The RAG Playbook: Structuring Scalable Knowledge Bases for Reliable AI Agents

Written by:

September 2, 2025

•

5 min read

The knowledge base for your AI agent isn’t just a dusty FAQ archive – it’s the brain fuel that powers accurate, on-point conversations. Building a knowledge base (KB) for a human support team is one thing, but doing it for an LLM-powered AI agent is a different game entirely. What works for humans– long articles with anecdotes, context, and even a dash of marketing fluff–can utterly confuse an AI agent.

The result? Sometimes it’s harmless, like the time a customer asked for your cancellation policy and the bot earnestly replied with your company’s mission statement, three paragraphs about how you value innovation, and a cheerful “Have a great day!” Other times, it’s not so harmless. Think confident but incorrect therapeutic advice or an unauthorized “discount” it just invented on the spot; not exactly what you want from your brand’s digital representative.

In this guide, you’ll see exactly how to format and structure a knowledge base so your AI Agent stays accurate, relevant, and on-script. We’ll break down why traditional human-centric KBs fall short, and show how smart formatting can reduce hallucinations, improve accuracy, and keep your AI Agent’s behavior on the rails.

We’ll cover best practices like single-topic chunking, concrete instructions, explicit outcomes, and explain what to put in the prompt vs. the KB. And don’t worry if “chunking” or other terms sound like AI-speak now. By the time you finish this guide, you’ll know exactly what they mean and how to put them to work, without needing to wade into the full Retrieval-Augmented Generation (RAG) playbook. Let’s dive in!

Why Human-Centric Knowledge Bases Fall Short for AI Agents

A knowledge base written for human consumption often contains knowledge clutter that doesn't bother a human reader, but can wreak havoc on an AI agent’s responses. Before we get to solutions, let’s identify the biggest content culprits:

Irrelevant Context and Fluff: Human-facing articles often start with a narrative or extra background – “At Acme Co., we value our customers…” – before the useful info. An AI agent retrieving this may latch onto the fluff and respond with irrelevant platitudes. Unnecessary context dilutes the useful facts. The AI might even mistake a marketing blurb for an answer and go off on a tangent. Case: From Compliance Guide to Chaos Gremlin. NYC’s small-business chatbot told entrepreneurs it was fine to do illegal things (like taking workers’ tips), then doubled down with outdated wage info. It’s the content-equivalent of quoting a press release when the user needed the statute.
Assumed Prior Knowledge: Many documents assume the reader has some context. For example, an internal guide might say “follow the usual refund process” without detailing it. A human agent knows the “usual process,” but an AI agent has no memory of knowledge outside what’s in the retrieved text. Missing context forces the AI to guess, which is a recipe for hallucination. If the KB chunk doesn’t spell something out, the model may fill the gap with its own invention, as in the case of Air Canada’s website chatbot. The AI confidently invented a bereavement-refund process that didn’t exist, so convincing that the tribunal made the airline compensate the passenger.
Conflicting or Redundant Instructions: Large knowledge bases often accumulate overlapping content. You might have two docs with slightly different refund policies, or an old procedure lurking alongside a new one. Human agents can figure out which one is current; an AI might randomly pick or (worse) mash them together. This leads to unpredictable, inconsistent answers. Having too much redundant or conflicting info can confuse the AI and make its behavior less predictable. In one case, an AI was given two sources on whether a fee is required – it sometimes said “no fee” and other times “a fee applies,” purely based on which chunk vector search was fetched first.
Ambiguous Language: Human writers might use polite or vague phrasing like “you may want to consider doing X” or “Try Y if possible”. This subtlety can baffle an AI agent that craves clear directives. Ambiguity in knowledge content can increase the chance of the AI choosing an incorrect interpretation. For instance, if a policy note says “late cancellations might incur a charge”, the AI might just omit the fee entirely. Without concrete wording, the model’s guesses can go astray.

The Impact: Hallucinations and Off-Script Behavior

When a knowledge base has the issues above, an AI Agent’s accuracy and reliability plummet. The model might confidently fabricate answers or instructions that were never in your company policy, simply because it thinks that’s what you implied. And despite common prejudices, these hallucinations stem not from the model “being goofy,” but from bad or poorly retrieved data – essentially, the AI is being “gaslit” by a messy knowledge base. The good news: by reformatting how knowledge is structured, we can ground the AI Agent in the right context and cut down on hallucinations dramatically.

