What is conversational AI? The complete guide for modern businesses [2025]

You’ve probably seen it by now — AI that chats, drafts, and even thinks.

Every day, customers ask numerous queries, prompting agents to search for answers, engineers to fix issues, and product teams to seek more context.

At the core of all these activities are conversations — constant, complex, and coming from every direction.

But, here’s the thing: as more AI tools deliver cold, robotic responses, customers expect conversations with them to be warm and human-like. They want to be understood — clearly, instantly, and naturally.

That’s where conversational AI comes in.

It acts as a team member by understanding customer intent, tracking context from past interactions, and helping your business respond not just swiftly but meaningfully.

In this blog, we’ll dive into how conversational AI works, why it matters, and how it’s reshaping the way businesses talk, connect, and support at scale.

What is conversational AI?

Conversational AI is an advanced software that enables users to chat with bots as if they’re interacting with human agents. Whether checking a request, fixing an issue, or getting step-by-step guidance, these bots simulate human-like conversation. The support team uses it to save time and resolve routine queries, enhancing overall productivity.

Deloitte predicts that by 2027, half of all businesses using generative AI will have deployed AI agents. This is a strong sign that hands-free, intelligent automation is fast becoming the new standard for customer support, helping teams scale faster, improve response times, and deliver more personalized experiences, customer satisfaction without scaling headcount.

Key benefits of conversational AI for businesses

Whether you’re a support manager, CX leader, product owner, or founder building for scale, conversational AI helps you meet customers where they are and answer faster, better, and smarter.

Projections indicate that by 2030, the worldwide AI chatbot market within the banking, financial services, and insurance (BFSI) sector is anticipated to surge to around $7 billion. In 2019, this market was assessed at approximately $586 million.

Let’s start with a few common pain points:

• Customers drop off when they’re forced to wait on hold, switch channels, or repeat themselves to get basic information.
• Business leaders struggle to deliver personalized support at scale without blowing up costs.
• Support managers find it hard to maintain round-the-clock service quality with limited teams and growing query volumes.
• Support teams are overwhelmed by repetitive L1 & L2 tickets like order updates, password resets, or shipping delays—leaving little room for actual problem-solving.

These challenges hurt both efficiency and experience, but with conversational AI, you flip the script:

1. Improves customer engagement with conversational AI applications

When a customer lands on your site, opens your app, or sends a message on social media, instead of funneling every query into a ticketing black hole and creating a wait room—conversational AI fixes it. It gives customers what they actually want: quick, personalized, and consistent answers, no matter where they reach out. Brands that prioritized customer engagement through AI technologies and conversational interface saw an average revenue increase of 123% over the past year.

See how DevRev’s conversational interface performs in an AI-native platform

2. Enhances operational efficiency while reducing costs

As business grows, it is normal for the support queues to swell, and not because customers are asking complex questions. It’s the opposite. Most teams are drowning in the basics:

• Where’s my order?
• How do I reset my password?
• Can I cancel this?

These types of questions stack up fast, draining bandwidth and slowing teams down, requiring the addition of headcount. This is where automation, powered by conversational AI, changes the total operational standpoint. By handling high-volume, low-effort L1 & L2 interactions across channels without human involvement, it clears the queue, keeps costs lean, and frees up your agents to focus on work that actually moves the needle.

3. Make your business more accessible and user-friendly with artificial intelligence

As business grows and queries pile up, support shouldn’t clock out when your team does. Your customers run into problems at odd hours, and if they don’t get answers fast, they bounce. That’s why accessibility today isn’t just about being available — it’s about being effortlessly helpful, any time of day.

In fact, 81% of customers try to resolve issues on their own before reaching out to a human agent. This shift opens up a powerful opportunity for businesses to build an intuitive self-service experience, like conversational search over knowledge bases, contextual responses, and always-available chat, that empowers customers while reducing load on your team.

Jump in to know: How AI search boosts your knowledge base.

4. Scale effortlessly with business needs conversational AI technology

It is normal for support queries to spike when your business grows, be it onboarding new customers, rolling out new features, or entering a new market. This brings more questions, tickets, and pressure on your team.

Instead of hiring an army of agents for every growth milestone, AI scales with you. By taking on the repetitive and routine L1 & L2 tickets, you can free up your experts for the trickier, more impactful L3 queries.

