Beehive AI

Name: Beehive AI
Brand: Beehive AI
Rating: 4.7 (12 reviews)

Beehive AI is using the power of generative AI to help companies transform their business through a deeper understanding

Est 2016|Valuation $20.1M|Raised $6.7M|10 Ref Customers

LinkedIn 2K|Website|FTEs 27|HQ South San Francisco, California, US

MarketsCustomer Experience Platform (CXP)Voice of the Customer

01Competitive Landscape

Because Beehive AI intersects VoC suites, text analytics, and qualitative research tooling, competitor selection depends on primary workflows and priorities.

Narrative comparison

- Versus Qualtrics/Medallia: Beehive AI is positioned as an intelligence layer that ingests from multiple systems (Qualtrics, Medallia, SurveyMonkey, Zendesk, Intercom). Organizations standardized on a VoC suite should assess incremental value versus native text analytics to justify an additional vendor. - Versus Dovetail/ATLAS.ti: Beehive AI is suited for scaled open-ended survey and comment analysis with quantification, while Dovetail and similar repositories may better support deep interview workflows.

Market adoption signals

Third-party review sites show a modest number of reviews with a high average rating; the footprint appears small but includes some reviewers identifying as enterprise users.
The vendor references enterprise customers on marketing pages; customer logos in our capture were not legible and should be validated.

	Beehive AI	Qualtrics (XM + Text Analytics)	Dovetail (qual research repository)
Starting Price	Not publicly listed (sales-led)	Not publicly listed for enterprise suites (sales-led)	Public/self-serve tiers exist (team pricing varies)
Primary Market / Use Case	Enterprises seeking generative-model-driven theme discovery and segmentation across multiple unstructured sources	Large VoC programs that combine survey collection and analytics within one ecosystem	UX research teams focused on interview repositories, multimedia evidence, and collaborative synthesis
Key Strength	Emphasis on models trained on a customer's proprietary data and multi-source ingestion	End-to-end XM/VoC suite with established enterprise procurement/security posture (typically)	Strong interview and evidence-management workflows for qualitative research
Key Limitations	Public pricing opaque; limited public independent validation of performance claims; potential weaker support for interview workflows	Can be complex and costly; enterprise deployment considerations vary by organization	Less oriented to scaled operational VoC dashboards and multi-source operational integrations

01Competitive Landscape

Because Beehive AI intersects VoC suites, text analytics, and qualitative research tooling, competitor selection depends on primary workflows and priorities.

Narrative comparison

Versus Qualtrics/Medallia: Beehive AI is positioned as an intelligence layer that ingests from multiple systems (Qualtrics, Medallia, SurveyMonkey, Zendesk, Intercom). Organizations standardized on a VoC suite should assess incremental value versus native text analytics to justify an additional vendor.
Versus Dovetail/ATLAS.ti: Beehive AI is suited for scaled open-ended survey and comment analysis with quantification, while Dovetail and similar repositories may better support deep interview workflows.

Market adoption signals

Third-party review sites show a modest number of reviews with a high average rating; the footprint appears small but includes some reviewers identifying as enterprise users.
The vendor references enterprise customers on marketing pages; customer logos in our capture were not legible and should be validated.

	Beehive AI	Qualtrics (XM + Text Analytics)	Dovetail (qual research repository)
Starting Price	Not publicly listed (sales-led)	Not publicly listed for enterprise suites (sales-led)	Public/self-serve tiers exist (team pricing varies)
Primary Market / Use Case	Enterprises seeking generative-model-driven theme discovery and segmentation across multiple unstructured sources	Large VoC programs that combine survey collection and analytics within one ecosystem	UX research teams focused on interview repositories, multimedia evidence, and collaborative synthesis
Key Strength	Emphasis on models trained on a customer's proprietary data and multi-source ingestion	End-to-end XM/VoC suite with established enterprise procurement/security posture (typically)	Strong interview and evidence-management workflows for qualitative research
Key Limitations	Public pricing opaque; limited public independent validation of performance claims; potential weaker support for interview workflows	Can be complex and costly; enterprise deployment considerations vary by organization	Less oriented to scaled operational VoC dashboards and multi-source operational integrations