Best Practices for Structuring an AI-Ready Knowledge Base

Designing your knowledge base for AI agents requires a more disciplined, machine-friendly approach to content. Here are the top best practices to adopt:

Single-Topic Chunking: Break your knowledge into bite-sized pieces, each focused on a single topic or question (a single-topic chunk). Avoid giant all-in-one documents. Smaller, well-scoped chunks ensure that when a retrieval occurs, the AI gets just the specific information it needs – nothing more, nothing less. As a rule of thumb, think a few paragraphs (or even a few sentences) per chunk. Regal’s platform, for example, automatically uses semantic chunking to split sources. This granular approach keeps retrieval precise and prevents irrelevant text from piggybacking along.
Concrete Instructions (No Filler): Write the content like you’re instructing a particularly literal intern – clear and direct. Use imperative language for procedural info specific to the AI Agent (e.g. “Call custom action “Submit” to save changes” instead of “You could then click submit”). Remove the “fluff” that you might include for a human’s reading pleasure. The AI doesn’t need motivational quotes or company history in the middle of a how-to. By sticking to concrete steps or facts, you reduce ambiguity. The goal is that any sentence the AI might quote is immediately useful and actionable. If the agent needs to explain something to a user, give it the exact phrasing or numbers it should use. For example, instead of a blurb that says “our plans are affordable”, give a factual statement: “Plan A costs $30 per month, Plan B costs $50 per month.”
Explicit Outcome Statements: Wherever applicable, state the desired outcome or resolution in the knowledge chunk. This helps the AI understand the point of that information. For instance, after listing steps for a cancellation process, add a line like “Outcome: Appointment is canceled and a confirmation email is sent to the customer.” This explicitly tells the model what the end state should be. Outcome statements function as a sanity check – they remind the AI “this is what you’re trying to accomplish.” If the user’s question is resolved by that chunk, an outcome statement ensures the agent’s answer includes confirmation of that resolution. It also helps prevent the agent from stopping short or going on an unrelated tangent. Essentially, you’re baking a mini-summary of “why does this info matter?” into the content.
Contextual Reminders and Applicability: Give context about when or to whom a chunk applies, especially if your knowledge base covers nuanced scenarios. For example, if a policy differs by region or customer type, don’t make the AI infer that – state it outright. A chunk title or first line might be “Return Policy (California customers)” or “Procedure: Password Reset for Mobile App (Android)”. This way, the retrieval system can grab the chunk that matches the user’s context, and the AI Agent will see in the content itself who/what it’s for. Contextual labels reduce the chance of the agent using the right answer in the wrong situation. In Regal’s RAG system, AI agents can pull region-specific data when appropriate – e.g., if a contact is in California, the agent fetches the California-specific info first, keeping the conversation compliant and on-target. Our customers achieve this by structuring their KB into clearly scoped chunks (by product, region, scenario, etc.) and adding one-line intros like “Use this info when <these_conditions> are true.”
Reduced Ambiguity: Write in a consistent style and use the same terminology that users and the AI Agent will use. If your product is called “AcmePhone Pro” in one doc and “AcP Pro” in another, choose one and stick with it (or at least mention the alias in the same chunk). Avoid idioms or internal slang that the model might not interpret correctly. If a process must never be done a certain way, say “Do not XYZ” rather than “XYZ is not recommended” (which the model may soften or ignore). The key is to leave no room for the AI to wonder what you mean. One trick: read the content as if you have no other context – would you 100% understand what to do or say? If not, clarify it. This also means removing any conflicting guidelines: ensure each piece of knowledge gives a single, unambiguous source of truth on that topic. If two chunks even appear to disagree (e.g. one says “usually do A” and another says “in some cases do B”), add context or combine them into one explicit decision-tree chunk. Your AI Agent can’t do critical thinking on clashing instructions – it will just pick one side. Cleaning up these ambiguity and consistency issues will greatly improve the agent’s accuracy.

