DEV Community: Michael Esola

How to Actually Increase Revenue For Your Business with AI

Michael Esola — Tue, 06 Jan 2026 23:04:55 +0000

In the last 18 months, the idea of AI voice agents managing real interactions for businesses has gone from science fiction to reality. Thousands of companies, from SMBs to enterprises, are using voice AI to schedule appointments, complete bookings, run surveys, do intakes, and much more. These agents save costs for businesses, generate additional revenue, and free up human employees to do higher leverage-and more enjoyable-tasks.

But we’re still in the earliest innings. Most companies deploying voice AI today are in what an Andreessen Horowitz executive recently termed the “voice-as-a-wedge” phase-using it to automate one or two narrow call types as a point solution. The real opportunity lies ahead: voice agents that manage entire workflows, operate across multiple modalities, and eventually own full customer relationship cycles from first touch to long-term retention.

Why Voice, Why Now

The sudden viability of voice AI stems from a confluence of factors that all matured at roughly the same time.

First, the underlying models got dramatically better. The jump from GPT-3.5 to GPT-4-and the subsequent improvements across foundation model providers-meant that AI could finally handle the ambiguity, context-switching, and nuance that real conversations demand. A customer calling to reschedule an appointment might also mention a billing question, express frustration about a previous experience, and ask about a new service. Modern LLMs can navigate these shifts gracefully in ways that older NLU systems simply couldn’t.

Second, latency dropped to acceptable levels. Voice is uniquely intolerant of delay. Even a 500-millisecond pause feels unnatural. But optimizations in inference, streaming responses, and text-to-speech synthesis have brought round-trip latency down to the point where conversations feel genuinely fluid. This wasn’t true even two years ago.

Third, tool use and function calling became reliable. An agent that can only talk is of limited value. The real unlock comes when voice agents can take actions: check a calendar, update a CRM record, process a payment, send a follow-up email. The ability to call APIs and operate across systems transforms voice from a novelty into infrastructure.

Finally, businesses are ready. Labor costs continue to rise, hiring remains challenging in many sectors, and customer expectations for responsiveness have never been higher. The economic case for voice AI has become obvious to operators across industries. One example is the popular voice assistant Sloane which had explosive adoption by businesses in 2025.

Voice Solutions Today

Today, most voice AI deployments follow a familiar pattern. A company identifies a high-volume, relatively straightforward call type-appointment scheduling, order status inquiries, lead qualification, after-hours answering-and deploys an AI agent to handle it. This works remarkably well. Businesses report handling rates of 70–90% for these targeted use cases, with significant cost savings and improved customer satisfaction scores.

But this is just the entry point. The strategy makes sense for a few reasons: it’s easier to sell a specific solution to a specific pain point, it’s easier to build when the scope is constrained, and it’s easier to measure ROI when you’re replacing a clearly defined function.

The limitation is that point solutions capture only a fraction of the value that voice AI can ultimately deliver. A scheduling agent that can book appointments but can’t answer questions about services, handle complaints, or identify upsell opportunities is leaving enormous value on the table. It’s also creating friction-customers who call with complex needs still get routed to humans, or worse, get told the agent can’t help them.

The Evolution: From Calls to Workflows to Relationships

The next phase of voice AI will be defined by expansion along three dimensions: workflow depth, modal breadth, and relationship scope.

Workflow depth means handling not just the call itself, but the entire process around it. Consider a home services company. Today, a voice agent might schedule the appointment. But the full workflow includes confirming the appointment via text the day before, handling rescheduling requests, dispatching the technician, collecting payment, requesting a review, and following up on any issues. Each of these touchpoints is an opportunity for voice (or voice-adjacent) AI to add value. The companies that will win are those building agents that can orchestrate the entire workflow, not just answer the phone.

Modal breadth recognizes that voice is often just one channel in a broader interaction. A customer might start with a text message, escalate to a phone call, receive a follow-up email, and later chat through a web interface. Agents that can maintain context across these modalities-understanding that the person texting about their order is the same person who called yesterday-will provide dramatically better experiences than those siloed into a single channel.

Relationship scope is the most ambitious expansion. Rather than handling individual transactions, voice agents can begin to manage entire customer relationships. This means understanding a customer’s history, preferences, and patterns; proactively reaching out when appropriate; recognizing when a customer is at risk of churning; and building genuine rapport over repeated interactions. The agent becomes less like a call center rep handling tickets and more like an account manager nurturing relationships.

