Linq's $20M Series A: How AI Assistants Are Moving Into Messaging Apps [2025]

Introduction: The Quiet Shift From Apps to Conversations

We're living through an interesting contradiction. Everyone talks about app fatigue, about too many notifications, about notification hell. Yet we keep building new apps, new platforms, new places where users have to go to talk to AI assistants.

Then something shifts. You notice your friends aren't opening Chat GPT or Claude as separate apps anymore. They're just texting. They're asking questions in iMessage. They're getting answers back without ever leaving the app that's already embedded in their muscle memory.

This is the world Linq is betting on, and it just raised $20 million to prove the thesis is real.

Linq isn't a household name yet. The startup, founded by former Shipt executives Elliott Potter (CEO), Patrick Sullivan (CTO), and Jared Mattsson (President), spent years building infrastructure that most people will never see. But what they've built matters more now than it ever has: an API that lets companies and developers deploy AI assistants directly into iMessage, RCS, and SMS.

Here's what makes this radical: those blue bubble messages you get from your friends? Those are actually from AI agents. Your customer service chatbot? Living in your SMS thread with your actual friends. The restaurant reservation system? Threaded right into your messages.

The pivot that got them here tells you something important about how markets actually work. Linq started as a digital business card tool. Then it pivoted to SMS-to-iMessage upgrading for B2B communication. Then it pivoted again. Now, with the rise of AI agents actually becoming useful enough to talk to without wanting to throw your phone, Linq saw an opening that could dwarf everything they'd built before.

Within eight months of launching their iMessage API in February 2025, Linq doubled the annual recurring revenue it had built over four years. Their customer base expanded 132% quarter-over-quarter. Their retention metrics hit something that makes most SaaS companies dream: 295% net revenue retention with zero churn.

But here's the thing that matters: this isn't just about replacing SMS. It's about a fundamental shift in how we interact with software. Not through apps. Not through websites. Through the medium that's already the backbone of communication for billions of people.

The $20 million Series A, led by TQ Ventures with participation from Mucker Capital and angel investors, is validation that this shift is real. More importantly, it's fuel for what could become essential infrastructure in a world where AI agents are as common as customer service chatbots are today.

Let's break down what's actually happening here, why it matters, and what it means for the future of how you'll interact with AI.

The History of Business Messaging: SMS, Branding, and the Blue Bubble Problem

To understand why Linq exists, you need to understand the incredibly frustrating world of business SMS.

For two decades, companies have been obsessed with reaching customers where they already are. That place is their phone. Specifically, it's their text messages. So every company from your bank to your airline to your favorite restaurant started texting you.

But there's a problem: when a business texts you, it looks like a business texting you. The messages come in green or gray. There's no profile picture. It doesn't feel like talking to another human. It feels like what it is: a system sending you information you may or may not want.

Twilio built an $18.26 billion company on the back of this. They made it easy for companies to send text messages at scale. Apple built the Messages for Business service, which lets companies text iPhones with slightly more polish. But no matter how you do it, customers know they're talking to a business.

Then Linq asked a simple question: what if they didn't know?

The company launched an API in February 2025 that does something Apple technically allows but hasn't made easy: it lets businesses send iMessages that look like they're coming from a person. Blue bubbles. Full feature set. All the stuff that makes iPhone texting feel natural.

This is important because perception matters. When I get a blue bubble message, my brain categorizes it differently. It's more urgent, more human, more trustworthy. I respond differently. The person (or system) on the other end gets faster responses, deeper engagement, better interactions.

Linq's customers saw this immediately. Their retention exploded. Their engagement skyrocketed. And suddenly, a company that had been quietly servicing B2B communication use cases was sitting on something bigger.

The technical achievement here is actually non-trivial. Sending iMessages at scale isn't like sending SMS. iMessage is Apple's proprietary protocol. It's built into the iOS ecosystem. Getting permission to integrate with it, keeping the infrastructure reliable, maintaining authentication so that bad actors can't impersonate real people—it all requires serious technical depth and deep relationships with Apple's platform teams.

Linq solved this. They built an API. They wrapped it in reliability and authentication and compliance. And they made it so that a developer could, in minutes, connect an AI assistant to iMessage.

The result was a 4-year company that doubled its revenue in 8 months by pivoting to a market that hadn't even fully materialized yet.

Linq provides significant benefits for developers, including reduced go-to-market friction, handling integration complexities, and enabling a 34% average account expansion. Estimated data based on typical platform advantages.

