How our multimodal AI works

See how Dstillery connects behavioral, language, and partner signals to understand real audience intent.

Build once. Activate across supported channels.

See the complete customer journey, not isolated channels

Modern consumers move between channels. They search on mobile, read reviews on desktop, watch streaming TV, and buy in-store.

Traditional programmatic targeting treats these moments separately. Dstillery’s multimodal AI connects them into one understanding of audience intent.

The power of a shared data language

Dstillery's Multimodal AI uses custom vector embeddings to map different data types into a shared intelligence layer. Keywords, web visits, CTV habits, and purchase data can then be evaluated together

This produces a richer picture of people who are in-market for your brand.

Powered by four foundational signal pillars

1

Web and mobile visitation

Behavioral patterns across more than 400 million devices reveal intent at scale.

2

Opted-in search and intent panels

About 2 million privacy-safe users show the search and browsing sequences that lead to buying decisions.

3

LLM and language intelligence

Language models connect cross-channel relationships. They can link CTV viewing to web research or turn a written brief into targetable patterns.

4

Enriched partner data

Specialized signals add depth for CPG, healthcare, B2B, and retail use cases.

Learn how these signals work together in Our data & DS-1: how they fit together.

Start anywhere. Activate everywhere.

The shared intelligence layer gives you flexibility in how you begin and where you deploy.

1. Flexible seed inputs: Start with a keyword list, URL, product category, CRM list, CTV exposure, or plain-language brief.

2. Always fresh: Custom predictive models reach your DSP within 24 hours. They re-score daily to help reduce data decay.

Key benefits

• Omni-channel consistency: Build your audience model once. Deploy it across CTV, audio, display, and web without rebuilding segments.

• Zero data decay: Models train on live behavior, reach your DSP within 24 hours, and re-score every 24 hours.