NinjaCat named 'One to Watch' by Snowflake
NinjaCat, an enterprise marketing AI platform built natively on Snowflake, was named a "One to Watch" in the Analytics & Measurement category of Snowflake's fifth annual Modern Marketing Data Stack Report, announced at Cannes Lions 2026. For practitioners, the more substantive signal than the award itself is NinjaCat's disclosed architecture: a model-agnostic agent platform - supporting OpenAI, Anthropic and Google Gemini - that lets marketing teams build autonomous agents without engineering support, on top of a customer's existing Snowflake warehouse rather than a separate proprietary store. NinjaCat's press release credits agency customer VML with a 30% engagement lift from one agent and a 53% conversion-rate increase from another, though these figures come from the vendor's own release and customer testimonial and are not independently verified.
What happened
NinjaCat announced at Cannes Lions 2026 that Snowflake recognized it as a "One to Watch" in the Analytics & Measurement category of Snowflake's Modern Marketing Data Stack Report, now in its fifth year. Per Snowflake's own report page, "Ones to Watch" are vendors selected for strong market momentum, an innovative approach to working with Snowflake, or recently demonstrated customer capabilities - distinct from "Leaders," who are ranked by usage and consumption data across Snowflake's customer base. NinjaCat's release quotes Snowflake CMO Denise Persson crediting the platform's combination of "Snowflake's governed data foundation with purpose-built AI agents that can act autonomously across the full marketing lifecycle."
Technical details
NinjaCat describes itself as an enterprise marketing AI platform built natively on Snowflake, offering a no-code agent builder ("Builder Bob") that lets marketers, not just engineers, create agents to monitor performance, flag wasted spend, and support optimization workflows with configurable human oversight. The platform is model-agnostic, supporting OpenAI, Anthropic and Google Gemini so customers can choose a model per task rather than being locked into a single provider.
Customer evidence
NinjaCat's release cites agency VML as an early enterprise deployment. One agent ("Meta Creative Carol") monitors Meta campaign performance daily across hundreds of creative placements, which VML says produced a 30% increase in consumer engagement. A second agent ("Commerce Funnel Felicity") monitors roughly 20 million e-commerce sessions weekly, which VML says cut analysis time by 90% and lifted site conversion by 53%. These figures originate entirely from NinjaCat's press release and VML's quoted testimonial; they have not been independently audited.
For practitioners
The more durable takeaway is the architecture pattern rather than the award: NinjaCat is one of a growing set of vertical marketing-AI vendors building multi-model, agentic workflows directly on a customer's existing data warehouse instead of requiring data movement to a proprietary platform. That "bring your own model, keep your own data" approach mirrors a broader enterprise AI trend toward composable stacks over single-vendor lock-in, which Snowflake's own report frames as a defining shift in martech for 2026.
Key Points
- 1Snowflake named NinjaCat a 'One to Watch' in its 2026 Modern Marketing Data Stack Report's Analytics & Measurement category, announced at Cannes Lions.
- 2NinjaCat's model-agnostic agent platform supports OpenAI, Anthropic and Google Gemini, letting marketers build autonomous agents on their existing Snowflake data without engineering support.
- 3Vendor-reported customer results (30% engagement lift, 53% conversion increase at agency VML) come from NinjaCat's own release and are not independently audited.
Scoring Rationale
Fundamentally vendor-recognition news with unverified self-reported customer metrics, but the underlying technical detail - a model-agnostic (OpenAI/Anthropic/Gemini) agentic marketing platform built natively on a customer's Snowflake warehouse - reflects a real, recurring enterprise AI architecture pattern worth documenting for practitioners. Kept in the minor band given the promotional framing and lack of independent verification of the cited results.
Sources
Primary source and supporting public references used for this report.
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