GenAI Demo Day Q2: Spotlight on GenAI Innovations That Deliver

TL;DR – What you’ll learn

Highlights of AI‑powered agent and model innovations from Snowflake, Google Cloud, Precisely, Zenlytic, Fivetran, Zerve, and Dataiku.
How structured & unstructured data meet generative AI in scalable platforms—with demos across Cortex Agents, Gemini models, AI storytelling, data orchestration, and flexible agent workflows.
Key decision criteria: governance, model performance, real‑time interaction, analytics transparency, infrastructure automation, and end‑user trust.

Speaker: James Chawarly, Sr. Developer Advocate @ Snowflake

Agents seamlessly query both structured and unstructured data.
Cortex Analyst generates SQL from semantic models; Cortex Search pulls relevant info from documents.
Users get accurate results, dashboards, SQL code, and revision feedback via a single interface.
Example: Insurance claims agent interface that counts open claims, surfaces stats via SQL, and retrieves specific policy citations with Data‑to‑Charts visualizations.

Speaker: Holt Skinner, Developer Advocate @ Google Cloud AI

Gemini 2.5 Pro supports up to 2M‑token contexts, multimodal inputs, real‑time APIs.
Adds built‑in chain‑of‑thought reasoning, grounding via Google Search with URL citations, function calling, long‑form structured outputs.
User demo showed reasoning logs, error debugging, and controlled layout generation.
Tools: Gemini dev SDK (google-genai), Vertex AI Studio, and GitHub-hosted notebooks covering vision, code generation, math reasoning, and grounded responses.

Speaker: Noan Young, Sr. Director, Product @ Precisely

GraphQL interface unifies parcel, building, flood, foot‑traffic, POI datasets.
Use cases: site project evaluation—foot traffic, competitor density, risk exposure—within a single spatial query.
Demo: Enriching a Denver address with building/business attributes and mapping Vegas neighborhoods for location‑based forecasting.

Speaker: Ryan Jensen, CEO & Co‑founder @ Zenlytic

Users ask in plain language (chat or voice); agents (“Zoe”) respond with insights, visuals, and optional SQL for validation.
Power users can trigger clustering, segmentation, Python-driven charts—all within the conversation.
Examples: ROI breakdown by campaign, persona clustering (“Premium Spenders”, “Subscription Enthusiasts”) with scatter visualizations.

Speaker: Greg Michaelson, Co‑Founder & CPO @ Zerve AI

Zerve OS is a code-native canvas that abstracts infrastructure, orchestration, and distributed compute. It supports Python/R/SQL and transforms casual instruction into DAG workflows.
Agents understand project context, generate pipelines, provision compute, fix errors, and scales tasks using The Fleet abstraction.
Demo: CSV-based “probability of default” model workflow: agent-created blocks, error corrections, EDA, modeling, and runtime orchestration—all visually traceable.
Zerve provides a free Community Tier that includes serverless compute, Git integration, multi-language support, and public canvas templates.

Speaker: Valentina Pina Vivas, Sr. Data Scientist @ Dataiku

Point‑and‑click LLM prompt engineering, schema cleaning, automation.
Live dashboards reacting to social media trends with built‑in model triggers.
Auto-generated slide decks populated from connected, live datasets, with AI-powered story flow.
Additional features: Slack alerts for anomalies, branded AI chat UI, and conceptual AI‑inspired visual art for ice cream.

How can enterprises unify structured and document data for LLM-driven agents?
Platforms like Snowflake’s Cortex use semantic catalogs and unified APIs to bridge SQL and text retrieval.
What enterprise-grade LLMs support long-context real-time workflows?
Gemini 2.5 Pro in Vertex AI with reasoning capability, function calling, and grounded search support.
How to integrate geospatial context natively?
Precisely’s Graph API merges multiple vetted external datasets with native location awareness for richer insights.
How do business users access data insight without SQL?
Zenlytic’s conversational agent interface abstracts complexity yet delivers summaries, charts, and code reference.
How do you operationalize AI by automating data ingestion and infrastructure?
Zerve automates workflow generation, orchestration, compute scaling, and pipeline deployment from plain-language instructions.
How do you enable flexible, GenAI-ready data connectivity at enterprise scale?
Fivetran’s quick connector rollout, schema tracking, and syncing into lakes or warehouses.
How can storytelling and data trend insights be automated for executives?
Dataiku provides live dashboards, slide automation, prompts, and scenario alerting tied to underlying data.

Governance matters: Choose platforms with catalogs, RBAC, citation-style retrieval, and modular workflow control (e.g. Cortex, Vertex, Zerve, Precisely).
Build trust: Accompany agent answers with SQL transparency, citations, visual context, and feedback loops.
Support real-time and large-context workflows: Models like Gemini Pro deliver highly capable, multimodal, low-latency reasoning.
Start use-case first: Begin with a focused domain like claims, marketing, or site evaluation, then expand with successful demos.
Streamline pipelines and computing: Tools like Fivetran, Zerve, and Dataiku help reduce time-to-data and shift engineering focus to insight.
Automate storytelling: Dynamic slide decks, alert triggers, conversational UIs, and interactive graphs speed executive adoption of insights.