When Poke Went Viral: The AI Agent Catalyst Moment

In spring of 2024, something happened that changed Linq's trajectory entirely.

A company called the Interaction Company, building something called Poke (poke.com), came to Linq asking a strange question: can we use your API to deliver our AI assistant?

Poke is an AI assistant that lives in iMessage. You can ask it questions, have it help you schedule your calendar, accomplish tasks. It's built for iPhone first, which is unusual. Most AI assistants are built as web apps or native apps. This one was different.

When Poke launched in September 2024, something unexpected happened: it went genuinely viral. Not "tech Twitter thinks this is cool" viral. Actually viral. People were talking about it. Using it. Recommending it.

Then Linq's phone started ringing. Not with B2B customers wanting iMessage integration for their customer service. With AI companies wanting to deploy their agents through Linq's API.

This was the catalyst moment.

Elliot Potter and his team realized something fundamental had shifted. AI had become good enough that you didn't need an app. You didn't need to navigate to a website. You didn't need a special client or special tool. You just needed an intelligent enough system that understood what you wanted, could take actions, and could be accessed through something you already used every single day.

The insight is almost brutally simple: iPhone users have iMessage open all the time. They check it dozens of times a day. They trust it. They have the thread history with their friends. The notification system is integrated. Everything about iMessage is optimized for constant, lightweight interaction.

That's literally the perfect interface for an AI agent.

Poke's viral success created a gold rush moment. Suddenly, dozens of AI companies saw the opportunity. Why build yet another app that competes with 7 million other apps? Why compete for home screen real estate? Why force users to remember to open yet another application?

Just talk to the AI in iMessage. That's it.

Linq immediately saw that they had a choice. Continue being a spoke in the wheel, focusing on B2B customer service and sales communication. Or become the hub. Build the infrastructure layer that all these different AI applications would run through.

They chose the hub.

Linq's metrics significantly outperform typical SaaS benchmarks, particularly in NRR and customer churn, indicating strong product-market fit and customer retention.

The Technical Architecture: How AI Assistants Live Inside Messaging

Understanding how Linq's platform actually works requires understanding some of the complexity hidden behind that simple blue bubble.

When you send an iMessage from your iPhone, a lot of things happen under the hood. Your message gets encrypted end-to-end. It gets routed through Apple's servers or through SMS fallback if the recipient is on Android. It gets delivered, with read receipts, typing indicators, the full rich feature set that makes modern messaging modern.

Linq's API lets developers tap into that same infrastructure for their AI agents. That means when your AI assistant responds, it can do it with full feature parity: group chats, threaded replies, emojis, voice notes, image attachments, everything.

The technical challenge is massive. Linq needs to:

Maintain authentication and trust: iMessage is encrypted end-to-end. Linq needs to maintain cryptographic keys and authentication that proves these messages are legitimate and coming from the right sender. This isn't like HTTP APIs where you just pass a bearer token. This is integrating with Apple's security infrastructure.

Handle scale: Linq claims to be facilitating over 30 million messages per month. That's not a trivial infrastructure problem. These messages need to be routed, stored, tracked, logged, and monitored in real-time, all while maintaining reliability.

Support multi-platform delivery: Not everyone uses iMessage. Some customers have Android phones. Some prefer RCS, which is the next-generation protocol replacing SMS. Linq supports SMS as fallback. This means their infrastructure needs to handle message routing to the right protocol, on the right platform, with the right features.

Integrate with AI workflows: The API doesn't just send messages. It needs to route messages back to the AI system, provide context, handle function calling (when an AI needs to take an action like booking a calendar slot), and maintain conversation state across distributed systems.

From a developer's perspective, the API is elegantly simple. You define your AI agent. You integrate it with Linq's SDK. You tell Linq where to send messages and what your agent should do. From that point forward, when someone messages your agent in iMessage, the message gets routed to Linq's servers, which route it to your AI system, which processes it and sends a response back through the same path.

The hard part—keeping it secure, fast, reliable, and compliant—is all happening behind the scenes.

Market Size and Opportunity: From SMS to Conversational AI Infrastructure

Let's talk about the size of this opportunity, because the numbers are genuinely staggering.

Twilio is a $18.26 billion company. Their core product is sending SMS messages on behalf of businesses. That's it. Billions of SMS messages, used by millions of businesses, generating tens of billions in annual value.