‍

By following these practices, you transform your knowledge base from a messy wiki into a lean, mean answering machine. A well-structured KB means the RAG system can grab the right facts quickly, and the LLM can trust what it sees. It’s the difference between an agent floundering through irrelevant paragraphs, versus confidently citing a single crystal-clear paragraph that directly answers the question. As a bonus, a slimmed-down, relevant knowledge chunk keeps the token count low and speeds up responses – no more prompt bloating with unnecessary text. In other words, you get faster, more accurate answers grounded in your authoritative content.

Prompt vs. Knowledge Base – Knowing What Goes Where

One common question we get is how to split duties between the AI agent’s prompt and the knowledge base. Both are essential, but they serve different purposes for your AI agent:

The Prompt is the script and personality. Use it for behavior, tone, decision rules, and short, always-needed info (like disclaimers, FAQs, or guardrails). Think of it as the “how to act” layer.
The Knowledge Base is the agent’s memory. Use it for detailed, dynamic, or frequently updated content (like policies, product manuals, or regional variations). Think of it as the “what to say” layer.

‍

The general rule of thumb is: keep prompts lean (stable rules and flow), and put the rest in a structured KB so the agent can fetch facts on demand. This avoids prompt bloat while keeping responses accurate and scalable.

For a deeper dive on context engineering and when to use prompts, KBs, or custom actions, check out our full guide.

Knowledge Bases That Make Your AI Agent (and You) Look Smart

A well-structured knowledge base is the secret sauce behind highly effective AI agents. By eliminating irrelevant context, breaking knowledge into focused chunks, and writing instructions that are unambiguous and outcome-oriented, you ground your AI in facts and context, dramatically reducing the odds of it veering off into nonsense or error. In turn, your customers get faster, more accurate answers. Your AI Agent isn’t playing detective to figure out what the policy really means; it’s confidently quoting the correct, up-to-date information you gave it.

To recap, when building a knowledge base for LLM-powered agents, remember:

Format is king – Structure your content for retrieve-ability. Clear headings, short paragraphs, and markdown bullet points (if applicable) all help the vector search do its job and help the AI parse the info quickly.
Keep prompt and knowledge in their lanes – Use the prompt for your agent’s personality and always-needed logic, and the KB for the details that can be fetched on demand. This keeps your AI’s “mind” uncluttered and focused.
Continuously curate – An AI knowledge base isn’t “set and forget.” Monitor your agent’s outputs; if you catch a weird answer, trace it back to the source chunk. You might find an opportunity to edit that chunk for clarity or split it further. Remove outdated info promptly to avoid misinformation. Essentially, tend to your AI knowledge base like a garden – prune the bad, nurture the good.
Test in realistic scenarios – Use Regal’s agent testing tools to simulate queries and see what it pulls from the KB. This will show you whether it’s grabbing the right chunks and phrasing answers correctly. If not, tweak the content or add more guidance (either in the KB description or the prompt) until the AI consistently does the right thing.

‍

By investing effort upfront in building a clean, AI-optimized knowledge base, you’re setting your virtual agent up for success. You’ll get predictable, reliable agent behavior, faster and more accurate answers, and conversations that stay on-message and on-brand – all at scale, without having to stuff an ever-growing list of facts into your prompt. It’s a win-win: the AI Agent is happier (less confused), and your customers are happier (more satisfied with the help they receive).

Finally, remember that knowledge bases and AI agents are a partnership. The smartest AI won’t shine if it’s fed garbage info – garbage in, garbage out. But with a well-built knowledge base, even a relatively small LLM can outperform a larger one that’s unguided, because it’s grounded in the truth of your data. You’re the expert on your business; by translating that expertise into a structured knowledge base, you make your AI agent an expert too.

Play around with these tips and watch your AI agent’s IQ (and CSAT scores) soar. And if you need a little help crafting the perfect AI knowledge base, schedule a demo with us!

