Blend360 Launches Generative AI Suite to Innovate the Data Science Space

Blend360, a leading data science solutions provider, is introducing a suite of new generative artificial intelligence features to help drive clients’ business performance.

‍According to the company, Blend360's foundation in innovative data science solutions seamlessly integrates with the generative AI landscape.

With a commitment to ethical and responsible AI practices, Blend360 has crafted a comprehensive suite of offerings that guide clients from strategy to full-scale deployment. These offerings, born out of substantial investment over the past year, underline Blend360's commitment to advancing AI services.

With support from Blend360's data and engineering teams, clients benefit from increased efficiency, shorter time-to-market, and reduced overheads, ensuring maximum value realization, according to the vendor.

Blend 360’s new full-service suite of offerings includes:

‍AI strategy:

  • Risk/reward/feasibility prioritization process
  • Security, architecture, and algorithm design
  • Engineering foundations

AI augmented development:

  • Code conversion and co-pilot
  • Data discovery, preparation, and cleansing accelerator.
  • Headless design accelerator; pluggable model architecture

Data science:

  • Model selection; core model library with strengths and weaknesses
  • Training and reward harness for extending models
  • Viper accelerator for standard algorithm performance selection

"At Blend, we are extremely excited by the applications of generative AI. We see how smart, ethical applications can drive meaningful impact for ourselves and our clients. At every corner of our business, we are challenging our teams to think AI-First and fuel bold visions with our clients," said Adam Mincham, head of go-to-market at Blend360. “The advancement that LLMs are enabling is mind-blowing; an explosion of new models unlocking new use cases. I'm incredibly excited about how it enables companies to experiment, find value, and expand, quickly ramping to scale without a significant technology implementation phase."

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