The Integration Imperative

None of this is possible without deep integration into business systems. An agent that can only access limited context will provide limited value. But an agent with access to the CRM, billing system, inventory database, scheduling platform, and communications tools can do genuinely useful things.

This is where the current landscape gets interesting. The first wave of voice AI companies largely built around proprietary models and closed systems. The next wave will be defined by integration breadth-how easily can an agent plug into Salesforce, HubSpot, Square, Toast, ServiceTitan, or whatever vertical-specific systems a business runs on?

The companies that build the most robust integration layers will have a significant moat. Not because integrations are technically difficult (though they can be), but because they require understanding the specific workflows and data models of each business system. This is unsexy, grind-it-out work that compounds over time.

Vertical vs. Horizontal

A natural question is whether voice AI will be won by vertical specialists or horizontal platforms. I suspect the answer is both, but with vertical players having an advantage in the near term.

The reason is that voice interactions are deeply domain-specific. The way a dental office handles appointment calls is different from how a property management company handles maintenance requests, which is different from how an e-commerce company handles order inquiries. The scripts, the integrations, the edge cases, the compliance requirements-all of these vary by vertical.

Vertical specialists can build these domain-specific capabilities from day one. They can speak the language of their customers, understand their workflows, and build integrations with the systems those customers actually use. This creates faster time-to-value and higher customer satisfaction.

Over time, horizontal platforms may emerge that are flexible enough to serve multiple verticals well. But I expect this will happen through a “vertical then horizontal” playbook rather than trying to be everything to everyone from the start.

What To Look For

Given this thesis, here’s what excites me in the space:

Companies moving beyond the today’s configurations. I want to see founders who are thinking about the full workflow and the full relationship, not just the initial call type that gets them in the door. This doesn’t mean they have to build it all at once-in fact, they shouldn’t-but they should have a clear vision for expansion and a product architecture that supports it.

Deep vertical expertise. The best founders in this space have either operated in their target vertical or have spent significant time understanding it. They know the specific pain points, the existing systems, and the buying process. They’re not building generic voice AI and hoping to find a market.

Integration-first architecture. The technical approach matters. Companies that treat integrations as an afterthought will struggle to deliver the contextual, workflow-spanning experiences that create real value. I look for teams that are building integration as a core competency from the beginning.

Thoughtful approaches to trust. Voice AI is inherently more intimate than text-based AI. Customers expect that when they speak to a business, they’re being heard and understood. Agents that feel robotic, that make obvious errors, or that can’t handle reasonable edge cases will damage brand relationships. The bar for quality is higher in voice than in any other AI modality.

The Opportunity Ahead

We’re at a fascinating moment. The technology has crossed the threshold of viability, early adopters have proven the model works, and the broader market is just beginning to understand what’s possible. As the underlying models continue to improve-and agents can now call tools and operate across systems-there’s no reason why every company shouldn’t have voice-first AI products running and optimizing critical parts of their business.

The companies that will define this space are being built right now. Some of them will start as point solutions and expand into platforms. Others will go deep in a specific vertical and become the default infrastructure for that industry. A few will figure out how to build horizontal capabilities that genuinely work across contexts.

What unites them is a recognition that voice AI isn’t just about answering phones more cheaply. It’s about reimagining how businesses interact with their customers-making those interactions more responsive, more personalized, and more valuable for everyone involved.

95% of Companies Are Lighting Their AI Budgets on Fire

Michael Esola — Tue, 06 Jan 2026 21:59:23 +0000

Somewhere between $30 and $40 billion has been poured into enterprise AI over the past two years. That’s billion, with a B. CEOs have stood on earnings calls promising “transformational AI capabilities.” LinkedIn is drowning in posts about prompt engineering and “AI-first cultures.” Every tech vendor on the planet has slapped “AI-powered” onto their product pages like it’s a magic revenue incantation.

And what do most companies have to show for it? According to a recent state-of-AI report, approximately 95% of organizations investing in generative AI are getting zero measurable return. Not disappointing returns. Not below-expectations returns. Zero.

