The State of RAG in 2025

RAG Comes of Age

In 2025, retrieval augmented generation (RAG) has matured from a promising generative AI methodology into the de facto industry standard for enterprise GenAI applications. At its core, RAG bridges the gap between large language models (LLMs) and the ever-expanding corpus of organizational knowledge by retrieving verified, contextually relevant data at the moment of generation to ensure that AI outputs are both informed and trustworthy.

Unlike traditional generative AI, which generates answers based on static training data, retrieval augmented generation grounds responses in real-time, curated information, enabling enterprises to build systems that are not only intelligent but also compliant, secure, and scalable. In a landscape where outdated knowledge or misinformation can carry significant operational and legal risks, RAG provides the confidence layer that businesses need. 

Inside the RAG Search Process Flow

At its core, the RAG flow begins when a user enters a prompt. Based on the prompt, the system queries an enterprise’s knowledge bases, retrieves the most relevant information, and feeds it alongside the prompt into a large language model (LLM), which uses the enhanced prompt to generate an accurate output. 

Several preparatory steps are required to ensure that the knowledge base is rich, relevant, and ready. 

• Data Ingestion brings in structured and unstructured information from across the organization – documents, emails, databases, and more.
• Data Preprocessing cleans and normalizes the data, removing duplicates and handling inconsistencies to ensure integrity.
• Chunking breaks large documents into smaller, manageable pieces, enabling the system to search and retrieve only the most relevant portions rather than full documents.
• Vectorization transforms each chunk into a numerical representation using embeddings, making it possible to accurately compare the semantic meaning of content.
• Indexing organizes these vectors into a searchable structure, optimizing for speed and precision when matching user queries.
• Metadata Enrichment adds crucial context – such as author, source, date, or other forms of data classification, further refining the retrieval process.
• Quality Assurance ensures every step functions properly, confirming that the right data is being retrieved and used effectively.

Each of these upstream processes is critical in determining the performance of a RAG-powered application. From reducing noise to increasing precision, they collectively ensure that enterprise GenAI delivers reliable, traceable, and contextually grounded answers.

While each step may be simple in theory, scaling them to full-scale enterprise deployments requires deep expertise. 

What’s New in RAG: Key Wins Driving Performance

While RAG excels at surfacing relevant information quickly and reducing hallucinations by anchoring answers to trusted sources, traditional RAG systems still face limitations. Their performance depends heavily on the quality and structure of the retrieved data, the size of the context window the LLM can handle, and the system’s ability to validate generated outputs. 

When workflows require more than just answering static queries, such as reasoning through multistep tasks, complying strictly with regulatory requirements, integrating real-time operational data, or retrieving data with deterministic accuracy, RAG alone often falls short. 

In 2025, advanced RAG systems address these and other limitations with a variety of innovations and architectural considerations. These enhancements push RAG from useful to indispensable – positioning it not just as a search enhancer, but as a core enabler of enterprise AI that can operate safely, contextually, and at scale.

Knowledge Graphs

GraphRAG combines vector search with structured taxonomies and ontologies to bring context and logic into the retrieval process. Using knowledge graphs to interpret relationships between terms has paved the way for deterministic AI accuracy – boosting search precision to as high as 99%. A prerequisite for effective and highly accurate GraphRAG is a carefully curated taxonomy and ontology. 

AI Guardrails

Embedding AI guardrails directly into the generation process elevates the trustworthiness of GenAI outputs to the next level. These may include aligning GenAI to user roles, ensuring policy and legal compliance, brand tone enforcement, and enhancing efficiency and quality. Guardrails enrich prompts with contextual and role-specific information and validate outputs to meet regulatory and organizational standards – ensuring consistent, safe, and on-brand results.

Operational Data Integration

Unlike traditional ETL processes, advanced RAG platforms are now able to connect directly to structured data sources via API. This real-time access allows GenAI to incorporate operational insights from both structured (databases, spreadsheets) and unstructured (emails, chats) data,  enhancing the quality of outputs and enabling faster, more informed decision-making and paving the way for a variety of previously unavailable use cases.

