{"slug": "sciene-ai-companion-building-an-autonomous-customer-success-platform-on", "title": "Sciene AI Companion: building an autonomous Customer Success platform on Databricks", "summary": "Sciene launched an AI Companion platform on Databricks to automate customer success workflows for Quartile, the world's largest retail media optimization platform. The platform ingests data, runs AI inference, and serves results in real time, enabling CSMs to focus on strategy and relationships. Deployed across Quartile's entire CS organization managing over 1,000 brands, it improves efficiency and data-informed service.", "body_md": "AI Companion, making customers feel unique at scale\n\nby [Renata Fencz](/blog/author/renata-fencz), [Solano Campos](/blog/author/solano-campos), [Rodrigo Mohr](/blog/author/rodrigo-mohr) and [Ricardo Morandini](/blog/author/ricardo-morandini)\n\nSciene develops AI products that standardize and scale high-volume, relationship-centered enterprise workflows. When Quartile, the world’s largest retail media optimization platform, managing performance marketing for over 1,000 brands, started using Sciene's platform to scale their Customer Success operation, it transformed the way they work across geographies and time zones.\n\nIn digital advertising, Customer Success Managers (CSMs) are the bridge between an agency and its customers, analyzing campaign performance, preparing strategy presentations, proactively flagging problems, and maintaining the ongoing relationship that keeps accounts healthy and growing. This role demands both analytical depth and personal touch. At scale, though, that combination breaks down.\n\nCSMs spend hours each week assembling decks from scratch, reconstructing account context, and triaging accounts without a systematic read across the book of business. Without the right tooling and technology, they can’t keep pace.\n\nThis is a perfect application for generative AI. Sciene, extending beyond Quartile, is trying to solve how to introduce AI efficiency to relationship-driven business processes while maintaining vital personalization to the essential human connection.\n\nSciene's platform had to solve three problems simultaneously:\n\nFrom data availability to CSM presentation, Sciene has a very narrow processing window. The platform must ingest, model, run AI inference, and serve results at a real-time pace. All pipelines, AI workloads, and the operational layer must use the same governed source of truth — making Databricks the architectural solution.\n\nTo address all the requirements, Sciene built an AI Companion platform, structuring three modules to solve distinct bottlenecks in how users are served:\n\nNone of this replaces the CSM's judgment — it removes the work that was getting in the way of it. The CSM still owns the account, the relationship, and the call on what to do next; AI Companion just makes sure they walk into every customer conversation with the context already in hand.\n\nSciene AI Companion is deployed across Quartile's entire CS organization managing over 1,000 brands. With data assembly and drafting handled, CSMs spend more of their week on what's always been the core of the role — deeper account strategy, sharper customer conversations, and the judgment calls that matter the most. The impact flows downstream: customers receive faster, more data-informed service and the business operates a CS organization that scales with efficiency.\n\nAI Companion's architecture was built on one principle: all consumers (data pipelines, AI models, dashboards) must read from the same governed tables with no synchronization drift.\n\nSciene evaluated that the alternative to use a stitched stack of separate databases, compute, and AI serving infrastructure would create massive maintenance overhead due to multiple data copies, complex reconciliation, and inevitable data drift. Databricks eliminates this entirely by using:\n\nAs a result: data engineering never ships custom exports, the application never recomputes analytical logic, AI workloads never maintain their own data store. One foundation, no drift.\n\nThe same architecture supports all three AI Companion modules in slightly different ways:\n\nThe Databricks Data Intelligence Platform's unified architecture enables new capabilities. Sciene is exploring deeper integration with the Databricks AI platform, including Databricks Apps for scalable AI inference, MLflow for experiment tracking across AI Companion's multiple generation tasks, and Unity Catalog Lakeflow Connect for extending governance and data ingestion across the growing number of AI-generated assets the platform produces.\n\nAs Databricks releases new features, Sciene's platform incorporates them, making AI Companion faster and more capable without requiring architectural changes.\n\nTo learn more about how Sciene partners with Databricks to build data- and AI-native products for enterprise workflows, visit [sciene.com](https://sciene.com) or reach out to your Sciene or Databricks contact.\n\nSubscribe to our blog and get the latest posts delivered to your inbox.", "url": "https://wpnews.pro/news/sciene-ai-companion-building-an-autonomous-customer-success-platform-on", "canonical_source": "https://www.databricks.com/blog/sciene-ai-companion-building-autonomous-customer-success-platform-databricks", "published_at": "2026-06-16 14:29:06+00:00", "updated_at": "2026-06-16 16:26:22.346796+00:00", "lang": "en", "topics": ["generative-ai", "ai-agents", "ai-products", "ai-infrastructure", "ai-startups"], "entities": ["Sciene", "Databricks", "Quartile", "AI Companion", "Unity Catalog", "MLflow", "Lakeflow Connect"], "alternates": {"html": "https://wpnews.pro/news/sciene-ai-companion-building-an-autonomous-customer-success-platform-on", "markdown": "https://wpnews.pro/news/sciene-ai-companion-building-an-autonomous-customer-success-platform-on.md", "text": "https://wpnews.pro/news/sciene-ai-companion-building-an-autonomous-customer-success-platform-on.txt", "jsonld": "https://wpnews.pro/news/sciene-ai-companion-building-an-autonomous-customer-success-platform-on.jsonld"}}