Latest Blog Posts

SEPTEMBER 2023 RELEASES

September 2023 Releases

SEPTEMBER 2023 RELEASES

September 2023 Releases

SEPTEMBER 2023 RELEASES

September 2023 Releases

Announcing Regal + ElevenLabs: Dynamic Voices for Every Conversation

Regal and ElevenLabs bring expressive, multilingual AI voices to customer and sales conversations, adapting tone and emotion in real time to boost conversions, trust, and satisfaction at scale.

Introducing Simulations: End Manual Bottlenecks in AI Agent Testing

Discover how Regal’s AI Simulations eliminate manual QA bottlenecks for enterprise AI agents. Auto-generate test cases, run bulk end-to-end conversations in seconds, and validate performance at scale before any live calls are made.

Introducing Real-time Agent Stats for Live Performance Monitoring

Introducing five new real-time AI Agent metrics, giving you real-time visibility into transfers, conversation quality, and customer receptiveness to AI.

Announcing Regal + Rime: Human-Like Voices at Enterprise Scale

Regal is partnering with Rime to deliver ultra-realistic, emotionally nuanced AI voices for enterprise contact centers. Discover new voice options that capture human qualities like pacing, resonance, and subtle expression, and learn how Rime’s technology powers conversations that build trust at scale.

Regal IQ: How We Turned Transcripts Into Strategic Insights

Learn how Regal IQ turns unstructured AI Agent transcripts into actionable business intelligence. Discover patterns, pinpoint performance gaps, and update prompts or knowledge bases to improve customer experience at scale.

Introducing Regal IQ: Conversation Insights for Smarter AI Agents

Regal IQ gives enterprises a scalable way to analyze thousands of AI agent calls, uncover conversation patterns, pinpoint knowledge gaps, and improve AI performance and customer outcomes over time.

Introducing Custom AI Analysis: Extract Data from Every Conversation

Regal’s Custom AI Analysis transforms post-call transcripts into actionable, structured data points, so you can personalize follow-ups, analyze trends in-aggregate, and scale improvements across every interaction.

July 2025 Releases

New features include AI-powered conversation simulations, custom post-call analysis, voicemail configuration options, real-time metrics, and full transcript exports, all designed to improve AI Agent performance and compliance.

Context Engineering for AI Agents: When to Use RAG vs. Prompt

Master context engineering by choosing the right method for AI agent knowledge—prompts for behavioral control, RAG for long-form, unstructured data, and custom actions for precise lookups.

Building AI Voice Agent-Ready APIs: Lessons from the Front Lines

Lessons from the frontlines on how to build AI Voice Agent-ready APIs.

Inside Regal’s H2 Roadmap Reveal: The Future of AI Agents

If you missed our H2 Roadmap Reveal, here are the biggest takeaways, including real customer wins and a preview of what’s coming in the second half of the year.

Announcing Regal + Cartesia: Expanding Voice Options for Enterprise AI

Regal is partnering with Cartesia to deliver ultra-low latency, high-fidelity AI voices for enterprise contact centers. Explore new voice options, hear them in action, and learn about Cartesia's industry-leading voice AI offerings.

How to Perfect Your Voice AI in Regal

Learn how to tune AI voice agents for sales, compliance, and customer experience. Explore Regal’s voice parameters, like speed, tone, temperature, responsiveness, to optimize conversion, clarity, and trust at scale.

The LLM Matchmaking Guide for Enterprise-Grade AI Agents

This guide outlines the capabilities, trade-offs, and real-world applications of every model currently supported in Regal.

Anatomy of an AI Voice: What Makes It Sound Human

This article outlines the core characteristics that influence how voice AI is perceived on live calls. From mechanical traits like speed and volume, to more emotional and conversational behaviors, we’re going to look at what those characteristics mean, why they matter, and how they impact your bottom line.

AI Voice Customization 101: Settings That Work Best

Learn how to configure your AI Voice Agent for real performance. This guide covers the most important voice settings in Regal, what ranges top brands use in production, and how adjusting speed, tone, and responsiveness impact cost, containment, and overall customer experience.

Beyond CCaaS: From Customer Data Dips to Customer Data-Driven

Modernizing your Contact Center to drive personalization and more revenue requires a new tech stack you won't get with legacy CCaaS.

