Custom analytics required months of full-stack development. Now, self-serve AI apps connect analysts directly to data models.
A global partner to life sciences companies builds advanced analytics models to identify undiagnosed patients with rare diseases and map complex healthcare journeys.
Providing non-technical marketing professionals and business analysts with access to these complex workflows required a dedicated team for full-stack...
“Databricks Apps enable us to get all of our best data and AI workflows into one place where our analysts can leverage everything we've built in an easy, accessible way.”
Healthcare marketing, communications, and data analytics agency for life sciences.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Real Chemistry's Self-service analytics is part of this use case:
Related implementations across industries and use cases
Silos forced teams to manually chase data. Now, AI agents identify adverse events in seconds, eliminating weeks of cross-referencing.
Navigating 100,000 medical codes manually stalled billing. AI now predicts and routes them to EHRs, cutting manual work by 70%.
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Security fears fragmented AI usage. Now, clear guardrails and automated Slack nudges empower teams to accelerate design and code reviews.
Manually illustrating vintage assets would have tripled production time. Now, a 3-person team uses AI to generate and animate the visuals.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
Custom analytics required months of full-stack development. Now, self-serve AI apps connect analysts directly to data models.
A global partner to life sciences companies builds advanced analytics models to identify undiagnosed patients with rare diseases and map complex healthcare journeys.
Providing non-technical marketing professionals and business analysts with access to these complex workflows required a dedicated team for full-stack...
“Databricks Apps enable us to get all of our best data and AI workflows into one place where our analysts can leverage everything we've built in an easy, accessible way.”
Healthcare marketing, communications, and data analytics agency for life sciences.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Real Chemistry's Self-service analytics is part of this use case:
Related implementations across industries and use cases
Silos forced teams to manually chase data. Now, AI agents identify adverse events in seconds, eliminating weeks of cross-referencing.
Navigating 100,000 medical codes manually stalled billing. AI now predicts and routes them to EHRs, cutting manual work by 70%.
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Security fears fragmented AI usage. Now, clear guardrails and automated Slack nudges empower teams to accelerate design and code reviews.
Manually illustrating vintage assets would have tripled production time. Now, a 3-person team uses AI to generate and animate the visuals.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.