Search beyond keywords.
Eazy Search AI
Eazy Search AI is search infrastructure for SaaS products, websites, portals and knowledge experiences. It combines keyword and vector retrieval with typo tolerance, synonyms and natural-language intent, adds faceting, filters, permissions and tenant-aware indexes, and offers a conversational Ask layer grounded in results. Product teams control ranking in Relevance Studio, and relevance contracts turn plain-language promises about ranking into executable tests that block unsafe releases.
- 01Query: "lightweight laptop for designer under 80k"
- 02Intent: category=Laptop · use=Design · budget<₹80,000 · preference=lightweight
- 03Retrieval: hybrid (BM25 + vector) · filters applied · re-ranked
- 04Contract check: in-stock rule PASS · exact-SKU rule PASS
One search layer. Multiple experiences.
The same index and ranking engine powers different surfaces, each with its own filters, permissions and presentation.
E-commerce catalogues
SaaS in-app search
Documentation and help centres
Marketplaces
Enterprise portals
Support and ticket search
Indexes are schema-driven, so product catalogues, documentation and tickets can live side by side with different fields, facets and permissions, and a single query can be scoped to one experience or federated across several.
Search architecture
A transparent pipeline where each stage can be inspected and tuned, rather than a black box that returns a list.
- 01
Index
- 02
Hybrid retrieval
- 03
Intent extraction
- 04
Filters and permissions
- 05
Ranking signals
- 06
Personalisation
- 07
Answer or results
Each stage is observable: developers can see which documents each stage returned and why the final order changed, which turns relevance debugging from guesswork into inspection. Latency budgets are enforced per stage.
Relevance Studio
Product managers tune boosts, business rules, freshness, popularity and custom signals without editing backend code, and see the effect on real queries before publishing.
Changes are versioned and previewed against golden queries before publishing, with A/B experiments available for anything contentious. Product teams ship ranking changes the way engineering ships code, with review and rollback.
- Boosts and buries by attribute, category or tenant
- Freshness, popularity and margin as ranking signals
- Synonyms, typo tolerance and query categorisation
- Session-context personalisation controls
- Preview any change against golden queries
Relevance contracts
Search teams need reliability as much as relevance. A relevance contract is a plain-language promise about ranking that the platform converts into an executable test and runs before every index or ranking release.
"Current policy must always rank above archived policy."
PASS · 48/48 test queries
"Out-of-stock products cannot occupy the top 3."
FAILED · 3 violations · release blocked
"Exact SKU must outrank semantic alternatives."
PASS · 120/120 test queries
Ranking behaviour becomes software that is tested, not a setting that is hoped about.
Contracts are written by product managers in plain language and stored beside the index configuration, so a new engineer can read what the business promised about ranking before touching it. When a contract fails, the release is blocked with the violating queries listed, and the fix is either a ranking change or an explicit, reviewed change to the contract itself.
Search intelligence
Analytics that point at fixes, not just numbers.
Zero-result and abandoned searches with suggested fixes
Poorly ranked queries and emerging queries
Content and catalogue gaps
Evaluation suite: golden queries, relevance tests and A/B experiments
Analytics feed the evaluation suite: a zero-result query that recurs becomes a golden query, and a poor ranking that a merchandiser fixes becomes a relevance test, so the system's tests grow from real usage.
Works with tools you already use
Eazy Search AI connects to the systems you already run and to the wider Eazyware intelligence layer, so answers, documents, knowledge and metrics can move between products.
We've got answers
Everything you need to know about Eazy Search AI before early access.
Is it a replacement for Elasticsearch or Algolia?
It is a hosted search layer with hybrid retrieval, a conversational Ask layer and relevance contracts; it can sit alongside or replace existing search depending on your stack.
What are relevance contracts?
Plain-language ranking promises converted into executable tests that run before every release and block changes that violate them.
How fast is indexing?
Incremental updates are near real time; full reindexes run in the background without downtime.
Does it support multi-tenant SaaS?
Yes. Tenant-aware indexes, permissions and per-tenant ranking rules are built in.
Can it answer questions, not just list results?
Yes. The Ask layer answers conversationally, grounded in retrieved results with source references.
When is it available?
Eazy Search AI is Live Soon; developers can request early API access.
Search that understands what people mean.
Request early API access and bring your golden queries.
- No commitment; we reply within two working days
- Onboarding with your own data and documents
- Launch pricing for early-access customers
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