LLM Agnostic Architecture 

The most future-proof RAG systems are LLM-agnostic by design, allowing seamless integration with a variety of large language models. The flexibility provided by LLM-agnostic RAG platforms empowers organizations to select models that best align with their specific needs, security requirements, and cost considerations. By avoiding vendor lock-in, LLM-agnosticity enables businesses to adapt swiftly to the evolving AI landscape, ensuring long-term adaptability and control over their AI strategies.

Flexible Deployment Options

Offering multiple GenAI deployment options, including on-premises, private cloud, and hybrid setups ensures that organizations can maintain data sovereignty, comply with regulatory requirements, and integrate seamlessly with existing systems. By providing tailored deployment strategies, forward-looking GenAI platform vendors support enterprises, including in harshly regulated industries, in achieving optimal performance and security in their AI initiatives.

Massively Scalable Data Access Control Enforcement

Security and compliance are paramount in enterprise settings, making granular, fully scalable data access control mechanisms, including real-time access control lists (ACLs), role-based permissions, and comprehensive audit trails a prerequisite for production deployments. These features ensure that sensitive data is accessible only to authorized personnel, aligning with stringent regulatory standards and safeguarding against unauthorized access.

Cost-Effective Scalability

The entry ticket to real-world enterprise-wide impact is the capability of handling extensive data volumes in the terabytes and user bases in the tens of thousands, while still ensuring accurate, privacy-enabled, secure, and cost-effective low-latency performance. Fortunately, a small number of leading enterprise GenAI platform providers have demonstrated their ability to deliver on the promise of scaling deployments. 

Where RAG is Delivering Value in 2025

Let’s start with where RAG often fails: in endless proof-of-concepts that never scale. Why? Because many pilots overlook what matters to a CSO (privacy), CFO (risk and ROI), CTO (scale), and COO (operations). Ultimately, it all boils down to scalability: To unlock full value, RAG needs to prove itself in enterprise-scale deployments – handling millions of documents, thousands of user roles, and complex entitlements.

At Squirro, we don’t just talk AI – we deliver it at enterprise scale. From 10,000-user deployments and 15 million+ document rollouts to complex privacy and governance requirements in highly regulated environments, we’ve delivered GenAI deployments that have stood the test of time. Building a small-scale RAG may be straightforward. The true test is scaling it securely, cost-effectively, and in full compliance with enterprise-grade data governance — and that’s exactly where Squirro excels.

Enterprise AI Use Cases in Manufacturing

From customer support to internal research, market intelligence, and employee knowledge portals, our RAG-based enterprise GenAI platform is turning structured and unstructured data into operational insight. Sales teams gain faster access to competitive intelligence; service reps get real-time responses; executives get contextual summaries that drive better decisions.

Semantic Enterprise Search for Manufacturing

Henkel, a global innovation leader, partnered with Squirro to transform internal knowledge sharing. By streamlining over 300,000 search results from 45+ data sources, Henkel fostered collaboration, reduced redundancy, and fueled innovation among its technical teams. This strategic partnership enhanced Henkel’s market leadership and set a new standard for sustainable growth.

=> Read our case study on semantic enterprise search for manufacturing

High-Stakes, Regulated Environments in Banking and Financial Services

Financial institutions, law firms, and healthcare providers now trust RAG to augment workflows where accuracy, auditability, and explainability are non-negotiable. With structured oversight and permission-aware access, RAG systems can operate safely even in the most regulated sectors.

Empowering Client Advisors with AI Workflow Automation

A wealth management firm partnered with Squirro to equip client advisors with GenAI Employee Agents. This enabled faster, data-driven decisions, improved regulatory compliance, AI workflow automation, process optimization, and client management, ultimately enhancing client service and relationships.