Let that sink in. Nearly every company that rushed to integrate ChatGPT, spin up internal LLMs, or build AI copilots into their workflows is sitting on the same P&L impact they had before they started writing checks. Meanwhile, the remaining 5%-a mix of enterprises, mid-market players, and the occasional scrappy startup-are extracting millions in actual value.

Someone coined this as the GenAI Divide. Apparently, if you’re on the wrong side of it, no amount of “prompt optimization workshops” is going to save you.

Chatbot Graveyard

Most generative AI deployments have been solutions looking for problems. Don’t agree with this assessment?

Companies built chatbots that nobody uses. They created internal knowledge assistants that employees bypass in favor of just asking their coworkers. They automated content generation that still requires so much human editing it would have been faster to write from scratch. The technology works. The use cases don’t.

The 5% succeeding aren’t smarter or luckier. They identified workflows where AI solves a genuine operational bottleneck-something that was expensive, time-consuming, and didn’t require the kind of nuanced human judgment that makes AI implementations fall apart. They found the unglamorous, high-volume, process-heavy work that actually moves the needle.

Which brings us to a technology that’s quietly eating the enterprise while everyone else argues about which foundation model is best.

Voice AI: An Infrastructure Layer with Value

While the business world obsessed over text-based AI, voice AI agents have undergone a transformation that many have completely missed.

If your mental model of automated phone systems is still “Press 1 for Sales, Press 2 for Support,” you’re about three years behind. The modern voice AI stack has moved so far beyond IVR 2.0 that calling it the same category is almost misleading. We’re talking about fully autonomous conversational endpoints that can handle complex, multi-part customer interactions without human intervention.

I’ve spent the last 6 months testing multiple voice AI agent platforms, and the technology has quietly reached a tipping point. The improvements aren’t incremental. They’re categorical.

The real breakthrough isn’t text-to-speech quality or transcription accuracy-though both have improved dramatically. It’s the orchestration layer. Modern voice AI agents can parse multi-intent queries in real time by running parallel LLM chains. A customer can say, “I need to update my address, check on my order status, and actually, can you also cancel the subscription I set up last month?”-and the system handles all three without breaking a sweat or losing track of the conversation.

They maintain conversational state across long calls without context collapse. They trigger API workflows mid-conversation-updating CRMs, creating tickets, validating leads, running OTP checks-while still talking to the customer. They adjust latency dynamically with on-device caching and streaming inference, which means the awkward pauses that made older systems feel robotic are disappearing.

The Secret Sauce Nobody Talks About

Here’s what most people underestimate: true full-duplex audio.

Human conversations aren’t turn-based. We overlap. We interrupt. We say “uh-huh” and “right” while the other person is still talking. We cut each other off when we already understand the point. Traditional voice systems couldn’t handle this-they waited for silence, then responded. It felt mechanical because it was mechanical.

The ability to overlap listening and speaking makes modern voice AI feel genuinely human-grade. More importantly, it reduces average call times by 20 to 40 percent. That’s not a UX improvement. That’s a direct cost reduction that shows up on the P&L in month one.

Adaptive interruption handling is basically the secret sauce for natural conversational UX. When a customer interrupts mid-sentence, the AI needs to recognize what’s happening, gracefully abandon its current response, and pivot to address whatever the customer actually wants to talk about. Get this wrong and you have a frustrating experience. Get it right and customers forget they’re not talking to a human.

Companies like Sloane have built their entire model around this capability-AI phone assistants that handle inbound and outbound calls for businesses without the uncanny valley problem that plagued earlier generations of voice automation. It’s the kind of focused, workflow-specific AI deployment that actually generates returns.

Where This Is Headed

Voice AI agents are becoming an infrastructure layer, not a feature. Within the next 18 months, the question won’t be whether your business uses AI phone systems-it will be whether you’re using first-generation technology while your competitors deploy agents that can handle 80% of call volume autonomously.

The GenAI Divide isn’t really about who spent more money or who has the best data scientists. It’s about who identified real operational problems and deployed AI against them versus who bought into the hype and built impressive demos that don’t move business metrics.

Text-based generative AI will eventually find its footing. The use cases will mature. The implementations will improve. But right now, today, if you’re looking for AI that actually shows up in your financial statements, voice is where the smart money is going.

95% lit their budgets on fire to chase chatbots while the remaining 5% automated their phones.