Every time I speak to our customers, they tell me how awesome our customer support team is. When we were just using slack, I was concerned about scaling. Now with DevRev, we'll be able to scale and maintain the same level of support.

Kshitij GuptaCo-Founder/CEO @ 100ms

Difference between conversational AI, agentic AI, & generative AI

Also read: Chatbots vs. conversational AI—what your business really needs

Core components of conversational AI platform

conversational AI platform is not just a chatbot giving replies; it’s a smart, layered system built on a foundation models of integrated technologies that work together to understand, process, and respond to users’ queries in human language in real time.

1. Text analysis and automatic speech recognition

These are the two components that help conversational AI’s understanding much better: what you say and how you say it—whether you type or smart speak.

• Text analysis: It breaks down written input to figure out key parts of a sentence — like who’s doing what — and catches the meaning, tone, or urgency behind the words.
• Speech recognition: It turns spoken words into text, so the AI algorithms can process voice commands just like written ones.

2. Natural Language Processing (NLP)

NLP (Natural language processing in conversational AI technologies) basically helps the system understand what people say or type—just like how a human would. When someone writes “Wya?” or “Need help ASAP,” NLP figures out what that means in context.

​​It breaks down the sentence, spots important words, understands the sentiment, and figures out what the person really wants. This helps the system respond in a smart, human-like way—whether it’s answering a question, guiding a user, or pulling the right info.

3. Dialogue system management

This is the part of the system that keeps the conversation flowing between the user and the system by understanding the context. It remembers what the user said earlier, figures out what they really mean, and helps the AI technologies to decide what to say next.

Whether the chat has one question or a bunch of back-and-forth messages, sentiment analysis in an AI dialogue system keeps it smooth and on-topic.

4. Natural Language Generation (NLG)

Once the system understands what the user wants, NLG helps the AI talk back to users in a way that feels natural and human. By creating a clear, friendly response using system data.

It can use simple templates or advanced AI models, but the goal is always the same: to turn complex info into easy, helpful messages people understand.

5. Machine learning

ML (machine learning algorithms) is what helps the conversational AI technologies get better with time. It learns from large sets of human conversations to improve how it understands questions, picks the right replies, and keeps the conversation flowing.

Techniques like deep learning and reinforcement learning allow the system to grow smarter as it handles more interactions, just like people learn by practicing.

How does conversational AI work?

Conversational AI works like a smart assistant, which is supported by deep neural networks (DNN) and machine learning (ML), and consists of the following steps:

1. User gives the input (Start of conversation)

When a user interacts with the system, either by typing or speaking, the automatic speech recognition (ASR) pops in. If it’s a text message, the system directly processes it. If it’s a voice input, the system first converts speech to text before processing.

Example:

When the user sends “I need help with my account” (or)

Say “Hey, I can’t log in!”

In both cases, the ASR ensures that the system captures the exact message, whether written or spoken—and prepares it for the next step. It does this by identifying the keywords and phrases from the input so the AI can begin to understand what the user needs.

Recommended reading: Why AI search is now foundational to intelligent conversations.

2. The message is understood

After the AI system understands the key parts of the user’s input, it has to understand the meaning behind it (human language) by determining what the user means.

Example:

“Hey, my refund is taking forever!

Even if the message is casual or slightly unclear, natural language understanding (NLU) in conversational AI detects the intent, sentiment, and entity behind the query. It converts this raw, unstructured input into clean, structured data that the system can work with, setting the stage for an accurate, helpful response.

3. Context is checked

As a following step, understanding a single message in isolation is not enough; conversation has context, which needs to be checked to respond accurately.

Example:

The user sends the first message – “I paid last week.”

Then, they send a follow-up – “Why is it still pending?”

The system uses sentiment analysis in an AI dialogue system management to recognize that the user is referring to their previous payment. It keeps track of the conversation history, helping the system stay consistent and relevant in its replies, like checking the payment status or escalating the issue.

4. Generates relevant responses to the query

After it understands the intent, the conversational artificial intelligence generates a precise, human-like response to the user queries using natural language generation (NLG).

Example:

Data: Refund issued on May 26

Response: “Your refund was processed on May 26. It should reach your account in 3–5 business days.”

NLG takes structured data, customer data or system knowledge for the query and turns it into a human language-sounding, clear, and contextually appropriate response.

5. The system keeps learning

What makes conversational artificial intelligence powerful is that it doesn’t stay static – it gets better over time by learning and adapting to the environment, intent, and data.

