How to Use Retrieval Augmented Generation in Your Business

Generative AI has radically transformed expectations for business innovation. However the large language models (LLMs) that form the foundation of GenAI have limitations: inaccurate outputs, unverifiable responses, outdated data and, in some cases, hallucinations. For CFOs and decision makers, this represents a real operational risk, especially in regulated or process-critical industries.

That’s where Retrieval Augmented Generation (RAG) comes in. An emerging architecture in the field of AI, RAG integrates LLMs with external knowledge sources that are structured, updated and controllable, making AI responses more accurate, contextual and traceable. Because of this, RAG is quickly becoming a key technology for CFOs, CIOs and leaders who want to integrate AI into critical processes with reliability and governance guarantees.

What is Retrieval Augmented Generation?

RAG is an AI architecture that combines an LLM with an information retrieval system, enabling the generation of answers based on up-to-date, authoritative and contextual data sources. This combination provides a higher level of accuracy, relevance and control, which are essential for using AI in enterprise contexts.

RAG operates on the following architecture:

• Embedding model: Business documents, databases or APIs are converted into vector numerical representations stored in a vector database.
• Information retrieval: When a query is posed, the system performs a semantic search in the vector database to retrieve the most relevant information.
• Augmented generation: The LLM uses the information retrieved to generate a contextualized, transparent response based on verifiable sources.

This combination of retrieval and generation makes RAG particularly powerful in environments where data changes frequently or where compliance is critical, such as finance, legal, customer service and IT.

RAG reduces reliance on generalist models, increases the relevance of responses and improves AI governance. It represents the transition from generic AI to contextual AI, fueled by specific and constantly updated company knowledge.

As a result, RAG is growing in popularity around the world. According to Straits Research, the global market for intelligent document processing (IDP) — a key component of RAG-based solutions — has grown from $2.44 billion in 2024 to a projected $37.28 billion by 2033, with a compound annual growth rate of 35.4%.

The role of the vector database & data sources in RAG

RAG’s effectiveness is based on a fundamental technological component: the vector database. This type of database is designed to manage and query numerical representations of documents, enabling high-performance semantic search. Unlike traditional keyword-based searches, generating output using vector databases allows for understanding the meaning and intent of the query, significantly increasing the accuracy of the answers.

Data sources play an equally crucial role. Whether they’re corporate knowledge bases, financial documents, regulatory archives or data from ERP and CRM systems, the quality and timeliness of these sources determine the value of the RAG system. An effective embedding model transforms these sources into a library that can be queried in real time by LLMs, ensuring consistently relevant and contextually aligned responses.

The RAG approach is therefore only as powerful as its information ecosystem is reliable. Companies wishing to implement an effective RAG model must ensure that their knowledge bases are:

• Accurate and clean
• Structured and easily indexable
• Subject to continuous updating (even asynchronously)

RAG and artificial intelligence: a strategic alliance

In the context of enterprise AI, RAG isn’t just a technical advancement, but a concrete response to the most critical challenges that traditional generative models cannot address. RAG creates high quality output even in situations with large volumes of dynamic data, distributed sources and highly regulated environments.

From an operational perspective, RAG offers several advantages over an LLM used in isolation:

• Reduces the hallucinations typical of LLMs because it’s connected to reliable knowledge bases
• Improves transparency by allowing you to track the information sources behind responses
• Enables dynamic personalization, based on proprietary company content
• Integrates easily with existing architectures without having to rewrite or retrain models

RAG is now one of the key technologies for modernizing corporate IT architectures, because it not only improves the quality of responses, but also builds a bridge between AI and real-world decision-making processes.

How can RAG transform business? The enterprise perspective

For many business leaders, AI represents resilience, innovation and competitive advantage. RAG in particular provides:

• Operational accuracy: reduces errors thanks to answers based on verified sources
• Content governance: enables companies to define, curate and control the information universe from which AI draws
• Strategic responsiveness: supports real-time decisions based on up-to-date, contextual data
• Cross-functional efficiency: streamlines repetitive tasks in critical areas such as finance, procurement and customer service

A global Gartner survey found that 62% of CFOs and 58% of CEOs believe AI will have the most significant impact on their industries in the next three years. This confirms that adopting models like RAG is no longer an experimental choice, but a strategic priority.

