply catalog, customer-group, price and availability rules.

  4. 04

    Rank

    Blend relevance, business priorities and observed behavior.

Authenticated search request
Permission gateFiltered
PublicContract AContract B

Only the permitted slice proceeds to retrieval and answer generation.

B2B governance

Permissions belong in retrieval, not in a disclaimer.

The safest result is the one the model never receives. We filter source data before generation so restricted products and commercial terms remain restricted.

  • Customer-group and shared-catalog boundaries
  • Price and product visibility rules
  • Traceable sources and query logs

Proof, not promises

Define success before tuning the model.

A baseline keeps the project honest and shows whether search quality moves buyer behavior.

Zero-result rate

Where are buyers leaving with no viable result?

NDCG / judged relevance

Are the right products actually near the top?

Search-assisted conversion

Does better discovery lead to useful commercial action?