KDB.AI
Product Capabilities
Focused feature set
As of July 20, 2026, KDB.AI's features: The 14 stated capabilities span real-time search, metadata filtering, multi-source retrieval, and shape-based matching, which is an unusual inclusion that suggests use cases in pattern detection on sequential data. LangChain is the only major third-party AI framework in the 4-integration list, so buyers building outside that ecosystem should check compatibility before committing.
High confidence · 18 dated facts
Corroborated across multiple dated, sourced facts.
Corroborated across multiple dated, sourced facts.
Capabilities
Multi TenantSimilarity SearchReal Time SearchTime SeriesMulti Source RetrievalSub Second LatencyShape Based MatchingMetadata FilteringVector SearchOn Disk IndexingVector IndexingRetrieval Augmented GenerationMultimodalTemporal Similarity Search
Integrations
SlackGitHubLangchainKdb
Features Change Historydated events · values unlock with a key
Feature removed: Youtube · high significance
Feature added: Temporal Similarity Search
Feature removed: Semantic Search · high significance
Feature removed: Temporal Search · high significance
Feature removed: Huggingface · high significance
Feature removed: Azure AI · high significance
Feature removed: Unstructured IO · high significance
Feature removed: OpenAI · high significance
Every features fact on this pagekey · value · provenance · dated · sourced
| Fact | Value | Provenance | As of | Source |
|---|---|---|---|---|
| features.integration.github | GitHub | company stated | 2026-07-20 | kdb.ai/solutions |
| features.integration.kdb | Kdb | company stated | 2026-07-20 | kdb.ai/learning-hub |
| features.integration.langchain | Langchain | company stated | 2026-07-20 | kdb.ai/learning-hub |
| features.integration.slack | Slack | company stated | 2026-07-20 | kdb.ai/legal/ |
| features.metadata-filtering | Metadata Filtering | company stated | 2026-07-20 | kdb.ai/solutions |
| features.multi-source-retrieval | Multi Source Retrieval | company stated | 2026-07-20 | kdb.ai/solutions |
| features.multi-tenant | Multi Tenant | company stated | 2026-07-20 | kdb.ai/legal/ |
| features.multimodal | Multimodal | company stated | 2026-07-20 | kdb.ai/learning-hub |
| features.on-disk-indexing | On Disk Indexing | company stated | 2026-07-20 | kdb.ai/learning-hub |
| features.real-time-search | Real Time Search | company stated | 2026-07-20 | kdb.ai/solutions |
| features.retrieval-augmented-generation | Retrieval Augmented Generation | company stated | 2026-07-20 | kdb.ai/learning-hub |
| features.shape-based-matching | Shape Based Matching | company stated | 2026-07-20 | kdb.ai/solutions |
| features.similarity-search | Similarity Search | company stated | 2026-07-20 | kdb.ai/solutions |
| features.sub-second-latency | Sub Second Latency | company stated | 2026-07-20 | kdb.ai/solutions |
| features.temporal-similarity-search | Temporal Similarity Search | company stated | 2026-07-20 | kdb.ai/learning-hub |
| features.time-series | Time Series | company stated | 2026-07-20 | kdb.ai/solutions |
| features.vector-indexing | Vector Indexing | company stated | 2026-07-20 | kdb.ai/learning-hub |
| features.vector-search | Vector Search | company stated | 2026-07-20 | kdb.ai/solutions |
Features across Vector DatabasesKDB.AI ranked in place · tap through for each read
| Company | Features read | Facts |
|---|---|---|
| Zilliz | Hybrid search platform | 79 |
| Qdrant | Hybrid search platform | 50 |
| Vectara | RAG platform | 48 |
| Chroma | Hybrid search platform | 46 |
| LanceDB | Hybrid search platform | 38 |
| Activeloop | RAG platform | 37 |
| TopK | Multi-tenancy platform | 33 |
| Pinecone | Reranking platform | 31 |
| Turbopuffer | Multi-tenancy platform | 31 |
| KDB.AI this record | Focused feature set | 18 |
| Epsilla | RAG platform | 16 |
| Vespa | Hybrid search platform | 14 |
| Marqo | Focused feature set | 5 |
No observed features facts yet for Milvus, MyScale and Weaviate.
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.multi-tenant",
"value": true,
"provenance": "company_stated",
"as_of": "2026-07-20",
"source_url": "kdb.ai/legal/"
},
{
"key": "features.integration.slack",
"value": true,
"provenance": "company_stated",
"as_of": "2026-07-20",
"source_url": "kdb.ai/legal/"
},
{
"key": "features.similarity-search",
"value": true,
"provenance": "company_stated",
"as_of": "2026-07-20",
"source_url": "kdb.ai/solutions"
},
{
"key": "features.real-time-search",
"value": true,
"provenance": "company_stated",
"as_of": "2026-07-20",
"source_url": "kdb.ai/solutions"
},
"…"
]
}
}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 overBuild 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.