Introducing Knowledge Bases for AI Agents

Regal’s Knowledge Base feature brings Retrieval-Augmented Generation (RAG) to Voice AI Agents, enabling real-time access to proprietary data for smarter, compliant, and brand-aligned conversations at scale

June 2025 Releases

June’s releases give you more control to test, improve, and scale AI agents—while also making life easier for human agent teams. From real-time debugging tools to capacity control and a floating Chrome extension, we’re unlocking faster workflows, better control, and stronger performance across the board.

Introducing Progressive Dial for AI Voice Agents

Discover how you can now staff Regal’s progressive dialer with AI Voice Agents—to run high-volume outbound campaigns with smarter pacing, instant call connection, and voicemail detection—boosting efficiency and eliminating abandoned calls.

RAG: The Reason Regal’s AI Agents Are So Smart

Learn how RAG transforms AI Agent performance by retrieving real-time customer and contact data—ensuring every Voice AI Agent response is unique to your business, contextual, and compliant.

Caller ID vs. CNAM vs. Branded Caller ID: What’s the Difference?

Caller ID, CNAM, and Branded Caller ID all help identify an incoming call and, therefore, increase call answer rates. Find out how they differ and how outbound phone sales teams can utilize each to reach prospects and customers effectively.

How AI Appointment Setter Technology is Redefining CX at Scale

Discover how AI appointment setter technology is being adopted by enterprises in industries like healthcare, insurance, and education as a strategic advantage for scaling operations and improving customer satisfaction.

How to Use SIP Headers in Regal to Route and Personalize AI Voice Calls

Learn how to use SIP headers with Regal AI Voice Agents to personalize routing, enrich transfers, and integrate with your existing telephony stack—no backend changes required.

Build AI Agents Without Code, Directly From Regal’s Platform

Discover how Regal's AI Agent Builder lets enterprises create and deploy custom Voice AI Agents without code, integrate with existing systems, and scale workflows to deliver human-like customer interactions at scale.

How to Use SIP to Integrate Regal Voice AI Agents with your Contact Center Software

Learn how SIP integration enables enterprises to connect Regal Voice AI Agents to existing CCaaS platforms, pass call context in real time, and deploy voice AI without infrastructure changes.

May 2025 Releases

May’s releases unlock more conversations with AI Agents through smarter dialing, automatic callback scheduling, and native calendar booking to boost connect rates and accelerate follow-ups.

What is the true cost of AI Voice Agents?

Wondering about the true cost of AI Agents? Discover how Regal’s AI Agents compare to human labor and why the cost of implementing AI Agents delivers scalable, predictable ROI.

What Makes Regal AI Agents So Good?

See why leading companies trust Regal’s AI Agents for better conversations, real outcomes, and HIPAA-compliant customer experiences.

Debunking AI Agent Fears: "What if my agent says the wrong thing?"

Worried your AI Agent will say the wrong thing? Well, they might. But find out why that's just a natural part of the process, and how to mitigate the risk of it happening.

Measuring Customer Experience: Proven Strategies to Assess and Enhance CX

Learn how you can start measuring customer experience effectively with key metrics, tools, and AI-driven strategies. Discover how to track CX impact, prove ROI, and enhance personalization to drive business growth.

Regal Raises $40M to Bring AI Phone Agents to Enterprise Brands

Regal just raised $40M to accelerate our mission of building the new standard in high-touch customer communication with the rollout of our exceptional AI Phone Agents for contact centers.

The Automated Phone System Revolution: Why 73% of Enterprises Are Getting It Wrong (And How to Get It Right)

Explore how automated phone systems are transforming enterprise communication strategies. Learn how to avoid common pitfalls, optimize customer journeys, and achieve ROI through strategic deployment across industries like healthcare, insurance, and education.

How to Best Combine Voice and SMS AI for Omnichannel Support

The best customer experiences are seamlessly omnichannel. In this guide, see how Regal enables seamless, AI powered omnichannel support across voice and SMS.

Staying Compliant with the New TCPA Rules: A Guide for Enterprise Contact Centers

New TCPA updates require interpreting opt-out intent and suppressing outreach across all channels. See how you can use Regal's AI Decision Node to stay compliant.

Debunking AI Agent Fears: "Will humans get frustrated talking to AI?”

