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DAXA at JPMorganChase's Technology Innovation Forum

Huseni Saboowala
April 7, 2025
min read

One thing is clear from last week’s conversations with technology and business leaders at JPMorganChase :

Banks are ready to scale GenAI—but not if it means losing control of their data.

Daxa was honored to be one of the few startups selected—from a large field of applicants—to present at JPMorganChase's Technology Innovation Forum. A strong validation of our work at the intersection of AI apps/agents and enterprise data—advancing GenAI adoption that is secure, responsible, and scalable.

What we consistently hear from banks about production-grade GenAI:

  • AI must respect data boundaries—even at scale
  • Identity is critical—compliance depends on who sees what, and when
  • Governance can’t slow delivery—speed and safety must go hand in hand

Kudos to JPMorgan Chase for setting the tone on responsible AI adoption in financial services.

Sharing a couple of (slightly scrappy!) snapshots from our session—grateful for the opportunity and proud of the Daxa team that made this happen: Basavaraj Hooli, Nishan Jain—and big thanks to Vinod Nanu and Mark Halliwell, our partners and collaborators on this journey.

What’s a Rich Text element?

The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

Static and dynamic content editing

A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!

  1. edcbbkn
  • yvbjnklm

How to customize formatting for each rich text

Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.

from langchain.document_loaders.csv_loader import CSVLoader    
from langchain_community.document_loaders.pebblo import PebbloSafeLoader

loader = PebbloSafeLoader(
          CSVLoader(file_path),
          name="acme-corp-rag-1", # App name (Mandatory)
          owner="Joe Smith", # Owner (Optional)
          description="Support RAG app",# Description(Optional)
)

documents = loader.load()
vectordb = Chroma.from_documents(documents, OpenAIEmbeddings())

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