Example:

If many users keep asking “Where’s my stuff?” instead of “Track order,” the artificial intelligence learns to interpret casual phrasing as the same intent and responds accordingly.

This continuous improvement is driven by machine learning (ML). As conversational AI system interacts with more users, it gathers data to improve natural language understanding, conversation flow, and reply accuracy. Feedback loops and behavior patterns help the system predict intent better and respond more naturally with each customer interaction.

3 Use cases of conversational AI across industries

With industries becoming more digitalized than before, conversational AI platforms are looping in the process and transforming how those industries operate. Conversational AI solutions make rigid, legacy chatbots go beyond by offering contextual, intelligent support that’s always on.

Here are the three strong use cases where conversational AI software is ruling the industry:

1. SaaS: Scales support while closing the product feedback loop

As businesses increasingly rely on SaaS to operate smoothly, many companies are struggling to keep up with rising support ticket volumes, siloed feedback, and growing product misalignment. This surge in usage is accompanied by higher customer expectations—for faster resolutions and a better overall experience.

To meet that demand without endlessly expanding support teams, businesses are turning to conversational AI—a smarter way to automate routine tasks, scale operations without scaling headcount.

How conversational AI tools resolve it:

• Resolves the high-frequency support queries L1 & L2 instantly (e.g., login issues, setup guidance) without human involvement.
• Triages incoming tickets using context and intent, ensuring faster routing and resolution.
• Seamlessly connects user feedback from support chats to product teams automatically, creating workflow items with context.
• Summarizes long conversations across email, Slack, or chat to update internal teams and avoid duplication.
• Offers 24/7 multilingual support, which is crucial for global SaaS operations.

AI conversational example:

As Bolt expanded its SaaS operations, it faced challenges with its fragmented support systems, limited self-service, and problematic help-site search functionality, which strained its support team.

To address these issues, Bolt implemented DevRev’s conversational AI platform, bringing search, ticketing, and workflows into one connected system. By embedding PLuG, a multilingual AI Copilot, directly within the product, Bolt empowered merchants to resolve issues instantly using an intelligent knowledge base. And, with automated triage, contextual deflection, and conversational AI search, agents focused on critical cases while repetitive queries resolved themselves.

This resulted in a 43% drop in ticket volume, 40% faster resolutions, and a 25% boost in customer retention—turning support from a bottleneck into a growth enabler.

DevRev significantly improved our help-site by developing a search widget that automates and enhances information retrieval, solving traditional manual searching issues and streamlining merchant interactions.

Elec BootheSenior Manager, Support and Technical Writing, Bolt

2. FinTech: Automates the transactional support and compliance queries

FinTech platforms are being operated in real-time, in high-trust environments, where every second matters. From payment failures to KYC verification and refund tracking, customers are expecting instant and accurate answers. But as platforms scale, traditional support systems collapse under the pressure of growing query volumes, regulatory complexity, and the need for around-the-clock service.

How conversational AI tools resolve it:

• AI conversational agents with their built-in NLP instantly respond to transactional FAQs like “Where’s my refund?” or “Why was my card declined?”
• Detects sentiment like frustration → auto-prioritizes and escalates.
• Answer frequently asked questions (L1/L2) via pre-built flows + knowledge integration.
• Remembers previous customer interactions with sentiment analysis in an AI dialogue system → no need for customers to repeat info.

Route complex customer queries to the right agent using past resolution data.

AI conversational example:

PayPal scaled its FinTech operations and faced increasing pressure to deliver real-time support for transactional and compliance queries, without adding to support costs. With customers wanting answers to their questions instantly. To meet these expectations, PayPal deployed AI agents that could understand user intent, detect sentiment, and respond contextually across digital channels. Resulting in a 20% drop in customer support costs, a 25% boost in user engagement, and a scalable support model fit for a high-trust environment.

3. Banking: Handling repetitive queries seamlessly and streamlining the operation

As banking becomes increasingly digital, like other businesses, the customer experience has become critical to building loyalty, satisfaction, and retention. Yet support teams—both customer-facing and internal—are overwhelmed with repetitive queries around transaction status, card issues, account updates, and KYC checks. Traditional IVRs and ticketing systems often delay responses and frustrate users.