Even under economic pressure, AI remains at the top of the agenda. Another Gartner study from July 2025 reports that 33% of companies are recalibrating their spending, cutting back in some areas to invest in key technologies like AI.

As Alexander Bant, Chief of Research at Gartner, observed: "Companies know it's time to make the right changes to their cost structure to win the intensifying AI race."

How to integrate RAG in your business

Adopting RAG requires a strategic approach that takes into account your existing information structure, business objectives and technology governance. It's not just about "adding AI," but about integrating corporate knowledge into intelligent systems that support decision-making processes in a consistent, secure and verifiable manner.

This is where the difference between RAG and LLM fine tuning becomes clear. While fine tuning an LLM involves costly customization of models through retraining, RAG allows an LLM to be connected to external data sources (structured or semi-structured) at run-time.

Esker’s Synergy AI platform is an example of an AI solution where RAG is already applied to operational language models, enhancing them with customer-specific content (invoices, contracts, ERP data, email correspondence). The result is an AI system that’s:

• Scalable: easily adapts to new models or information sources
• Agile: reduces time to value and accelerates implementation
• Controllable: maintains high traceability and human oversight

This integration isn't just theoretical. For example, in account management, RAG allows you to generate intelligent responses to customer requests, suggest actions to credit analysts and improve automatic dispute categorization. In customer service, it optimizes customer inquiry management, ensuring contextual, rapid and personalized responses.

The synergy between RAG, LLM models and corporate information sources is the key to truly intelligent automation, capable of evolving over time and adapting to business dynamics.

How Esker’s automation tools use RAG

RAG in the Esker Synergy AI platform is radically transforming operational efficiency and the quality of interaction between systems, people and data. RAG in Esker’s Order-to-Cash (O2C) and Source-to-Pay (S2P) solutions is already a reality in complex, multi-ERP environments.

Customer inquiry management

One of the most obvious use cases for RAG is customer inquiry management. Traditionally, shared inboxes and disorganized workflows led to delays, errors and a negative impact on the customer experience. By combining natural language processing, sentiment analysis and RAG, Esker can:

• Automatically classify customer requests by type
• Identify and prioritize the most urgent messages
• Generate AI-assisted responses based on approved and up-to-date content
• Dynamically integrate data from ERP, CRM and logistics systems

The result is a drastic reduction in response times and significantly increased customer satisfaction, all while maintaining human oversight to ensure control and customization.

Accounts receivable and cashflow management

In the accounts receivable cycle, RAG means Esker’s Synergy AI not only reads and interprets documents but also suggests actions in real time — from reconciling bank balances to generating responses to disputes. Thanks to its connection with LLMs, Esker’s RAG can:

• Predict customer payment behaviors
• Speed up invoice and payment matching by up to 95%
• Provide strategic insights for the Finance team
• Automate remittance allocation

All this with a 0.5% error rate, a true revolution for working capital management.

Intelligent document processing (IDP)

Finally, applying RAG in IDP workflows allows unstructured documents to be transformed into data ready for processing. With RAG, every decision made by AI is supported by traceable and consistent sources, significantly improving:

• The quality of data extraction from orders, invoices and contracts
• Automating approvals without human intervention
• Multi-ERP integration in distributed environments
• Business continuity even in complex or multilingual contexts

This not only reduces costs and time, but also increases internal user trust and improves compliance, enabling robust and auditable AI governance.

Esker & RAG: AI that creates real value for businesses & people

In today's landscape, adopting RAG-enhanced solutions is no longer a futuristic option, but a real path forward for companies seeking to make AI a true ally. Esker uses RAG to optimize the output of its language models, integrating them with up-to-date, authoritative and contextual data for business processes.

Combining RAG with intelligent process automation and IDP, Esker can automate time- and resource-intensive tasks such as data entry, customer inquiry management, bank reconciliation and email classification. The result is not only greater operational efficiency, but a direct impact on key KPIs by:

• Reducing response and cycle times
• Enhancing precision and control
• Improving cashflow
• Simplifying compliance

But the true value goes beyond that. Offering your teams advanced, intuitive tools that leverage human capital means attracting and retaining talent, freeing up time for strategic activities and making innovation a daily driver. AI designed in this way doesn't replace people, it enhances them.

Choosing Esker means choosing a platform that combines technology, experience and real impact.

Discover how Esker's integrated RAG can transform your financial processes. Let's talk.