By Jordan Hanley, Senior Solutions Engineer
Background
When thinking about the impact that generative AI can have on our business operations, there is sometimes a tendency to skip over the most pivotal element to the success of any AI project: the cleanliness of the data that is entering the pipeline at the front. Especially when it comes to deploying Retrieval Augmented Generation (RAG) applications.
‘yeah, but what does this have to do with athletes?’ , hear me out…
All elite level athletes, performing at the top of their game, are finely tuned machines. A great amount of care, planning and training goes into this. Would Lewis Hamilton have 7 Formula 1 world championships by training in a mix of manufacturers cars along with a regular diet of burger and fries?
The same applies to our enterprise AI projects. Without rigorous routines around planning, focused training and most importantly correct data, these projects will likely not provide the valuable business outcome they promise.
But what is the impact of all of this? Potentially devastating for our businesses and our AI investments. What happens if our RAG enabled healthcare chatbot started talking to our customers or copilots talking to employees with inaccurate medical information provided as context? Or our gen AI trend analysis tools gave us a bad steer based on incorrect financial data extracted from our accounts?
Solving the problem at source
With that said, we know that data is the lifeblood of our AI applications. Specifically when it comes to semi-structured and unstructured documents, how can we ensure that only the most correct data is accepted as truth and used by RAG applications or to fine tune our models? Can we rely on legacy techniques such as Optical/Intelligent Character Recognition (OCR/ ICR)? Or a jerry-rigged pipeline orchestrating these technologies with tools such as Robotics Process Automation (RPA)?
Hyperscience customers do not think so, and neither should we. Extraction utilizing machine learning models that mimics human behaviors coupled with a human in the loop, drives real results in terms of data correctness. Analysts such as Gartner have called this out in their most recent Market Guide for Intelligent Document Processing Solutions
“In addition to a large number of vendors, buyers perceive low differentiation between these vendors and their products. We forecast this to continue until buyers realize, and vendors reveal, that optimized performance depends upon a portfolio of AI models derived from trained examples representing document type, business function and industry.”
Should we choose to build our business process automation and data pipelines with Hyperscience, we can enjoy the benefit of true human in the loop, where our knowledge workers are able to interact directly with a business process, just in time when the machine is not confident it can reach our target correctness level. We would be able to provide reports on the correctness of the data flowing through our business processes and data pipelines.
It is also important to consider that with this level of accuracy there is also a requirement for an associated high level of automation. To achieve this, extraction models must have the ability to be trained upon our own enterprise documents, broad models based on many different samples or ‘someone else’s data’ are not able to accurately detect and transcribe information from a document with high levels of automation.
Hyperscience’s customers, enabled by the Hypercell platform, are consistently able to achieve 99.5% data correctness, along with 98% automation.
Conclusion
Just as athletes rely on precision training and proper nutrition to perform at their peak, so too must AI systems be fueled with clean, accurate data to succeed. The importance of data quality in AI, especially when dealing with semi-structured and unstructured documents, cannot be overstated.
At Hyperscience, we stand by this philosophy and have seen real-world results, with customers achieving remarkable levels of data correctness and automation. By addressing data cleanliness at its source, we empower businesses to trust the insights their AI generates and make impactful decisions.
In the end, the success of any AI initiative depends on the quality of the data that fuels it.
About the Author
As a member of the Hypersciences international solutions team, Jordan has wide industry experience as both a technical user and provider of automation and customer experience AI powered software. Reach out at [email protected]