How conversational AI tools resolve it:

• Answer user queries (e.g., balance checks, failed payments) instantly using a secure conversational knowledge base, freeing up agents.
• AI agents auto-triage issues like declined cards or failed UPI, assigning them based on intent, priority, and past patterns.
• Conversational AI applications detects intent, context, and delivers multilingual support across chat, WhatsApp, and web with omnichannel integration.
• Integrates with core banking systems and CRMs to offer secure, real-time updates on account status and transactions.
• For internal teams (sales ops, branches), conversational AI copilots retrieve app logs and context to resolve technical queries faster, without waiting on the L1 helpdesk.

AI conversational example:

When Capital One launched Eno, the first text-based conversational AI solution from a major U.S. bank, they didn’t just digitize support—they humanized it. Customers could check their balance by texting “balance,” a 💰 emoji, or even just “$”—and Eno would respond in seconds. But it didn’t stop there. Eno proactively monitored spending, flagged unusual charges, and even created virtual card numbers for secure online shopping.

This wasn’t just about convenience—it was about building trust and loyalty at scale. With a customer rating of 4.7 out of 5, Eno proved how AI, when designed with emotional intelligence, could deflect thousands of repetitive queries while keeping service personal and always-on with consistent brand voice.

How to strategically implement conversational AI cloud in your business?

Implementing conversational AI isn’t just about deploying an AI chatbot—it’s about building a connected, intelligent support system that scales with your product and customers.

Here’s how to strategically roll out conversational AI to increase customer satisfaction:

1. Start with your support and product data repo

AI models are only effective when they are completely trained on data. To give it that, unify your customer-facing and product data sources—support tickets, customer requests, chat logs, knowledge base articles, changelogs, onboarding docs, feature releases, and usage metrics—into one connected backend.

Why it matters:

• Isolated data leads to vague or incorrect responses to customer queries.
• Shared data helps the AI agents understand and connect the dots across user queries and product behavior.
• This unified base AI provides relevant responses and contextual support to the conversational queries that evolve with your product.

2. Define use cases based on query complexity

A strategic implementation starts by understanding what should be automated. Begin with an audit of your incoming support volume—categorize tickets based on frequency and complexity. High-volume, low-complexity queries like “reset password,” “KYC document upload,” or “track refund” are ideal for full automation.

More nuanced issues that are highly sensitive and need human agent attention should be routed automatically by contextual understanding the intent, context, and urgency. This way, agentic AI become a more focused tool for operational efficiency, not a blunt force for all issues.

Why it matters:

• Avoids the trap of over-automation of L1, L2, & L3 queries, which can alienate users.
• Ensures high-value human effort is preserved for complex queries.
• Delivers faster ROI by targeting the most complex queries and impactful use cases first.

3. Deploy conversational AI across all customer touchpoints

Customers don’t wait. They message you when they want answers—on your website, inside your product, through your app, or on WhatsApp. If your AI agents live in just one corner (like a chatbot buried on your homepage), you are making a pathway to bombard your system with tickets.

Conversational AI needs to be everywhere your users are for their seamless communication. Embed it across every channel: in-product, on-site, mobile, email, Slack, WhatsApp—wherever support is expected for offering better customer satisfaction.

Why it matters:

• Delivers a consistent customer experience across all touchpoints and throughout customer journey.
• Fast, personalized help directly impacts NPS, CSAT, and renewal rates.
• AI conversational agents resolve low complexity queries before they are escalated to agents.
• Improves self-service adoption.

4. Train AI agents with product knowledge and user behavior, not just scripts

To move from reactive to proactive support, your conversational AI must be trained like your best support rep: with deep product knowledge and real user context.

This goes far beyond scripts and templates. Your AI algorithms should consume the same inputs your support and product teams use to stay sharp—product documentation, engineering release notes, changelogs, past support conversations, known issues, and internal wikis. This will help conversational AI agents provide accurate responses with contextual understanding and up-to-date information.

Why it matters:

• When conversational AI chatbots understand your product deeply, it can resolve more tickets on its own—including L2-level issues.
• Reduces reliance on engineering for conversational AI cloud updates—knowledge grows organically.

5. Set up feedback loop to constantly improve conversational AI performance

No AI system is ever “set and forget.” As businesses scale, so do customer expectations. Establish ongoing feedback loops using key metrics—deflection rate, resolution accuracy, escalation frequency, and sentiment analysis.

Regularly review where the conversational AI tools succeed, where they fail, and how real users perceive them. Create conversational flows to refine intents, update outdated answers, and retrain the model based on real conversations.

