Structured Data for Generative AI | Acodis
Everyone is ready for Generative AI, except your data
✅ Turn your documents into information for LLMs
✅ Structured extraction and contextualisation enables reliability and traceability in Generative AI use-cases
The path to Generative AI starts with structured data from your company.
80% of data is locked in PDF or Word documents. Common problems companies face opening their documents for AI include:
- Long documents: lengthy documents need to be broken down into segments while keeping the context only to feed relevant information to your Large Language models
- Heterogeneous documents: complex documents containing tables, text, and image icons need to be structured into content pieces
- Documents of varying formats: data comes in multiple document formats such as JPG, PDF, and Word with challenges beyond OCR
- Document repository: the ability to query and analyze efficiently not just 1-10, but 1000+ documents at once
4%
According to Gartner research, only 4% of organizations have reported that their data is prepared for AI.
AI-ready Data Requirements
AI-ready data is data that is clean and accurate, well-structured, labeled and easily accessible in interoperable formats so that AI models can reliably consume it at scale for training and operational use.
How to get reliable, secure and comprehensive data?
Control
Every step from a document to structured data is fully within your control with integrated human-in-the-loop (HITL) function.
Accuracy
Set custom parameters for additional assurance that data points are extracted exactly how you want.
Traceability
Trace the output back to the original source of a document.
Collaboration
Designed for seamless collaboration among business and data teams.
How document AI data extraction works
Everything you need to know about AI Data Extraction explained in 59 seconds.
Generative AI in Life Sciences webinar series with Acodis and Microsoft.
Watch the webinars on demand.
Webinar 1
The Path to Generative AI in Life Sciences.
Discover the concrete Life Sciences Generative AI uses cases, how to get your company data AI-ready and what structured data's role in Generative AI initiatives is.
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There is a tremendous potential for Generative AI in driving business impact for Life Science companies toward creating a better life for patients around the globe! Using AI at scale is key to maximizing these benefits for a wider audience. 🌍
However, it's not just about the technology itself. We need to make crucial investments, master essential arts, and infuse a responsible approach to every aspect of AI in order to make the most out of Generative AI for the long term.”
Shalini Trefzer, Global AI/Data Lead for Roche, Microsoft.
Webinar 2
Getting Data Ready for Generative AI
Data ingestion from documents in Life Sciences.
Discover how Acodis transforms complex documents into structured data for Gen AI use cases like Chat / Search (RAG - Retrieval Augmented Generation), Content Authoring, and standardization initiatives.
Why not include the entire document in the prompt for Gen AI application? There are several reasons why you would want to avoid that.
- The first one is the prompt size, which heavily impacts cost and response time. So, the more context you provide to the models, the more costly it will be to generate responses and slow down the application's responsiveness.
- The second reason is that GPT models have certain limitations in terms of how much context they can process.
Florian Follonier, Sr. Partner Solution Architect for Data & AI, Microsoft.
What our clients are saying
“Acodis’ AI capabilities make the platform smarter with every scan. This is a big plus.”
Thomas H.
Syngenta
"OCR, Data-Extraction & AI - perfectly done on business documents like contracts"
Johannes W.
Roche
“Very powerful product packed with many useful features. Built for both experts and non-technical users, backed by a class A support team.”