Now, imagine a platform that does everything Twilio does for SMS, but for iMessage, RCS, and AI agents. Imagine thousands of AI companies building assistants that need a distribution channel. Imagine every major brand deploying customer service AI that lives in iMessage instead of a separate app.

Linq isn't trying to replace Twilio. They're trying to build on top of a different medium that's arguably more valuable: not transactional SMS, but conversational AI.

The math is interesting. Let's think about this from first principles:

Number of iPhone users globally: ~1.2 billion people actively use iMessage.

Average business interactions per person per month: Most people interact with 5-10 businesses via text or chat each month (customer service, shipping updates, appointment confirmations, etc.).

Average AI agent interactions per business customer: As AI agents become better, users will interact with them more frequently. Current estimates range from 2-5 interactions per user per month for AI assistants.

That suggests a total addressable market (TAM) of at least 6-12 billion messages per month from AI agents alone. Linq is currently facilitating 30 million messages per month. Even at just 10x their current volume, they'd still be capturing a fraction of the total opportunity.

Here's where it gets really interesting: the pricing model. Linq doesn't charge per message like Twilio does for SMS. They charge per customer or per API call. This means as AI agents get smarter and talk more, Linq's revenue doesn't scale linearly with message volume—it scales with the number of deployed agents and the richness of their capabilities.

Potter has said publicly that their vision extends far beyond messaging. They want to be the infrastructure layer for all conversational AI, regardless of channel. That means not just iMessage, RCS, and SMS, but Slack, email, Telegram, WhatsApp, Discord, Signal, anywhere your customers are.

If they execute on that vision, the TAM expands by an order of magnitude.

Linq excels in AI compatibility and scalability, while Apple's Messages leads in integration. Twilio offers a balanced feature set but lacks in AI focus. Estimated data based on platform capabilities.

The Metrics That Matter: 295% NRR and Zero Churn

Linq's growth metrics are worth studying because they tell you something about product-market fit that typical growth numbers don't.

The company claims:

• Customer base expansion: 132% quarter-over-quarter
• Per-customer revenue expansion: 34% average account expansion
• Net Revenue Retention (NRR): 295%
• Customer churn: Zero
• Monthly Active Users on platform: 134,000 across customer AI agents
• Monthly messages: 30+ million

Let's unpack what these numbers actually mean, because they're not typical SaaS metrics.

A 295% NRR is exceptional. Typical good SaaS companies have NRR in the 110-130% range. This means that if Linq had

1. Their customers are finding massive value
2. Their product is becoming more integral to their customer's business
3. Their price increases are working (either explicit increases or customers moving to higher-tier products)

Zero churn is unusual for infrastructure companies, particularly young ones. It suggests that switching costs are high (once you integrate Linq into your AI agent, switching is painful), retention is strong (customers are actively using the product and getting value), and their support and product are good enough that customers aren't leaving for competitors.

The 132% quarter-over-quarter customer growth is solid but not explosive. What's more impressive is the 34% average account expansion. This means each customer is buying more from Linq. This happens when:

• Existing customers deploy more AI agents
• Existing customers' agents grow in volume/usage
• Existing customers move into additional channels (iMessage → RCS → SMS or future channels like Slack)

That 34% average account expansion, combined with zero churn, creates this mathematical magic where you're growing revenue much faster than you're growing customers. This is the definition of operating leverage.

The 134,000 monthly active users and 30+ million messages tell you the platform is being used. But here's the key metric hidden in that number: what's the message volume per user per month?

30 million messages divided by 134,000 monthly active users = approximately 224 messages per user per month. That's about 7 messages per day per active user. For an AI assistant, that's healthy engagement. It suggests people are actually using these agents multiple times daily.

All of these metrics point to one conclusion: Linq has product-market fit. This is a platform that customers need, are willing to pay for, are expanding within, and aren't leaving.

Competitive Landscape: Why Linq vs Twilio vs Apple's Native Solutions

Linq is not operating in a vacuum. It's competing against established platforms that have massive distribution, resources, and customer bases.

Twilio ($18.26B market cap) has SMS figured out. They've spent 15 years building messaging infrastructure. They have millions of customers. But they're constrained by SMS's limitations: no encryption, no rich features, limited to text. Their iMessage integration exists but it's not their focus.

Apple's Messages for Business is built into every iPhone. It's free to use. It integrates perfectly with the OS. But it's designed for transactional messages and basic customer service, not for AI agents. It doesn't expose the full feature set. It's not built for programmatic integration at scale.

