Fisent First to Offer Applied Generative AI Process Automation Solution with Full Model Optionality
Fisent Technologies is First to Offer Applied Generative AI Process Automation Solution with Full Model Optionality
TORONTO (August 28, 2024) – Fisent Technologies today announced it is the first to deliver an Applied GenAI Process Automation solution that offers full model optionality. The Fisent BizAI solution allows enterprises to select the right LLMs for the processes they are looking to automate and to effortlessly shift processes to different models, adjusting for variables such as content, structure, size, and data latency.
Generative AI models continue to advance at a rapid pace and new LLMs are being introduced by well-funded market challengers at almost the same velocity. In a recent CB Insights survey of enterprise customers using LLMs, 34% of enterprises report currently using at least two LLM developers, 41% are using three, and 22% report using four or more LLM developers. This has created an urgent need for systems that can seamlessly add and shift the GenAI models best suited to the specific use case requirements.
Fisent BizAI capabilities essential to model optionality include:
- Support initial model selection based on use case fit
- Facilitate seamless switching between models if better options emerge or as initial choices evolve
- Offer flexibility in host selection and management for both Foundational models (when available) and proprietary LLMs
- Enable the models to process any content source/type
- Integrate seamlessly with any application/business process management (BPM) layer
Optionality Use Cases
“Different AI models are designed and trained for specific tasks based on their architecture, capabilities, and the nature of the data with which they were trained,” explains Adrian Murray, Founder and CEO of Fisent. “Where Gemini excels at audio transcription relative to other models, GPT has an advantage over others for text-based analysis, and the LLMs developed on internal data bring proprietary intelligence to the fold. BizAI allows all of these options to be harnessed to automate and optimize processes.”
The following real-world BizAI use cases underscore how certain LLMs are more appropriate than others depending on cost, structure, latency, and other factors.
For a health insurance company seeking to process care notes received from providers, BizAI might be utilized to analyze content and expedite the review process before invoicing. The complexity of this process and the requirement for high accuracy necessitates a state-of-the art LLM like GPT4o.
For an enterprise looking to streamline its credit dispute adjudication process, BizAI could be used to classify the supporting evidence contained in millions of documents. Given the high volume of content, a more cost-efficient and lightweight model like Llama 3.1 would be employed.
Fisent’s BizAI enables end-to-end automation of repetitive business tasks by leveraging the power of Foundational GenAI models. Fisent has helped harness transformative GenAI models for enterprises from small banks to Fortune 500 corporations, transforming processes that currently require intensive human interpretation of content.
About Fisent
Fisent Technologies is revolutionizing how business is performed by enabling the utilization of GenAI models for the automation of common business processes. By creating a bridge between the enterprise application layer and the rapidly evolving ecosystem of Foundational GenAI models, Fisent’s Applied GenAI Process Automation solution, BizAI, enables process automation of time-consuming repetitive tasks such as contract reviews, new customer onboarding, and order processing. By combining the power of new technologies and its industry expertise, Fisent creates solutions that are fast, efficient, and cost-effective for clients of all sizes, enabling them to achieve their desired business outcomes. For more information, please visit: www.fisent.com.
Media Contact
Mark Tordik
PR for Fisent
215-644-6502
[email protected]
Release distributed via GlobeNewswire
