FAQ

No, this is exactly what we want to avoid. This is why we designed our solution in a way that seamlessly integrates into your existing workflows and software landscape. You won’t need to learn another tool. For instance, our AI assistants are integrated into your e-mail client (e.g. Outlook or Gmail) and are automatically scanning incoming messages for relevant tasks like requests for quotations or sales orders. You can continue working in your environment as you did before without constantly switching to another platform.

You don’t need any specific hardware.

In terms of software, our AI assistants are designed to be compatible with almost any existing IT infrastructures. We provide integrations to most ERP systems (e.g. SAP, MS NAV/Axapta/Dynamics, Oracle NetSuite, proAlpha), CRM systems (Salesforce, HubSpot...), and function with the major e-mail clients (Microsoft Outlook, Gmail). Our IT experts will assess your current setup and ensure compatibility.

We provide comprehensive support during from the start, including the initial consultation, ongoing technical assistance, and staff training to ensure a seamless implementation of our AI solutions. Whenever you have a question or concern, you can directly ask us through a private live chat we provide to all clients.

We deliver fast: our solution will typically be up and running in two weeks.

We believe in our product. This is why we offer you a Proof of Concept (PoC) prior to implementation. For free. In the PoC, we will demonstrate how our solution performs in your specific case with real data (after signing a Non-Disclosure Agreement) and you can decide for yourself if you wish to go ahead with our solution. No strings attached.

LLMs improve through continuous learning from new data and feedback loops, for which the model provider is responsible. The Uify AI system can incorporate feedback from users to enhance its performance and accuracy over time, ensuring it stays up-to-date with the latest business requirements. But without using our data from model training.

• LLMs (Large Language Models): LLMs are used for understanding and generating human language. They can automate complex tasks that involve natural language processing, such as drafting emails, analyzing customer feedback, or generating reports. LLMs excel in tasks that require understanding context, nuances, and generating coherent responses, making them superior for automating processes involving complex language interactions.

• OCR (Optical Character Recognition): OCR technology converts different types of documents, such as scanned paper documents, PDFs, or images captured by a digital camera, into editable and searchable data. While OCR is good for digitizing physical documents and extracting text from images, it does not understand context or meaning, limiting its use to simpler data extraction tasks. Classical OCR solutions need to be manually adjusted to every specific documents layout you want to scan and extract information from, making it a tedious task when you are dealing with many documents formats (e.g. every customer sending their own version of a sales order by email).

• RPA (Robotic Process Automation): RPA automates repetitive tasks by mimicking human actions interacting with digital systems, such as data entry, form filling, or process navigation. RPA is effective for structured, rule-based tasks but lacks the ability to handle unstructured data or understand language context, which limits its applicability to more straightforward, repetitive tasks. For instance, you can assign your RPA to extract certain fields from a document (e.g. customer address or price), but it can’t understand it in context and check for correctness.

You don’t have to worry about your data quality. The models we use, namely Large Language Models (LLM), have the massive advantage compared to traditional Machine Learning approaches that they can deal with messy, unstructured data from all kinds of sources, e.g. e-mail free text, PDFs, Word files, Excel sheets, and even hand-written notes and images. The LLM will identify the relevant data points itself and use them for further processing.

No, the LLMs we use come pre-trained and ready to use. No lengthy training, feature engineering, manual labelling, and massive amounts of own business data are required, as you might be used to from traditional Machine Learning (ML) or other process automation techniques. However, we never deliver plain vanilla, off-the-shelf solutions to our customers because the devil is always in the detail. Every time we implement our AI Assistants for new clients, we ensure we understand in-depth your specific workflows and individual business requirements so we can fine-tune our solution to exactly suit your specific business needs and requirements.

LLMs, or Large Language Models, offer significant advantages over traditional AI models and standard Machine Learning (ML) techniques. Traditional AI models and ML algorithms are often designed for specific tasks and require extensive training, feature engineering, labeled data, and domain expertise. In contrast, LLMs are pre-trained on diverse and extensive datasets, allowing them to generalize across various tasks without the need for task-specific training.

• Breadth of Understanding: LLMs can handle a wide range of tasks from text generation to understanding context, while traditional ML models are usually task-specific.
• Minimal Feature Engineering: LLMs reduce the need for extensive feature engineering, as they can understand and process raw text data directly.
• Contextual Awareness: LLMs maintain context over longer passages of text, providing more coherent and relevant outputs.
• Continuous Learning: LLMs can improve over time with new data, whereas traditional models often require complete retraining for significant improvements.