RCS providers like Sinch and Vonage offer similar services to Twilio but for RCS. RCS is better than SMS but it's still primarily designed for transactional messages, not conversations with AI agents.

Standalone AI agents like Chat GPT, Claude, and others have their own apps. They have distribution. But they're constrained by the app limitation. Users have to remember to open them.

Linq's advantage is that it's purpose-built for conversational AI delivered through messaging. It's not trying to be a general SMS platform. It's not trying to be a general AI assistant. It's the plumbing that connects the two.

This is a classic platform positioning: you're not the content, you're not the channel, you're the infrastructure that lets content flow through the channel efficiently.

That said, Linq has competitive threats:

Apple could decide to build this themselves: Apple has the resources, the platform advantage, and the direct relationship with iMessage users. If they decided conversational AI through iMessage was strategic, they could build a competitive product.

Twilio could move upmarket: Twilio could acquire a company like Linq or build their own iMessage integration and AI agent platform. They have the distribution.

New entrants could emerge: Any team that understands both Apple's platform and AI agents could theoretically build a competing product.

But right now, Linq has a first-mover advantage. Their customers are growing. Their retention is incredible. And they're becoming embedded into the infrastructure of how AI agents get deployed.

Achieving $100M+ ARR and expanding beyond iMessage are the most critical steps for Linq's valuation growth towards IPO. Estimated data.

The Developer Experience: How Easy Is It to Integrate?

All the infrastructure in the world doesn't matter if developers don't want to use it.

Linq's value proposition to developers is essentially: "Stop thinking about building an app. Build an AI agent that lives in messaging."

For a developer, the typical path to deploying an AI assistant used to look like:

1. Build the AI model or integrate an existing one (OpenAI, Anthropic, etc.)
2. Build a web interface
3. Build a mobile app (iOS and Android) if you want mobile users
4. Handle authentication and user management
5. Deploy, monitor, and scale infrastructure
6. Market and distribute your app
7. Maintain the app as iOS and Android versions change

It's a lot of work. And even if you do it perfectly, you're competing for home screen real estate against millions of other apps.

With Linq, it looks like:

1. Build the AI model or integrate an existing one
2. Integrate Linq's SDK
3. Done

Your AI agent is now available in iMessage. It's accessible to every iPhone user. You don't maintain an app. You don't worry about distribution. Users find your AI agent the same way they find any contact: by searching or by being invited.

This is a dramatically different proposition, particularly for indie developers and small teams.

The technical integration is likely straightforward. Linq has published documentation on how to integrate. You authenticate with your developer credentials. You define your agent configuration. You point Linq at your LLM or AI backend. From that point, message routing is handled automatically.

The harder part is building an AI agent that's actually useful in a conversational, real-time context. But that's not Linq's problem. That's the developer's problem. Linq's job is just to handle the plumbing.

Where Linq becomes really valuable is in the features they provide to developers:

• Conversation state management: Keeping track of context across messages
• Function calling and tool integration: Letting AI agents take real actions (book appointments, check inventory, etc.)
• Analytics and monitoring: Understanding how users interact with agents
• A/B testing: Comparing different agent behaviors
• Compliance and audit logs: Maintaining regulatory compliance

For B2B use cases (customer service, support, sales), these features are non-negotiable. Large enterprises need to know who's talking to their AI agent, what's being said, and whether it's compliant with their policies.

Linq is building toward a complete platform where a developer can not only deploy an AI agent but also monitor it, improve it, and scale it.

Monetization: How Linq Makes Money and Why It Works

Linq's business model is something between API pricing and SaaS platform pricing. The exact details haven't been disclosed publicly, but we can infer from their behavior.

They're not charging per message like Twilio does for SMS. If they were, they'd be making approximately

But they're claiming

More likely, they're using a model like:

• Base seat or deployment fee: $500-2000/month per AI agent deployed
• Volume-based pricing: Scaling charges based on conversation volume
• Feature-based pricing: Premium features like advanced analytics or compliance require higher tiers

This model works better than per-message pricing because:

1. It's predictable: Customers know what they'll pay
2. It incentivizes usage: More messages doesn't mean higher costs (up to a point), so customers want high engagement
3. It aligns incentives: Linq's revenue grows as customers' businesses grow
4. It's defensible: Once a customer has integrated and is paying, they have switching costs

The 34% average account expansion suggests customers are moving up tiers, adding features, or deploying more agents. This is a healthy revenue expansion dynamic.

