Sign In

Michael Gilfix

Chief Product and Engineering Officer

Michael Gilfix is a seasoned software business executive recognized for driving growth and establishing scalable global software product ventures in the enterprise sector.

His forte lies in product innovation, team motivation, business model sustainability, and managing high-performing software portfolios.

With a diverse skill set that includes product development, customer relations, sales, M&A, partnerships, and deal negotiations, Gilfix excels in various facets of business leadership.

He possesses exceptional communication skills suitable for public speaking engagements and engaging with the press.

Gilfix pursued an M.Sc in Computer Science from The University of Texas at Austin after completing a B.Sc in Electrical Engineering from Tufts University.

He also studied at Lower Canada College, enhancing his educational background.

Throughout his career, Gilfix served in several key roles at IBM, including Vice President positions in Data & AI Product Management, Chief Product Officer Cloud Paks & SaaS, and Digital Business Automation, among others.

He held leadership positions such as Chief Product and Engineering Officer at KX and was an Advisor at Domo, Inc.

Gilfix's professional journey showcases a wealth of experience in software development, product management, and sales, making him a prominent figure in the software industry.

Highlights

Jul 10 · linkedin

Sharing this great article about multimodal AI from my colleague Neil Kanungo who always has great insight to share!

Apr 2 · linkedin

Lots of exciting news to share!

First, I'm incredibly proud and excited to share our recent recent of KDB.AI, our high performance vector database. This version has some really special features:

  • Multi modal RAG featuring our novel approach to hybrid search and results scoring. When combined with our pre-filtering, developers can really boost the accuracy of data returned to their LLMs when searching both structured and unstructured data
  • Time similarity search: our unique algorithms that make it possible to radically compress time data (to 1% of its size) or search for patterns without requiring embedding transformation, boasting an impressive 17x performance improvement for fast changing data sets while offering 12x reduction in memory consumption

Start building your applications today with our free client service to give these features a try and supercharge your AI apps:

https://kdb.ai/

We also are announcing our work with LlamaIndex today. We've gone above and beyond in our integration with LlamaIndex to create a compelling developer experience. Ryan Siegler wrote a great article describing how you can get started creating compelling RAG applications.

Announcement: https://lnkd.in/g9vvaEnT

Article: https://lnkd.in/geUXbNKY

#ai #vectordb #vectordatabase

Michael Gilfix
Add to my network

Location

Austin, Texas, United States