Learn how to design AI phone agents that prevent frustration, earn trust, and actually help customers—by getting the voice, logic, and data right from the start.

Debunking AI Agent Fears: "Will the AI lack empathy?"

With the right design and controls, AI agents can be built to deliver empathetic, human-like interactions for all of your routine contact center interactions.

Debunking AI Agent Fears: “What if the AI crashes mid-conversation?”

You're not crazy for worrying about AI crashing out of the blue. Here, see why you shouldn't concern yourself over that happening.

Debunking AI Agent Fears: "What if my AI Agent takes too long to respond?"

Worrying that an AI agent will take too long to respond is not a valid reason not to adopt AI. Here, we'll show you why.

AI Agents vs. Answering Services: 13 Essential Questions Answered by Contact Center Experts

Discover how AI Agents vs. Answering Services stack up and why modern businesses are replacing outdated systems with emotionally intelligent, revenue-driving AI voice agents.

April 2025 Releases

Here's our April 2025 product releases, including the early access period for our AI SMS Agents!

How to Build AI Agents for Beginners: A Step-by-Step Guide

Learn how to build AI agents for beginners with this step-by-step guide. Discover key skills, tools, and no-code AI agent builders to get started today!

How are Generative AI Voice Agents Different from AI Voice Assistants?

When it comes to comparing AI Agents vs. AI Assistants, Siri & Alexa handle simple tasks, but Gen AI Voice Agents—like Regal’s AI Phone Agent—drive real business impact with human-like conversations, automation, and seamless integration.

How to Choose a Text-to-Speech Provider for AI Voice Agents

Choosing a text-to-speech provider for AI voice agents can make or break your contact center’s customer experience. Discover how to evaluate TTS providers and find the best fit for industries like healthcare, insurance, and more.

Measuring AI Agent Success: Key KPIs for AI Agents in Your Contact Center

Discover key KPIs for measuring AI agent success in your contact center. Learn how to track performance, improve efficiency, and optimize AI-driven conversations for better business outcomes.

What is an AI Voice Agent for CX?

In this article, we’ll answer the question "What is an AI Voice Agent for CX?" and explore the technology behind AI voice agents, their benefits, real-world use cases, and how they are reshaping customer service across industries.

Introducing the AI Decision Node: Smarter AI Workflow Automation for Contact Routing

Introducing Regal's AI Decision Node—a new way to auto-route contacts in journeys based on the context of each customer interaction.

5 Customer Experience Journey Mapping Templates & Examples for 2025

Discover how customer experience journey mapping helps contact center leaders make small tweaks with big impact. Learn dynamic mapping tactics for personalization, optimization, and ROI.

7 Best Use Cases for AI Voice Agents in Your Contact Center

As AI technology continues to evolve, the use cases for AI Voice Agents in contact centers will only increase. By answering these six key questions, you can identify where AI agents fit best today in your contact center and plan for future integrations.

8 AI Agent Use Cases for Home Service Companies

Explore 8 powerful AI Agent use cases for home service companies that drive speed, increase capacity, and create predictable, high-converting customer workflows.

AI Collections: How Top Lenders Automate Growth in 2025

Automate follow-ups, reduce delinquencies, and boost ROI with AI Collections. Discover how Regal’s AI Agents are changing the future of loan servicing.

AI Agents Make AEP Easy for your Medicare Call Center

See how AI Agents are transforming the Medicare call center by automating AEP, screening, scheduling, and onboarding—with 24/7 support.

AI-Based Workflow Automation: How to Personalize and Scale Customer Journeys

Discover how AI-based workflow automation and customer journey automation can streamline operations, personalize customer interactions, and boost revenue.

How to Deliver a Unified Customer Experience with Regal in 2025

The more lines of communication you open with your customers, the more likely you’re starting the conversation on the right foot. Regal helps you unlock a more unified customer experience in a matter of days. See how.

Maximize Agent Throughput with Regal's Predictive Dialer

It’s critical for call center managers to understand how their power dialers work and to measure if they’re performing as intended. With Regal’s new Predictive Dialer, You can do just that, and much more.