Their vision to expand beyond messaging to Slack, email, and other channels suggests they're thinking about becoming a platform where you pay for access to multiple channels, not just one. That's a much larger TAM and a stickier business model.

Linq's roadmap outlines a clear trajectory from immediate stabilization to long-term vision as a conversational AI 'operating system'. Estimated data based on public statements.

The Risks: What Could Go Wrong

For all of Linq's apparent success, there are real risks worth acknowledging.

Apple platform dependency: Linq's entire business is built on Apple allowing third parties to integrate with iMessage. Apple could change this at any time. They could decide conversational AI in iMessage is a strategic feature they want to control. Precedent exists: Meta has been limited by Apple's app tracking restrictions. Facebook can't do certain things on iOS that it can on Android.

Apple has shown they care about iMessage and see it as a key differentiator. If AI agents become essential to iMessage's value, Apple might decide to build this themselves rather than let a third party capture the economic value.

Global expansion complexity: iMessage is primarily used in the US. Globally, people use WhatsApp, WeChat, Telegram, Signal, and other platforms. Linq's vision to expand to these platforms is sound, but technically and operationally, each platform is a different challenge. WhatsApp has different authentication. WeChat has different user models. Telegram has different APIs. Building for 5-10 platforms simultaneously is ambitious.

Regulatory exposure: As Linq handles more messages, particularly for customer service and business communication, they're increasing their regulatory exposure. GDPR, HIPAA, SOX, and other frameworks may apply depending on use case. Compliance infrastructure is expensive.

Competition from incumbents: Twilio could acquire someone, build this themselves, or partner with Apple to be the exclusive API partner. Any of these moves could dramatically reduce Linq's opportunity.

AI agent commoditization: If AI agents become commodities (which they might, as large language models improve and become cheaper), the value capture might shift to the AI model provider rather than the infrastructure provider. Linq is betting that the infrastructure layer is valuable even if the AI layer is commoditized. They might be right, but it's not guaranteed.

Reliability and trust: If Linq has a major outage and thousands of businesses' customer service agents go down, trust breaks. As with any infrastructure platform, they're only as good as their last incident.

These risks are real. But they're risks that come with building infrastructure. You're not building a consumer app that can pivot quickly. You're building plumbing. Plumbing is less sexy but more valuable if you get it right.

Use Cases: Where AI Assistants in Messaging Shine

To understand why Linq's platform matters, it helps to think about real-world use cases.

Customer service: A travel company could deploy an AI agent that handles booking changes, refund requests, and itinerary questions entirely through iMessage. Customers don't need to call, wait on hold, or navigate a website. They just text.

E-commerce support: An online retailer could have an AI agent that checks order status, helps with returns, and processes exchanges through iMessage. When customers want to reach support, they don't open a new app. They open their iMessage thread with the retailer and ask.

Healthcare: A telemedicine company could use an AI agent to triage symptoms, schedule appointments, and provide follow-up care through SMS (with HIPAA compliance). Patients have a trusted thread with their provider.

Restaurant reservations and ordering: A restaurant group could have an AI agent that takes reservations, handles special requests, and answers menu questions through iMessage. No app to download. No separate website to navigate.

Financial services: Banks could deploy AI agents for balance inquiries, transaction questions, and fraud detection through SMS. Higher confidence than a traditional SMS since it uses iMessage encryption.

Ride-sharing and delivery: A ride-sharing or delivery company could have an AI agent that handles customer issues directly. Where's my driver? Can I modify my order? I want to report a problem. All through iMessage.

Scheduling and calendar management: The Poke example: an AI assistant that coordinates scheduling directly through iMessage means no context switching. No logging into a separate system. No new app.

The pattern across all of these is the same: tasks that require back-and-forth communication are handled more efficiently through a messaging interface than through an app or a phone call.

Potter's insight about app fatigue is valid. Most iPhone users have their home screen full. Adding another app is friction. But texting? Everyone texts. Everyone checks iMessage dozens of times per day. An AI assistant that lives there is just more convenient.

Linq's revenue model likely comprises base seat fees, volume-based pricing, and feature-based pricing, with base seat fees contributing the largest share. Estimated data.

The Path to IPO: What Would Justify the Valuation?

Linq just raised

For Linq to reach the $1B+ valuation that would lead to IPO, they need to:

**Achieve

Expand beyond iMessage: iMessage is US-focused. Global expansion to WhatsApp, Telegram, and other platforms would unlock 3-5x the TAM. This is critical for the long-term valuation story.