Why it matters:

• Spot emerging product issues before they spike.
• Identify gaps in the knowledge base or misunderstood intents.
• Continuously retrain AI agents with real-world conversations.

6. Scale with a hybrid model: humans + AI

Though conversational AI software handles your L1 & L2 tickets seamlessly, don’t aim for full automation—aim for smart automation. Build a hybrid model where AI handles the repetitive, transactional support, while your human agents handle complex queries.

This is where routing, escalation, and internal collaboration become critical. Integrate your AI system with ticketing, CRM, and internal collaboration tools to ensure seamless handoffs and response quality.

Why it matters:

• Combines AI speed with human empathy—crucial for trust and retention.
• Reduces burnout in support teams by offloading repetitive work.
• Future-proofs your support system as query volumes, complexity, and customer expectations grow.

Recommended reading: How conversational AI can help your internal team work smarter.

Best practices for high-impact conversational AI

1. Focus on user experience

Design your conversational AI like your best support rep: clear, reliable, and helpful. By going beyond simple chatbot flows to enable natural language understanding (NLU), contextual memory across interactions, and semantic search that grasps customer intent—not just keywords.

Let’s say, if a user types “how do I get my invoice?” Semantic search can return help articles with relevant context often showing a single, precise answer first, followed by a few closely related suggestions (if needed). These experiences should guide users seamlessly with smart suggestions and guardrails, reducing friction and confusion.

2. Create natural and engaging dialogue

Unlike traditional AI, the best conversational AI goes beyond just providing answers—it connects. By moving away from robotic scripts and pre-set responses, it generates replies that incorporate tone and personality, aligning with your brand’s identity. Additionally, it utilizes contextual memory, allowing it to recognize users and avoid repetitive questions. When uncertain, it asks clarifying questions instead of making guesses.

3. Ensure data privacy and security

Data privacy isn’t an afterthought. Ensure data is encrypted both in transit and at rest (AES-256 is a must, with TLS for transit). Anonymize and redact PII where possible, and include audit logs and role-based access controls.

So, look for a platform like DevRev that is built on privacy-by-design frameworks and compliance readiness (GDPR, SOC 2, HIPAA).

4. Response time and reliability

Users always want answers to their questions, and it should be now. Optimize your infrastructure with caching for frequent queries and fallback systems to handle spikes.

When an AI system fails or stumbles, present a graceful message with the option: “Would you like to talk to a live agent?” Use real-time performance dashboards to monitor latency and uptime.

5. Cultural and linguistic adaptation

Your customers aren’t all the same, and your AI shouldn’t treat them that way. For example, your users may speak English, but not the same English; it varies from region to region, like American, British, Canadian, & Australian. In that case, it is good not to rely on the same message across all languages.

Use regional training data to tune the AI for realistic phrasing and sentiment. Detect language in each session and respond accordingly without making the user switch language modes.

Challenges and solutions in implementing conversational AI

Rolling out conversational AI isn’t just a technical project but an organizational shift. From training accuracy to building trust and scale, each step has its own challenges.

Let’s see the pressing challenges of conversational AI and how to navigate them:

1. Natural language processing (NLP) accuracy

AI agents’ ability to understand human intent, not just keywords, is what separates helpful AI chatbots from frustrating ones. However, in evolving customer expectations to receive answers now, conversational AI solutions at times fail to understand domain-specific terms, multi-turn conversations, or user sentiment, leading to irrelevant answers or generic responses.

2. Multilingual and contextual support

As businesses processes scale globally, your users expect native-language support. But translating queries isn’t enough—your AI must grasp regional phrasing, idioms, sentiment, and user intent in different languages, sometimes even within the same session.

This presents a major hurdle for conversational AI systems that are either trained in limited languages or depend on rigid translation APIs.

3. Data privacy and security

Conversational AI is completely trained on customer and product data to outperform the tasks given and answer the queries. It requires deep access to sensitive data, including user history, billing info, or internal documents, to function well.

However, this opens the door for data breaches and security risks–misuse, leakage, or non-compliance with regulations like GDPR, SOC 2, or HIPAA.

4. Integrating with an existing legacy system

As conversational AI agents rely completely on data, they become powerful only when they connect to systems, CRMs, ticketing systems, product logs, and internal databases.

Without deep integration, AI becomes a disconnected experience that can’t access the real-time context needed to answer queries accurately. Worse, they may provide outdated or misleading information, eroding trust and creating more manual overhead for support teams.