=> Read our case study on empowering client advisors with AI workflow automation

Saving Millions in OPEX with AI-Driven Ticket Classification

A multinational bank partnered with Squirro to use AI ticketing for faster, more accurate handling of millions of cross-border payment exceptions annually. This significantly reduced manual processing time and costs, saving millions in OPEX and freeing up capital for revenue generation.

=> Read our case study on saving millions in OPEX with AI-driven ticket classification 

Streamlining audit and compliance workflows

A major European bank used the Squirro Insights Engine to automate audit and compliance, saving over EUR 20 million in three years. This automation of risk detection, operational efficiency, and document analysis freed up the time equivalent of 36 full-time employees and achieved ROI in just two months post deployment.

=> Read our case study on streamlining audit and compliance workflows 

From RAG to ROI: Why RAG Matters More Than Ever

Beyond Chat: A Strategic Decision-Making Asset

RAG is no longer just an enhancement for AI chatbots – it’s the strategic backbone of enterprise knowledge management and knowledge access. As AI moves from novelty to necessity, RAG offers a repeatable, scalable way to bring intelligence to the point of work, for example by streamlining investment analysis. 

With real-time, role-aware augmentation, executives, analysts, and frontline teams are empowered to make better decisions, faster. No more digging through SharePoint or outdated PDFs – just the right answer, right now.

• Reduced operational overhead
• Fewer errors
• Enhanced customer service quality
• Shorter search-to-decision cycles.

A Competitive Edge in the Knowledge Economy

With the current wave of retirements in sectors including banking and financial services, decades of expertise in compliance, risk management, client relationships, and complex workflows are walking out the door. Meanwhile, new hires, while capable, often lack the time and structure to learn at the same pace. 

RAG enables organizations to face this generational change by mining their own data assets at scale. It democratizes expertise, reduces silos, and allows any employee to operate like a domain expert, offering a powerful competitive edge in the knowledge economy. 

• Increased productivity
• Faster onboarding
• Reduced dependency on subject matter experts
• Elevated performance and customer experiences

Agentic AI Will Drive Demands on RAG 

As enterprises lean harder into AI-driven transformation in 2025, RAG systems are facing growing demands – not just to retrieve information, but to enable action, accountability, and adaptation at scale. The expectations go well beyond helpful chat responses; they now sit at the core of enterprise-grade intelligence workflows.

AI Agents: From Insight to Execution

The most urgent pressure on RAG today comes from the rise of AI agents – autonomous or semi-autonomous systems designed to perform multistep processes. These agents don’t just answer questions; they plan, execute, and iterate, interfacing with internal systems, making decisions, and escalating when necessary.

But here’s the catch: these agents only work if they’re grounded in deterministic, accurate knowledge and operate within clearly defined guardrails.

While RAG has made significant strides, it has notable limitations, especially as enterprises move toward more complex AI-driven systems. RAG alone can retrieve relevant information but doesn’t evaluate whether that information is procedurally correct, nor does it have a memory of previous steps in multistep processes.

Meeting the Demands of AI Agents

To meet the growing demands of AI agents and enterprise automation, enhanced RAG incorporates process graphs that break down workflows into actionable steps, knowledge graphs to offer reliable responses with deterministic accuracy, and guardrails to ensure compliance with legal, regulatory, and brand standards. 

These enhancements allow RAG to not only retrieve information but also drive intelligent, reliable decision-making at scale.

Conclusion: The Road Ahead for RAG

In 2025, RAG isn’t just a trend – it’s a cornerstone of enterprise AI architecture. It’s what enables companies to harness their data assets responsibly, surface insights in real time, and empower every employee with the context they need to excel.

As enterprises navigate an increasingly data-saturated, risk-sensitive world, RAG offers a path to clarity, compliance, and competitive edge.

Those who adopt early – moving beyond pilots and build structured, scalable RAG architectures – will be the ones who lead in the AI-driven decade ahead.

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