Build defensible moats: They need to make it hard for Twilio, Apple, or other incumbents to replicate. This likely means building such a deep integration with customer AI systems and such comprehensive feature parity that switching is painful.

Prove enterprise adoption: They need customers that are mission-critical, that have high switching costs, and that represent strategic partnerships. Poke was a great early validation, but they need more visible wins.

Demonstrate AI agent market adoption: Ultimately, Linq's growth is capped by the growth of AI agent usage itself. If AI agents never become mainstream (which seems unlikely but isn't impossible), Linq's growth caps too.

Assuming they execute on these fronts, the path to $1B+ valuation is clear. They'd be building the infrastructure layer for a market that's expanding rapidly.

Comparison point: Stripe went public at a ~

The Broader Trend: Messaging as the New App Platform

Linq isn't operating in isolation. There's a broader trend of messaging becoming a platform.

WeChat in China has been this for years. It's not just messaging—it's payments, shopping, fitness tracking, government services. Everything happens inside WeChat.

The West is just beginning to realize that messaging could be a platform.

Slack started as messaging but is now an application platform. You build apps and bots within Slack. Thousands of companies are doing this.

Discord is messaging with communities and applications built in.

Telegram has bots and channels and is clearly positioning itself as a mini app platform.

iMessage is the last holdout. It's been purely messaging for a decade. But Linq and others are starting to suggest that iMessage could be more.

What's interesting is that this trend mirrors earlier platform shifts:

• SMS became a platform for 2FA and transactional messages
• Email became a platform for receipts, notifications, and newsletters
• Apps became a platform for everything else

Now messaging is becoming a platform for real-time, conversational interactions. The question is whether iMessage specifically becomes a platform (and at what point does Apple decide to control it directly) or whether the platform is the set of messaging apps broadly.

Linq is betting on the latter: a unified platform for deploying conversational AI across all messaging channels.

What's Next: The Product Roadmap and Vision

Based on public statements from Linq's leadership, their roadmap is clear even if not all details are public.

Immediate focus: Stabilizing and scaling iMessage integration. Expanding to RCS and SMS in markets where they're dominant. Building out developer tooling and analytics.

Near-term (6-12 months): Expanding to Slack, email, and potentially other enterprise messaging platforms. Building advanced features like multi-agent conversations, sophisticated function calling, and compliance tooling.

Medium-term (1-2 years): Reaching feature parity across all major messaging platforms. Building AI-native features that leverage the capabilities of the underlying model providers. Expanding internationally to support WhatsApp, Telegram, WeChat, and others.

Long-term vision: Becoming the "operating system" for conversational AI, regardless of platform or channel. Think of it as a layer between AI models and messaging platforms.

The $20M they just raised will go toward hiring (engineering, go-to-market, support), developing this roadmap, and likely acquiring smaller companies or integrations that accelerate the roadmap.

Based on their growth rate (132% QoQ customer growth, 34% account expansion), they're on a trajectory to be a unicorn-track company. They'd need to maintain that growth, which becomes harder at scale, but it's possible if:

1. The AI agent market continues to grow (likely)
2. Messaging adoption continues (definitely happening)
3. They execute on their expansion plans (uncertain)

Lessons for Developers and Entrepreneurs

Linq's journey offers several lessons worth paying attention to.

Lesson 1: Follow where the market actually goes, not where you planned to go. Linq started as a digital business card. They pivoted to iMessage upgrading for B2B. They pivoted again to AI agents. Each pivot was forced by market signals, but they listened and moved.

Lesson 2: Sometimes the infrastructure is more valuable than the application. Linq isn't building AI assistants. They're building the plumbing that lets others build AI assistants. This is less visible but more defensible and more valuable.

Lesson 3: Find the distribution channel people are already using. iMessage, SMS, and RCS aren't new. But people were already checking them hundreds of times per day. That's distribution that money can't buy.

Lesson 4: Retention and expansion matter more than new customer acquisition. Linq's 295% NRR is more impressive than their 132% customer growth. It means they're scaling by expanding existing customers, not by constantly acquiring new ones. That's a healthier business.

Lesson 5: Platform shifts create opportunities for infrastructure. When developers move from building apps to building AI agents, new infrastructure is needed. Linq identified this shift early and positioned accordingly.

These lessons apply broadly to infrastructure companies. The most valuable companies often aren't the ones doing the most visible work. They're the ones building the invisible plumbing that everything else depends on.

