KDB.AI
Market Positioning
Vector Database
As of September 3, 2026, KDB.AI's positioning: The homepage headline 'The Scalable Vector Database for AI' is a broad claim, but the sub-headline narrows it usefully to contextual and time-series search, which is a more defensible and specific territory than the headline alone suggests.
Backed by 3 dated facts
Every value derives from a dated, sourced capture — open any fact for its source.
Every value derives from a dated, sourced capture — open any fact for its source.
On their homepage
“The Scalable Vector Database for AI”
The vector database for contextual and time series search. Build AI apps, find patterns in your data, and mix structured with unstructured data.
Positioning Change Historydated events · values unlock with a key
Positioning changed: Target Segment · high significance
Positioning changed: Target Segment
Positioning changed: Target Segment
Positioning changed: Target Segment
Positioning changed: Target Segment
Positioning changed: Target Segment
Every positioning fact on this pagekey · value · provenance · dated · sourced
| Fact | Value | Provenance | As of | Source |
|---|---|---|---|---|
| positioning.category | vector-database | company stated | 2026-09-03 | kdb.ai |
| positioning.h1 | The Scalable Vector Database for AI | company stated | 2026-09-03 | kdb.ai |
| positioning.tagline | The vector database for contextual and time series search. Build AI apps, find patterns in your data, and mix structured with unstructured data. | company stated | 2026-09-03 | kdb.ai |
Positioning across Vector DatabasesKDB.AI ranked in place · tap through for each read
No observed positioning facts yet for Activeloop and Milvus.
Get positioning for kdb.ai via API / MCPevery field dated and sourced
RESTopen tier
GET https://api.bixel.com/v1/companies/kdb.ai/facts?dimension=positioning
{
"data": {
"facts": [
{
"key": "positioning.category",
"value": "vector-database",
"provenance": "company_stated",
"as_of": "2026-09-03",
"source_url": "kdb.ai"
},
{
"key": "positioning.h1",
"value": "The Scalable Vector Database for AI",
"provenance": "company_stated",
"as_of": "2026-09-03",
"source_url": "kdb.ai"
},
{
"key": "positioning.tagline",
"value": "The vector database for contextual and time series search. Build AI apps, find patterns in your data, and mix structured with unstructured data.",
"provenance": "company_stated",
"as_of": "2026-09-03",
"source_url": "kdb.ai"
},
"…"
]
}
}MCPfor agents
# any MCP client (Claude, agents)
const record = await bixel.get_company_facts({ domain: "kdb.ai", dimension: "positioning" })
# returns the positioning 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.