KDB.AI

KX Software Limited·Kdb.ai
Product Capabilities

Hybrid search platform

As of September 3, 2026, KDB.AI's features: 11 stated capabilities covering hybrid search and multi-tenancy give enterprise buyers a reasonable checklist, but one listed integration means most connection work lands on the buyer's engineering team.

Backed by 12 dated facts
Every value derives from a dated, sourced capture — open any fact for its source.
Capabilities
ExplainabilityHybrid SearchMulti ModalMulti Source RetrievalMulti TenancyNatural Language InterfaceOn Disk IndexingReal Time SearchTemporal Similarity SearchTime SeriesVector Search
Integrations
Langchain
Features Change Historydated events · values unlock with a key
Feature removed: Metadata Filtering · high significance
Feature removed: RAG · high significance
Feature removed: Shape Based Matching · high significance
Feature removed: Sub Second Latency · high significance
Feature removed: Vector Indexing · high significance
Feature added: Explainability
Feature added: Hybrid Search
Feature added: Natural Language Interface
Every features fact on this pagekey · value · provenance · dated · sourced
FactValueProvenanceAs ofSource
features.explainabilityExplainabilitycompany stated2026-09-03kdb.ai/solutions
features.hybrid-searchHybrid Searchcompany stated2026-09-03kdb.ai/learning-hub
features.integration.langchainLangchaincompany stated2026-09-03kdb.ai/learning-hub
features.multi-modalMulti Modalcompany stated2026-09-03kdb.ai/learning-hub
features.multi-source-retrievalMulti Source Retrievalcompany stated2026-09-03kdb.ai/solutions
features.multi-tenancyMulti Tenancycompany stated2026-09-03kdb.ai/legal/
features.natural-language-interfaceNatural Language Interfacecompany stated2026-09-03kdb.ai/solutions
features.on-disk-indexingOn Disk Indexingcompany stated2026-09-03kdb.ai/learning-hub
features.real-time-searchReal Time Searchcompany stated2026-09-03kdb.ai/solutions
features.temporal-similarity-searchTemporal Similarity Searchcompany stated2026-09-03kdb.ai/learning-hub
features.time-seriesTime Seriescompany stated2026-09-03kdb.ai/solutions
features.vector-searchVector Searchcompany stated2026-09-03kdb.ai/solutions
Features across Vector DatabasesKDB.AI ranked in place · tap through for each read

No observed features facts yet for Marqo, Milvus and MyScale.

Get features for kdb.ai via API / MCPevery field dated and sourced
RESTopen tier
GET https://api.bixel.com/v1/companies/kdb.ai/facts?dimension=features

{
  "data": {
    "facts": [
      {
        "key": "features.explainability",
        "value": true,
        "provenance": "company_stated",
        "as_of": "2026-09-03",
        "source_url": "kdb.ai/solutions"
      },
      {
        "key": "features.hybrid-search",
        "value": true,
        "provenance": "company_stated",
        "as_of": "2026-09-03",
        "source_url": "kdb.ai/learning-hub"
      },
      {
        "key": "features.integration.langchain",
        "value": true,
        "provenance": "company_stated",
        "as_of": "2026-09-03",
        "source_url": "kdb.ai/learning-hub"
      },
      {
        "key": "features.multi-modal",
        "value": true,
        "provenance": "company_stated",
        "as_of": "2026-09-03",
        "source_url": "kdb.ai/learning-hub"
      },
      "…"
    ]
  }
}
MCPfor agents
# any MCP client (Claude, agents)
const record = await bixel.get_company_facts({ domain: "kdb.ai", dimension: "features" })

# returns the features record above,
# each value with its source_url + as_of,
# ready to reason over
Build on the company record. One key, REST + MCP, every signal dated and sourced back to the page it came from.

Public record, read from companies' own pages and boards. Every fact dated and sourced; provenance (observed vs company stated) shown inline.