Also read: How to make the CRM migration painless.

5. Scaling a conversational AI model

What works in a sandbox fails in production. As support volume grows, AI systems must handle thousands of edge cases, intent variations, and dynamic product updates, without losing speed or accuracy.

6. Ethical Considerations in conversational AI

Although conversational AI helps in resolving the L1 & L2 queries, you can’t solely let the AI agents handle and perform the tasks. AI can misrepresent facts, mishandle escalations, or reflect bias in responses, especially when handling billing issues, outages, or complaints.

When there’s no human oversight, automation can go unchecked and impact trust. We must have encountered this issue with OpenAI—chatGPT. The AI agent hallucinated and answered the queries with outdated and old data from its storage.

Conversational AI trend to look for beyond 2025:

1. Global language inclusion with cultural context

There’s always an instant sense of comfort when customers receive support in their native language. It builds trust, shows respect, and creates an emotional connection. Just as businesses localize their websites to reflect cultural identity and say, “We’re one of you,” conversational AI is evolving to do the same—from simple translation to true cultural adaptation.

Beyond just speaking the language, modern conversational AI tools will recognize local idioms, customer sentiment, and even regional compliance norms. This ensures interactions feel native to users, regardless of the region.

2. Quantum AI integration for real-time decision-making

The rise of quantum computing is already redefining the speed and depth of AI reasoning. In the near future, conversational AI will leverage quantum power to process massive datasets, simulate complex decision paths, and resolve ambiguous queries near-instantly.

This is particularly useful for high-stakes conversations, like SLA-bound escalations, multi-system outages, or rapid triaging of product incidents, where traditional AI models often fail under pressure.

3. AI-assisted creativity in support and product flows

Conversational AI agents are already doing much more – writing emails and creating workflows- than just resolving tickets. Now, it is enhancing itself even better by co-creating. From suggesting product enhancement, designing content campaigns, or auto-generating support workflows, AI will serve as a creative partner that understands both the user and the business objectives, enabling faster, smarter, and more aligned execution across support and product teams.

4. Multimodal conversations for faster clarity

Conversational AI, powered by generative AI, can already understand text, voice, and image as input. It’s no longer restricted to typing messages; with its increasingly revolutionary technology, it can now understand voice and even videos.

That means you can communicate with support the way you want—by speaking, sending screenshots, or recording your screen. This makes it easier to explain problems and faster to get help. It’s like having a support agent who truly understands you, no matter how you communicate.

5. Integration with IoT devices

With conversational AI software already ruling across industries, it won’t just live in your chat window or voice assistant—it will work hand-in-hand with smart devices like wearables, home gadgets, and factory machines.

Let’s say a factory machine sends an alert through a chatbot saying, “I’m overheating,” and the AI automatically slows it down or calls for help without any human needing to step in.

This is where conversational AI and the Internet of Things (IoT) come together. The AI will “talk” to your devices, collect information, understand what’s going wrong, and take action to fix it, while keeping you in the loop the whole time.

Future of conversational AI in customer service

We’ve come a long way from clunky chatbots and static help centers. Whether it’s customers seeking answers, agents resolving issues, or product teams chasing clarity — conversations are at the heart of it all. And when powered by AI, these interactions become faster, smarter, and more meaningful.

With a projected growth from USD 6.8 billion in 2021 to an impressive USD 18.4 billion by 2026, the market is poised for a remarkable Compound Annual Growth Rate (CAGR) of 21.8%. This growth, underpinned by the relentless advancement of technology, will usher in a new era of conversational AI.

As customer expectations continue to rise, businesses can no longer afford to remain in rigid legacy systems or rely on AI that’s simply bolted on. Your customers want the system to understand the why behind a query, not just the what.

And, other side of the pole businesses needs more than a chatbots. A platform that brings together support, product, and engineering teams — with conversational AI working behind the scenes to connect the dots.

This is where DevRev steps in: Your gateway to smarter, more human-like conversations

Built on a real-time knowledge graph, DevRev connects your customer data, product insights, and support history — bringing full context to every interactions. Its agent AI doesn’t just respond — it understands, routes, summarizes, and helps resolve issues autonomously or alongside your teams.

All this on a single platform where support, product, and engineering stay aligned — automatically.

Ready to move from reactive support to intelligent conversations. Book a demo!