Can your customers just ask your site for what they want?
Turn search into the fastest way to find, compare, and buy — more sales, fewer dead ends. We build search that understands what shoppers mean, on your catalog.
Every search that fails is a sale you lose.
Search is becoming a conversation. Your customers just ask, in their own words — and expect the right answer back.
- Keyword search matches words, not meaning. Ask for “an SUV for a family of six for long summer road trips” and it returns zero results — even though the lot is full of them.
- The shopper doesn’t reach for filters. They leave. Every failed search is revenue walking out the door.
We build search that reads intent — so those queries return the right vehicles, ranked and tuned on your own catalog.
Leapfrog competitors whose search still guesses.
Most competitors still run keyword search. That gap is your opening.
Move first and better discovery becomes your advantage — before it becomes everyone’s baseline.
Search that understands what customers actually mean.
It runs on Elastic. Elasticsearch is the retrieval engine — keyword, semantic, vector, and hybrid in one platform. Here is how a plain-language query becomes the right result.
“a dressy automatic watch under $2,000 that fits a larger wrist”
Elasticsearch is the retrieval engine; labels are capability names, not a fixed configuration.
The retrieval foundation, built on Elastic
Semantic search
ELSER, Elastic’s native model for retrieval, matches on meaning — so ‘dressy watch’ finds elegant automatics it never shared a keyword with.
Vector search
kNN over dense embeddings finds close matches. Bring your own model — OpenAI, Google, Cohere, or open-source.
Hybrid search
BM25 exact-term and vector signals fused with RRF in one query — built for catalogs that carry both technical specs and natural-language descriptions.
We design and build this retrieval layer on your catalog — choosing where semantic, vector, and hybrid each earn their place.
One clear answer — not a page of links.
A shopper asks a real question and gets one specific answer built from your catalog, with the options cited — not ten blue links to sort through. It is the shopper-facing answer layer.
Reads what it retrieved
The generative layer sits on top of hybrid search — it reads the products just retrieved and composes one direct answer.
Grounded, with sources
It answers only from your catalog, every claim cited — not a free-form guess.
Falls back when unsure
Low confidence? It returns the ranked results instead. No bluffing.
We build this on the same Elastic retrieval underneath — grounded answers, tuned on your own catalog.
One retailer’s rebuild: 33% more sales, 67% more searches.
We have moved commerce search before, with a measured revenue result — and we bring that same relevance discipline to the modern search we build now.
A web-to-print retailer’s product-search rebuild.
Delivered on Elastic App Search — relevance tuning, ranking by sales volume, marketing-controlled merchandising, and autocomplete. Search moved from a cost of doing business to a measured revenue driver.
Read the case studyThat win was Elastic App Search — relevance, ranking, and autocomplete done well. The modern semantic, vector, and hybrid layer above is how we build now — connected by discipline, not by attribution.
A web-to-print retailer — product search rebuilt on Elastic App Search, with relevance tuning, sales-volume ranking, and marketing-run merchandising.
Read the case study Site searchA major American airline group — legacy site search migrated to Elasticsearch Serverless across 5+ domains and 5 languages, with relevance tuning and search APIs.
Read the case study Site searchA global Japanese electrical-engineering conglomerate — fixed the App Search crawler and migrated 200K+ documents to real-time site search across 4 domains.
Read the case study Document intelligenceA legal data-science company — dense Supreme Court judgments turned into short summaries and audio, on a hardened AWS stack SquareShift built and tuned.
Read the case studyPick a starting point — scoped, priced, and fast.
Every engagement is fixed-scope and outcome-first. Start free to prove the value, then buy the exact improvement your catalog needs.
AI Search Readiness Assessment
Find where your search leaks revenue, and how much.
- Zero-results, weak ranking, and missed-intent gaps, quantified
- A prioritized, costed roadmap you can act on
AI Answers POC
See grounded, cited answers on your own catalog before you commit.
- A working answer demo on a slice of your real data
- Proof of value with no build commitment
Relevancy Tuning
Ranking tuned to your catalog and how customers actually search.
- Relevance tuned on your products and real query mix
- Lift on your top revenue-driving queries
Search Performance & Scale Tuning
Fast, stable search under real and peak load.
- Latency and stability tuned for peak traffic
- Headroom for catalog and query growth
Zero-Results Recovery
Fix the searches that return nothing and lose the sale.
- The exact queries returning zero or converting poorly
- Fixes shipped for the top revenue-leaking gaps
Semantic + Hybrid Search Build
Go from keyword to intent-aware search on your catalog.
- Semantic, vector, and hybrid retrieval built on your data
- Keyword search replaced with intent-aware discovery
See it work on your own catalog.
Bring one catalog or find-and-choose journey that is leaking revenue through poor search. In a scoped working session we produce a relevance and discovery prototype on your own data — not a generic demo.