Corporate AI with ready-made pipelines

Discover how ValueXI’s pre-configured AI solutions are automating and optimizing workflows across business units — from Sales and Finance to HR and Support.

January 20, 2025

When companies consider adopting Artificial Intelligence, they often envision costly, resource-intensive projects that take months or even years to implement. But what if there were a smarter, more efficient way to bring in corporate AI? That’s where ready-to-use AI pipelines come in — these pre-configured solutions are designed to automate data-driven processes across departments like Sales, Finance, HR, and Customer Support.

How do ready-made AI pipelines work?

Think of these solutions as “smart” functional modules for data analysis and processing. They are integrated effortlessly into your existing infrastructure, adapt to the needs of different departments, and scale as your business grows. This approach not only reduces costs and accelerates AI implementation, but also empower companies to leverage cutting-edge tools like:

Large Language Models (LLMs) for intelligent chatbots, data generation, and enrichment.
Retrieval-Augmented Generation (RAG) for enhanced search within internal knowledge bases.
Optical Character Recognition (OCR) for seamless document digitization.

For instance, among pre-configured AI apps you can choose:

An HR chatbot powered by LLMs can automate candidate screening and handle frequently asked questions.
A lead scoring AI tool for marketing teams identifies high-potential prospects, boosting conversion rates.
A sales forecasting AI system enables personalized customer approaches, increasing average order value.

At ValueXI, we’ve curated a collection of specialized pipelines that work with data to build targeted corporate AI and that are ready to deploy, whether in the cloud or on-premise. These modules integrate seamlessly into your existing infrastructure, adapt to your unique business needs, and scale effortlessly as your company grows.

Whether you prefer a user-friendly interface or the flexibility of uploading process descriptions, both options are available. You can also integrate custom modules tailored to your specific business needs. With these ready-to-use solutions, your company can achieve measurable AI-driven results within a month of deployment and KPI setting.

Pre-configured AI pipelines for business units on ValueXI

AI for HR

LLM-powered chatbots for improved conversion

LLM models such as ChatGPT and LLaMA are effectively used in chatbots. ValueXI users can configure these models through a chat interface and adjust various parameters, including data generation with augmented retrieval (RAG search). With ValueXI, users can integrate AI into chatbots, manage configurations and access permissions, review conversation history, and enhance automation in candidate interactions.

Business case: X3 efficiency in talent acquisition

Client: A large retail chain.

Challenge: Develop an AI-powered tool designed to increase HR conversion rates by automating routine tasks.

Matching job postings with candidate resumes.
Summarizing video interviews in text format.
Implementing an LLM-powered messenger for hiring.

Solution and results: By increasing conversion rates with ML models, we managed to:

Reduce HR workload for initial screening by 50%.
Cut costs for accessing candidate contacts threefold.
Shorten time-to-fill vacancies by 50%.
Improve both candidate quality and onboarding efficiency by 50%, leading to higher employee satisfaction.

Evaluating employee performance with LLMs

Business case: X2 efficiency in processing support requests

Client: A leading home electronics manufacturer from the Forbes list.

Challenge: Develop an AI tool to analyze technical support specialists’ conversations with customers.

Solution:

Anonymizing data in ValueXI to ensure confidentiality.
Extracting key information from conversations using ChatGPT
Managing the model through prompt engineering to eliminate fabricated data.
Labeling and verifying data within ValueXI.
Analyzing the tone of conversations to assess customer sentiment.
Visualizing results in clear, easy-to-understand graphs.

Results: The conversation analysis tool helped:

Identify the most common questions and add them to the FAQ.
Increase the Customer Satisfaction Score (CSAT) by 34% in the first month of use.
Evaluate the effectiveness of technical support staff.

CASE

AI for sales and marketing

Lead scoring

Business case: 17% increase in airline ticket sales

Client: Wholesalesflight.com — a business-class travel agency.

Challenge: Improve conversion rates without additional ad spend or staff training.