8 Healthcare AI Agent Use Cases for Better Patient Outcomes

Discover how healthcare AI Agents are transforming patient engagement from intake to billing. See 8 powerful use cases driving higher adherence, faster scheduling, and better outcomes.

The Benefits of AI in Insurance: How AI Agents Are Reshaping the Industry

Discover the game-changing benefits of AI in insurance. Learn how AI Agents improve customer experience, reduce costs, and boost efficiency in claims processing, underwriting, and customer interactions.

6 Strategies to Optimize Phone Number Inventory Management

Discover effective strategies for phone number inventory management and learn how to maintain a stellar phone number reputation. Explore best practices, expert insights, and innovative solutions to optimize your communication operations.

Your Policyholders Hate You... File That Under "Totally Preventable Losses"

Not everyone gets excited about buying insurance. Learn how AI Agents improve the experience for policyholders, bring down your cost to serve, improve your response times, and help you get rid of the hold music for good.

AI Agents for Education: 8 Use Cases for More Meaningful Student Outcomes

AI Agents for Education are transforming student engagement—boosting enrollment, improving retention, and making support more human. Discover 8 game-changing use cases that free up your staff while delivering better student outcomes.

Click Your Heels, Ditch the Guesswork: Start Winning with A/B Testing

Many contact center leaders still wander through customer journeys as if they're in the Land of Oz. Dive in to see why and how A/B testing is your shortcut to unlocking provably, repeatably, and scalably better CX.

5 Must Run A/B Tests for your AI Voice Agent

Maximize your AI Voice Agent’s impact with strategic conversational AI testing. Discover 5 must-run A/B tests to optimize engagement, refine responses, and drive better business outcomes.

Regal Named One of Forbes America’s Best Startup Employers 2025!

Regal is officially one of Forbes America’s Best Startup Employers 2025, ranking #164 out of 500. This recognition is a testament to our incredible team, our innovative work culture, and our unwavering commitment to advancing AI technology.

February 2025 Releases

Here's our February 2025 product releases, including live progressive dialer performance measurement, support for Outlook signatures, and some new API endpoints for better data access!

AI in Education: The Future of Student Engagement & Enrollment

AI in education is helping to streamline admissions, automate student engagement, and enhance higher ed outreach. Discover key education technology trends to boost enrollment and learn why automated student engagement tools are the future.

January 2025 Releases

Here's our January 2025 (and last December's) product releases, including user profile URLs, deleting unintentional contacts, and early access to our Outlook integration!

Regal’s Q1 Product Roadmap: Webinar Highlights & Recap

Regal’s Q1 2025 product roadmap brings AI Agents, Intelligent Orchestration, and Enterprise Functionality to the contact center. Discover what’s coming next!

AI Agent Assist: Real-Time Insights for Smarter CX

Discover how AI Agent Assist transforms CX by boosting agent efficiency and customer satisfaction. Get real-time insights, automate tedious tasks, and empower your team to drive revenue.

A No-BS Guide to Rescuing Your Contact Center with AI

Discover how AI in customer experience can revolutionize your contact center. Learn to replace legacy tools, scale personalized outreach, and drive better outcomes with modern CX platforms like Regal.ai.

20 Questions to Grade Your Personalized Customer Experience

Learn how to create a personalized customer experience using AI and automation. Discover actionable steps to grade your CX, expert insights, and strategies for 2025.

AI Agent Ethics & Disclosure Timing in 2025

Explore the ethics of AI agents and the impact of AI agent disclosure timing on customer trust. Learn best practices for balancing transparency and performance in AI-powered customer interactions.

2025 Contact Center Automation Trends and Tools

Discover the 2025 contact center automation trends and tools to help you grow. Learn how AI-powered tools and best practices can enhance efficiency, customer experience, and cost savings.

Introducing Regal Custom Objects

Build your own data model and keep your agents in one tool with Regal Custom Objects.

November 2024 Releases

2024 Year In Review

The year Enterprise customers embraced Regal and AI Phone Agents came to contact centers. We look back at major milestones achieved in 2024.

10 Essential Call Center Metrics and KPIs for 2025

Discover the 10 most essential call center metrics and KPIs for 2025. Learn how to measure and optimize your call center's performance with our comprehensive guide.