Integration with AI Automation: Where Runable Fits

Linq is solving the distribution problem for AI agents. But once an AI agent is distributed, it still needs to be built and maintained efficiently.

This is where platforms like Runable become relevant. Runable offers AI-powered automation starting at $9/month, enabling teams to rapidly create presentations, documents, reports, and other outputs through AI agents.

Consider a scenario: you're building a customer service AI agent that helps with refunds and order changes. You want the agent to generate formatted receipts or refund confirmations. Instead of manually formatting documents, Runable's AI automation can generate these documents in seconds. You wire Linq for the messaging distribution and Runable for the document generation, and your agent handles the full workflow.

Or imagine building an internal operations AI assistant that drafts weekly reports. Rather than having the AI paste unformatted text into messages, Runable can generate properly formatted reports automatically. Your team messages the bot in Slack, and within seconds, a polished report is generated and shared.

Linq handles the where (messaging channels). Runable handles the what (content generation and automation). Together, they enable developers to build sophisticated AI-powered workflows that feel native to the tools people already use.

The Future of Customer Engagement: Messaging-First Strategy

We're at an inflection point. For 20 years, the strategy for customer engagement was "build an app or a website." It worked. Apps are how we access services now.

But that strategy is starting to invert. The winners in the next 20 years might be the ones who ask: "Why make customers come to us through an app? Why not go to them through the channels they're already using?"

Messaging is the most personal communication channel that exists. It's where you talk to people you care about. It's where you have your most important conversations. It's where you want to be.

Linq is betting that as AI assistants become more capable and more useful, the natural place for them is in that same personal channel. Not in a separate app. Not on a website. In your messages.

This is a messaging-first strategy, and it's going to reshape how businesses think about customer engagement.

Imagine in five years:

• Your bank doesn't have a banking app. It has an AI agent in your messages.
• Your restaurant doesn't have a website. It has an AI agent in your messages.
• Your doctor doesn't have a patient portal. They have an AI agent in your messages.

It sounds radical, but it's honestly less radical than the current state where you have to manage 50 apps for 50 services.

Linq is building the infrastructure that makes this possible. And with $20M in funding, they're just getting started.

Why This Matters Now: The Convergence of Trends

Linq's timing is perfect, and that's not an accident.

Three trends are converging:

AI agents are getting good enough: Two years ago, AI assistants were novelties. Today, they're useful. Tomorrow, they'll be expected. This is the right moment for infrastructure that makes deploying them cheap and easy.

App fatigue is real: Users have too many apps. They're not downloading more. Distribution through traditional app stores is becoming harder. Messaging distribution is the new frontier.

Messaging platforms are opening up: Apple, Google, and others are making it easier for third parties to integrate with messaging. The APIs that were locked down two years ago are now accessible. The timing is perfect for companies like Linq to move in.

These three trends together create a window of opportunity. It won't stay open forever. Incumbents will catch up. Regulations will increase. But right now, there's a genuine opportunity to reshape how people interact with AI.

Linq is positioned at the center of this shift. That's why the $20M raise is meaningful. It's not just capital—it's validation that the market sees what they see.

Closing: The Platform That Quietly Powers Your AI Interactions

In 10 years, you probably won't know Linq's name. You'll just know that when you text a business, an AI handles it. When you have a question, you ask your messaging app, and an AI answers. When you need customer service, you message from your current thread.

Linq will be invisible infrastructure, the way that DNS or SSL certificates are invisible infrastructure today. You don't think about them until they break.

But that invisibility is where the real value is. The companies that power entire industries without users knowing it are often the most valuable. Because they're not constrained by distribution challenges or user preferences. They're just efficient utility layers.

Linq is building that utility layer for the AI era. The $20M Series A validates that they're building something real. The metrics suggest they're building something that works. The market opportunity suggests they're building something that matters.

Watch this space. The quiet infrastructure revolutions are the ones that reshape everything.

FAQ

What is Linq and what problem does it solve?

Linq is an API platform that enables AI assistants to be delivered directly through messaging apps like iMessage, RCS, and SMS. The core problem it solves is distribution: instead of requiring users to download and open a new app to interact with an AI assistant, Linq lets those assistants live in the messaging apps people use constantly. This eliminates app friction while giving businesses an always-on communication channel with their customers.

How does Linq's technology actually work?