AI solution:

Predicts lead conversion and profitability based on initial data.
Enriches lead profiles with insights such as bonus mile usage and purchase readiness.
Automatically assignes high-potential leads to top-performing sales managers.
Tracks traffic and correlating it with sales performance.
Calculates the profitability of advertising campaigns.
Generates recommendations for cold calls to past customers based on purchase history.

Results:

Reduced lead processing time by a factor of 3.
Increased ticket sales by 17%.

Identifying high-potential leads for cold calls

Business case: Leasing demand prediction to boost sales

Client: Major leasing company.

Challenge: Increase sales by engaging with high-potential leads at the right time — before competitors reach them or they convert through paid channels. Additionally, predict the type of leasing asset each lead is likely to need.

Results:

Processed over 400,000 accounts monthly
AI recommended 14,200 contacts for outreach
3,240 contacts identified as the most relevant target audience
1 in 6 contacts became a client
1 in 2 contacts used leasing services

Business impact:

More precise targeting, reducing unnecessary outreach
2x increase in call center efficiency
Lower customer acquisition costs
Improved customer retention rate
Sales growth

CASE

AI for tech and customer support

Automating service request processing

Business case: AI-automated service requests precessing

Client: Engineering company

Task: Optimize and automate the processing of incoming requests by:

Automatically determining whether a request falls under warranty or non-warranty repair
Tracking the accuracy of repair category predictions
Reducing operational costs caused by incorrect request classification
Speeding up request processing to minimize client wait times for repair teams
Optimizing specialists' workload management

Solution: A text-mining-based module that:

Processes 3,000 requests per day
Analyzes request text and predicts the repair type with 80–98% accuracy
Assigns requests to the correct category and subcategory
Corrects errors in document entries

Results:

Improved service center workload.
50% reduction in human error risk and associated reprocessing costs.
Lower financial losses from misclassified warranty repairs, penalties, and reputational damage.

AI for finance

AI-powered billing

Business case: AI-predicted payments and reduced churn

Client: A developer of accounting and enterprise management solutions.

Challenge: Evaluate the possibility of using a machine learning-based solution for:

Predicting payment timelines for service invoices for each specific client.
Assessing potential customer churn.

Solution:A machine learning model that helps:

Reduce management workload and costs.
Identify at-risk customers and take proactive measures to retain them.
Pinpoint the factors most influencing target scenarios.

Results:

Achieved 75-80% accuracy in payment forecasts.
Predicted churn likelihood with 70-80% accuracy.
Reduced operational workload by 32% and costs by 25%.

CASE

AI for documentation

Analyzing documentation for industrial equipment

To improve the accuracy of responses to specific queries in LLM-powered chats, ValueXI users can activate the Retrieval-Augmented Generation (RAG) feature and upload their corporate knowledge base. This enables LLM models to provide more relevant and precise answers to queries related to specialized topics. Learn more about the RAG search solution in ValueXI here.

Business case: RAG-powered AI assistant for technical documentation

Client: A platform for purchasing industrial equipment.

Challenge: Enable RAG-powered search within technical manuals and documentation for the support team via a chatbot interface.

Results:

Optimized the support team’s workflow.
Enabled faster identification of equipment errors.
Reduced time spent searching for relevant information.
Achieved a 30% improvement in handling technical manuals.

Document digitization with OCR

For working with documents, ValueXI offers an OCR module that allows you to upload scanned images of text documents and automatically convert them into docx format for further processing.

Business case: Reducing staff workload
and minimizing errors with AI

Client: OMTO — mobile and web applications developer for patient transportation.

Challenge:

Reduce patient transportation time.
Minimize manual labor for medical staff.

Solution: Using ValueXI, the application was enhanced with the ability to quickly retrieve patient transportation data by scanning their forms and automatically sending insurance approval requests.

Results:

Reduced staff workload by 65%.
Decreased error rates by 87%.

CASE

How to get started?

Curious how ValueXI’s AI modules can solve your business challenges and deliver measurable results? Join us for a demo session to explore our pipelines and discover the wide range of functionalities available for tackling unique tasks. Just fill the form below or DM us [email protected]

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