Integrating Multimodal AI in Your Apps with Floom

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Webinar to explore the integration of multimodal AI functions in applications with insights from the co-founder and CEO of Floom AI.

Integrating Multimodal AI in Your Apps

MaxBrin,Co-Founder&CEO
CTO Cyber @ Amdocs
Co-Founder & CEO @ Cards
Research & Innovation Lead @ CyberArk
Co-Founder & CEO of Hoger
Chief Architect @ Enabley
About me

What is Floom trying to solve?
The gap between AI Potential
and AI Integration

Where’s the gap?
No robust dev/dep infra
Technical barrier
No Centralized Management
Inspiration Deficit

Existing AI Integration Flow
AI
Model
App
Code
Library
Gateway
Logging, Caching,
Routing, Security etc.
Logic Coating
Data Ingestion, RAG,
Embeddings
Centralization

Floom, AI Orchestrator
Open-Source
Floom
AIOrchestrator
AI Model
Multimodal
App

AI Functions
Direct Inference + Agents
Extract Physical Addresses
Generate Speech from Text
Detect Objects in Image
Detect Emotion in Text
Classify PG rating in Video Ask questions about data
Invoke Tools for action Generate Image

AI Functions
Deploy

RAG (Context)

Now... Run it!

Why did we choose Milvus DB?
Lowest latency
Friendly, well documented APIs
Predictable under load!
Passed our rigorous production testing

How does Milvus DB enable Floom?
Floom relies on Milvus for RAG and general embeddings search (CS/L2/IP)
Default Vector DB for Floom Docker (part of docker-compose)
Default Vector DB for Floom Cloud (Zilliz Cloud)
Full RAG + Multimodal (Video!) experimentation

Most companies keep GenAI as POC
Most product managers focus on the classic GenAI examples
(text+code generation)
Generalizing GenAI is hard, but feasible and well worth it.
GenAI integration is currently reserved to GenAI enthusiasts, Data
Science or Innovation team
Using current dev/dep methodologies, GenAI is not yet
production-grade
Key Lessons

Roadmap:
AI Definition Markup Language
● Generalized approach towards developing and maintaining AI integrations
● Distilled to “AI Functions” (including both agents and direct inference)
● Easy to understand, build, debug and maintain across versatile teams
● Designed for developers with no to little prior AI/GenAI knowledge
● Easily packed in a distributable format
○ Definition (AI Functions: Inference + Agents)
○ Configuration
○ Orchestration

Let’s Floom.
Questions?

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Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...

Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...

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Generative AI Use Cases and Applications.pdf

Generative AI Use Cases and Applications.pdf

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ADP Passwordless Journey Case Study.pptx

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Integrating Multimodal AI in Your Apps with Floom

1. Integrating Multimodal AI in Your Apps

2. MaxBrin,Co-Founder&CEO CTO Cyber @ Amdocs Co-Founder & CEO @ Cards Research & Innovation Lead @ CyberArk Co-Founder & CEO of Hoger Chief Architect @ Enabley About me

3. What is Floom trying to solve? The gap between AI Potential and AI Integration

4. Where’s the gap? No robust dev/dep infra Technical barrier No Centralized Management Inspiration Deficit

5. Existing AI Integration Flow AI Model App Code Library Gateway Logging, Caching, Routing, Security etc. Logic Coating Data Ingestion, RAG, Embeddings Centralization

6. Floom, AI Orchestrator Open-Source Floom AIOrchestrator AI Model Multimodal App

7. AI Functions Direct Inference + Agents Extract Physical Addresses Generate Speech from Text Detect Objects in Image Detect Emotion in Text Classify PG rating in Video Ask questions about data Invoke Tools for action Generate Image

8. AI Functions Deploy

9. RAG (Context)

10. Now... Run it!

11.

12. Why did we choose Milvus DB? Lowest latency Friendly, well documented APIs Predictable under load! Passed our rigorous production testing

13. How does Milvus DB enable Floom? Floom relies on Milvus for RAG and general embeddings search (CS/L2/IP) Default Vector DB for Floom Docker (part of docker-compose) Default Vector DB for Floom Cloud (Zilliz Cloud) Full RAG + Multimodal (Video!) experimentation

14. Most companies keep GenAI as POC Most product managers focus on the classic GenAI examples (text+code generation) Generalizing GenAI is hard, but feasible and well worth it. GenAI integration is currently reserved to GenAI enthusiasts, Data Science or Innovation team Using current dev/dep methodologies, GenAI is not yet production-grade Key Lessons

15. Roadmap: AI Definition Markup Language ● Generalized approach towards developing and maintaining AI integrations ● Distilled to “AI Functions” (including both agents and direct inference) ● Easy to understand, build, debug and maintain across versatile teams ● Designed for developers with no to little prior AI/GenAI knowledge ● Easily packed in a distributable format ○ Definition (AI Functions: Inference + Agents) ○ Configuration ○ Orchestration

16. Let’s Floom. Questions?