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Workflow Automation with Generative AI – New Layer of Intelligence

DATAFOREST goes beyond robotic automation and offers both the ability to automate tasks and analyze workflows with generative AI, suggesting optimizations or even more efficient processes.

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AI Agents

Our AI agents act as the intelligent layer between the generative AI model and the user or workflow. They bridge the gap between the model's technical capabilities and practical application within the business process. Based on that interpretation, they interact with users, interpret model outputs, and act within the workflow.

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RPA with Generative AI Integration

Robotic Process Automation (RPA) mimics human actions on a computer interface. Generative AI models analyze past RPA actions and identify opportunities for improving or automating additional tasks. The RPA tool can be integrated with the generative AI model to increase its capabilities within the automated workflow.

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Human-in-the-Loop

This solution combines human expertise with Generative AI for business processes in workflow automation. Gen AI models handle routine tasks like data entry, report generation, or initial analysis. Human workers intervene at specific points in the workflow to review the AI's output, make final decisions, or control complex situations that require human judgment.

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Generative AI Solutions for Specific Industries

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Healthcare

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Retail

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Finance

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Manufacturing

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Human Resources

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Logistics

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Real Estate

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Legal

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Insurance

Generative AI Solutions for Specific Industries

healthcare icon

Healthcare

retail icon

Retail

finance icon

Finance

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Manufacturing

Market icon

Human Resources

Manufacturing icon

Logistics

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Real Estate

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Legal

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Insurance

Superpowers of Generative AI
in a Workflow

Generative AI in a workflow is a brainy assistant that is a win-win for efficiency, happy customers, and innovation.
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Turbocharged Productivity – improving processing speed and overall workflow efficiency.
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Accuracy and Consistency – raising the accuracy of processes and decision-making.
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Customer Experience – communication and recommendations personalization based on customer data.
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Cost Reduction – faster processing times and streamlined workflows minimize operational expenses.
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Decision Making – identifying trends, predicting outcomes, and generating valuable insights.
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Scalability – adapting to new situations, making AI models valuable tools for dynamic environments.

Cases of Using Artificial Intelligence and Machine Learning

Check out a few case studies that show why DATAFOREST will meet your business needs.

Reporting & Analysis Automation with AI Chatbots

The client, a water operation system, aimed to automate analysis and reporting for its application users. We developed a cutting-edge AI tool that spots upward and downward trends in water sample results. It’s smart enough to identify worrisome trends and notify users with actionable insights. Plus, it can even auto-generate inspection tasks! This tool seamlessly integrates into the client’s water compliance app, allowing users to easily inquire about water metrics and trends, eliminating the need for manual analysis.
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of valid input are processed

<30 sec

insights delivery

Klir AI
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Automating Reporting and Analysis with Intelligent AI Chatbots

Gen AI Hairstyle Try-On Solution

Dataforest developed a top-on-the-market Gen AI hairstyles solution for US clients. It consists of the technology for the main product and the free trial widget. The solution generates hairstyle try-ons using the user's selfie. We had two primary objectives. The first was to ensure high accuracy in preserving the user's facial features. The second one was to create hairstyles that showcase the most natural hair texture. Our vast experience in Gen AI and Data science helped us achieve 94% model accuracy. It guarantees high-quality user face resemblance and natural hair in the generated photos. And it results in much higher user satisfaction, making it #1 on the market.
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sec photo delivery

90%

user face similarity

Beauty Match 2
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Gen AI Hairstyle Try-On Solution

Improving Chatbot Builder with AI Agents

A leading chatbot-building solution in Brazil needed to enhance its UI and operational efficiency to stay ahead of the curve. Dataforest significantly improved the usability of the chatbot builder by implementing an intuitive "drag-and-drop" interface, making it accessible to non-technical users. We developed a feature that allows the upload of business-specific data to create chatbots tailored to unique business needs. Additionally, we integrated an AI co-pilot, crafted AI agents, and efficient LLM architecture for various pre-configured bots. As a result, chatbots are easy to create, and they deliver fast, automated, intelligent responses, enhancing customer interactions across platforms like WhatsApp.
32%

client experience improved

43%

boosted speed of the new workflow

Botconversa AI
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Improve chatbot efficiency and usability with AI Agent

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Technologies of Artificial Intelligence and Machine Learning

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Lama 2
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Zilliz
Weaviate icon
Weaviate
Stable Difusion icon
Stable Difusion
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Qdrant
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Pix2Pix
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Pinecone
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Pgvctor
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OpenAI
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Momento
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Mixtral
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Llava
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Hugging Face
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Faiss
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Chroma
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ChatGPT
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Activeloop
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YOLO
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SageMaker
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Pillow
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NLTK
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Keras
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SciPy
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Redis
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Is your business ready to outpace the competition? AI automation is the answer.

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01

Identify and target

The first step is to pinpoint areas in your workflow that are repetitive, time-consuming, or error-prone.
02

Data Prep & Training

You'll provide relevant data sets (text, images, code, etc.) specific to your workflow. This data becomes the fuel for the AI to learn and develop.
03

Model Building

Complex algorithms help the AI identify patterns, understand relationships, and generate outputs. This involves iterative refinement to ensure accuracy.
04

Integration & Connection

Generative AI must connect with your existing workflow tools and software, allowing for smooth, automated task execution.
05

Testing & Optimization

The AI's performance is tested on real-world data. The model can be further optimized based on the results to ensure effectiveness.
06

Deployment & Monitoring

The generative AI model automates tasks and generates outputs. Monitoring performance ensures the AI continues to function optimally.
07

Learning & Improvement

Gen AI continuously learns and improves to stay relevant and deliver better results as it processes new data and interacts with workflow.
08

Feedback & Iteration

A feedback loop helps refine the AI model and ensures it continues to align with your evolving needs.

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FAQ

What is AI workflow automation?
AI in workflow automation leverages the power of artificial intelligence to streamline repetitive tasks within your existing workflows. Imagine a digital assistant that can analyze data, automate processes, and generate creative outputs designed to make your work flow smoother and faster. This frees you up to focus on more strategic tasks.
Name the main workflow automation tools.
Enterprise-grade automation workflow software can be achieved with several prominent platforms. Zapier is a robust integration hub, seamlessly connecting various software applications for automated task execution across your entire workflow. Alternatively, monday.com offers a project management solution with built-in AI-powered workflow automation functionalities, streamlining repetitive tasks within your project lifecycle.
What are the most popular workflow automation AI solutions?
The workflow automation landscape boasts a powerful trio of AI solutions. Robotic Process Automation (RPA), like UiPath, tackles highly repetitive tasks with pinpoint accuracy. Human-in-the-loop AI, championed by solutions like Amazon SageMaker, integrates AI analysis with human decision-making for complex tasks. Finally, Generative AI for workflow automation solutions, exemplified by tools like Gemini, adds a creative spark, automating tasks and generating creative outputs.
List the benefits of the AI automation workflow.
Generative AI workflow automation supercharges efficiency by automating repetitive tasks, freeing human workers for higher-value activities. Second, it enhances accuracy by following defined rules and minimizing human error. Finally, it improves decision-making by analyzing vast data to provide data-driven insights.
What does the organization need for AI-driven systems implementation?
Organizations require a robust foundation to implement AI-driven systems successfully. First, access to high-quality, relevant data is paramount for effectively training AI models. Second, a skilled team of data scientists and engineers is essential for developing, deploying, and maintaining AI systems. Lastly, a supportive organizational culture that embraces innovation and experimentation is crucial for fostering a successful AI implementation.

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