Linq's API sits between messaging platforms (Apple's iMessage, Google's RCS, SMS) and AI systems. When a user sends a message to an AI agent, Linq's infrastructure routes that message to the developer's AI backend, processes the response, and sends it back through the original messaging channel, maintaining full feature parity including rich media, encryption, and threading. The company handles authentication, routing, scale, and compliance, so developers can focus on building their AI agents rather than messaging infrastructure.

What are the key benefits of using Linq for developers?

Developers benefit from dramatically reduced go-to-market friction, as their AI agents are immediately available through channels people already use constantly, eliminating the need to build and distribute a separate app. Second, Linq handles all the complexity of integrating with Apple's proprietary iMessage protocol, Google's RCS standards, and SMS systems, which would otherwise require extensive platform-specific engineering. Third, the platform enables 34% average account expansion as customers deploy more agents and scale to new channels, creating a business model where revenue grows faster than customer acquisition requires.

Why did Linq pivot from B2B messaging to AI agents?

Linq initially built an API for businesses to send iMessage instead of SMS, which was working but limited. The catalyst came when Poke, an AI assistant, launched in September 2024 and went viral in part because it lived natively in iMessage. Suddenly, dozens of AI companies wanted to use Linq's infrastructure. Potter realized they had a choice: remain a spoke in the wheel serving B2B communication, or become the hub providing infrastructure for a rapidly expanding AI agent market. They chose the latter, and within 8 months had doubled their 4-year revenue.

What do the 295% NRR and zero churn metrics tell us about Linq's business?

These metrics indicate extraordinarily strong product-market fit and customer satisfaction. A 295% net revenue retention means that existing customers are expanding their spending at Linq at a rate of nearly 3x annually, which happens only when a product becomes increasingly central to customers' operations and when those customers see significant ROI from their investment. Zero churn suggests that once integrated, switching costs are high and customer satisfaction is sufficient that no one is leaving for competitors, which is rare for infrastructure companies.

How does Linq compete against larger platforms like Twilio?

Twilio built a $18B+ company on SMS infrastructure, but SMS is fundamentally limited for conversational AI use cases. Linq is purpose-built for conversational AI delivered through modern messaging protocols with rich features. Apple's Messages for Business exists but wasn't designed for AI agents or sophisticated multi-turn conversations. Linq's advantage is focus: they're not trying to be a general messaging platform, just the optimal infrastructure for deploying conversational AI through messaging channels. This focus creates defensibility even against larger competitors.

What are the main risks to Linq's business model?

The biggest risk is platform dependency: Linq's entire infrastructure is built on Apple allowing iMessage integrations, and Apple could change their policies at any time. Second, global expansion is complex because each messaging platform (WhatsApp, WeChat, Telegram) has different technical requirements and regulatory frameworks. Third, if AI agents become commoditized, the value capture might shift to AI model providers rather than infrastructure providers. Finally, incumbents like Twilio could move upmarket or Apple could build competitive features, reducing Linq's opportunity.

How is Linq positioned to scale beyond iMessage?

Linq's stated vision is to become the infrastructure layer for conversational AI across all channels: not just iMessage, RCS, and SMS, but Slack, email, Telegram, WhatsApp, and Discord. This multi-channel approach would dramatically expand their total addressable market. The capital from their $20M Series A is explicitly intended to fund this expansion, with the technical architecture designed to be channel-agnostic. This is critical because iMessage dominance in the US masks limited penetration in global markets where WhatsApp and WeChat are dominant.

What does Linq's growth trajectory suggest about the broader AI market?

Linq's 132% quarterly customer growth and 34% account expansion, achieved while already having product-market fit, suggests the market for AI agent infrastructure is not just real but accelerating. The fact that they've achieved zero churn despite competing against larger platforms suggests developers and businesses see lasting value in their approach. This indicates we're entering a phase where AI agent deployment becomes a fundamental business need, and infrastructure companies enabling that deployment will capture significant value.

How does Linq's $20M Series A compare to other infrastructure companies at similar stages?

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Key Takeaways

• Linq raised $20M Series A to distribute AI assistants through iMessage, RCS, and SMS, eliminating the need for separate apps
• The company achieved 295% NRR and zero churn by pivoting from B2B messaging to AI agent infrastructure after Poke's viral success
• Doubled 4-year revenue in just 8 months with 132% QoQ customer growth, indicating strong product-market fit in a rapidly expanding market
• Competing against larger incumbents like Twilio by focusing specifically on conversational AI rather than general messaging infrastructure
• Planning global expansion beyond iMessage to Slack, WhatsApp, Telegram, and other platforms to address international messaging fragmentation

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