8 Ways AI Sales Tools Assist in the Success of Call Centers

Discover how AI sales tools enhance call center performance, improve efficiency, and increase customer satisfaction with these 10 powerful strategies.

What Is Conversation Intelligence? Improve CX & Sales Insights

Learn how conversation intelligence can enhance customer experience and provide valuable sales and support insights. Discover key technologies, implementation strategies, and best practices.

6 Ways Agent Insights Into the Buyer’s Journey Improve CX

Discover 6 real-world examples for how high-consideration B2C companies are arming their agents with real-time customer data to drive more effective sales, support and retention interactions across industries.

October 2024 Releases

10 Proactive Outreach Strategies to Build Loyalty with Personalization

In order to build loyalty, it’s essential to create proactive outreach strategies that demonstrate your awareness of your customers' challenges and show that you’re taking action before they even need to ask for help.

5 Ways to Improve Agent Productivity & 5X Outbound Performance

In this blog, we share tips to improve agent productivity for handling high volumes of calls, whether it's inbound support teams or outbound sales teams. The guiding principle is simple — focus your agents on tasks that absolutely requires their attention, and automate everything else.

8 Strategies to Improve Call Answer Rates by 40%

Discover 8 proven strategies to improve call answer rates by 40%. Learn how personalized outreach, Branded Caller ID, and AI-driven solutions can transform your outbound contact center and boost engagement. Increase your call answer rates and drive more revenue today!

The Modern B2C Growth Stack for High Consideration Businesses

The modern B2C growth stack for high consideration brands is different from the retail and B2B growth stacks. AI agents are the next change vector for these stacks.

September 2024 Releases

Customer Data Management: 10 Strategies to Personalize Interactions

Discover 10 customer data management strategies to help you build engagement, increase conversions, and drive revenue.

5 Outbound Call Center Strategies to Connect with Customers

Learn five game-changing outbound call center strategies that’ll help you reach the right people, at the right time, with the right message.

7 Tips for Building AI Agents That Perform

Explore tips for building AI Agents that perform for your business - Spoiler: It's a lot like coaching human agents.

AI Emotional Intelligence: How AI Agents Keep Calm

Learn more about how AI emotional intelligence allows artificially intelligent voice agents to create emotion-regulated interactions using empathy, while keeping their cool even in heated situations, ensuring calls stay on track and productive.

AI Phone Agents vs. AI Copilots for Human Agents

AI Phone Agents can only do a fraction of your interactions today. But they are the future. Understand why you should not wait to invest.

Introducing Custom Events in Agent Desktop Activity Feed

Improve agent context with a synthesized view of the buyer's journey -- leading to more personalized conversations.

August 2024 Releases

AI for Contact Centers: Insights from Industry Leading CEOs

Explore how AI for contact centers is transforming businesses and how leveraging AI can enhance customer interactions and improve retention.

July 2024 Releases

Power vs. Progressive vs. Predictive Dialers

There is no one "best" auto dialer. There's just the right auto dialer for your campaign goals. We compare the capabilities of each and outline a framework for how you can make the right choice.

Demystifying AMD: How Answering Machine Detection Really Works

Regal's advanced AMD algorithm uses multiple factors to determine if a human answers your call. Learn more about the different techniques available.

Outbound Abandon Rate: Why It Matters & 5 Strategies Master It

Explore 5 actionable strategies to keep your outbound abandon rate low, optimize your dialer efficiency, and maintain customer trust.

Pros and Cons of a Progressive Dialer

Progressive dialers achieve more reach and agent efficiency, but there are compliance and customer experience implications.

Key Metrics for Optimizing Branded Caller ID Performance

The effectiveness of your Branded Caller ID strategy should be evaluated based on business impact using these 5 metrics.

What Are the Best Use Cases for Branded Caller ID

Branded Caller ID is a great tool to increase call answer rates. Find out the best use cases for Branded Caller ID, and learn when it's not the right solution.

Treat your customers like royalty

Ready to see Regal in action?
Book a personalized demo.

Thank you! Click here if you are not redirected.

Oops! Something went wrong while submitting the form.