What Burnout Taught Me About My Post-Hollywood Phone Problem

Michael Esola — Tue, 02 Dec 2025 18:03:45 +0000

When I was a motion picture literary agent in Beverly Hills, I averaged two hundred calls or more a day. My assistant fielded each one, booked meetings, and was the traffic controller keeping the planes from colliding. In 2015, it seemed incredibly efficient. Today, not so much.

After I stepped out of the corporate airlock and into the vacuum of entrepreneurship, hiring a salaried personal assistant didn’t make sense economically. I experimented with part-time contractors and outsourced call services like Ruby Receptionists. In every scenario, the experience was subpar at best. Before too long, I found myself trying to migrate all my comms to text and email, which worked for some callers but wasn’t appropriate for others.

Then came the challenge of finding assistants I could actually trust. If you run a business in California, you live the nightmare of navigating a decade-long explosion of upside-down labor law. Some of these regulations were absolutely necessary and genuinely reflect why California leads the country in employee protection. Many others, however, made the ayatollah of Iran look like a hall monitor in comparison. As a small business owner, employing in-house assistants became a minefield.

The Long Game

Like every founder, you fake it until you make it and fill in the gaps whenever you’re not passed out from exhaustion. I kept telling myself that if margins increased 10% or if revenue stayed consistent enough, I’d hire the right person eventually. Guess what? Startup wizardry and financial consistency operate in parallel universes that never intersect. Then for a while I thought I’d just overpay for someone great. With enough experience, you realize the high-paid executive assistant isn’t all that different from the entry-level version.

By my third startup, I thought I was doing everything right. Calendly for scheduling, Gusto for HR and private recruiting firms for new hires. And I disciplined myself to follow every voicemail with a quick email explaining the reason for my call. Before long, I changed that to never leaving a voicemail at all.

But incoming calls still remained a nightmare, and burnout reared its ugly head. I went through my esoteric phase: why do I even need to answer? Looking back, ignoring my phone for three months was one of the most liberating things I ever did. It only made sense to drop social media too. Expunging a fifteen-plus-year digital footprint no doubt put years back on my life-but was this too extreme? Would I buy stamps and mail letters too?

Telephonic Hijacking

As a business owner, you’ve likely started using your personal cell phone for calls and texts. That means your personal number, name, and other information get saved to hundreds of databases around the world. Before long, you’re receiving thirty calls a day from random salespeople, surveys, and scammers. This is still vivid for me. Every night, I had to delete all my voicemails because they kept maxing out my iPhone.

I tried everything to get people to stop calling me. I paid services to delist me from databases. I turned my phone to silent all the time. Some days I just gave up and let my voicemail max out to stop the calls. But nothing worked. I felt powerless. The only option I thought I had left was to ditch my personal number altogether and start clean. But I came to my senses when I realized my phone number and identity are linked. This isn’t up for debate since I’m a Zillennial. I grew up in an era where you actually memorized someone’s phone number, every digit, because your brain was the original Contacts app. This concept is so foreign today it might as well be a lost ritual from a vanished civilization.

Data Broker Fiasco

Over the last three years, data brokers around the world have proliferated at a rate never seen before. AI call-factory innovation dramatically reduced call-origination costs internationally. Routing systems that support spam calls at scale once required custom infrastructure, but today these systems can be purchased off the shelf for pennies. Foreign companies exploit loopholes in antiquated state and federal law. The FCC is losing its whack-a-mole war as the cost of launching new spammer farms become negligible. Crypto-funded ops have only made this worse.

The result? A perfect storm. Legitimate business owners like you and me end up paying the price for a global spam-industrial complex we never signed up for. Your number gets scraped, sold, repackaged, and resold like a knockoff handbag in a back-alley market. And every time a new spammer farm spins up in some overseas data center, your phone becomes their favorite chew toy. It’s exhausting. You can’t block the calls fast enough, and even when you do, ten more pop up hydra-style to take their place. At some point, you stop feeling annoyed and start feeling hunted by your own phone.

Meanwhile, you’re running a business. You can’t ignore the phone entirely because somewhere in that mess is an existing client. No problem. That’s what caller ID is for. But what if it’s a new customer? That means screening with voicemail is no longer an option. This became miserable purgatory: half gatekeeper, half spam bouncer. What an incredible waste of time! Why is this happening?

Why I Created Sloane

After I accepted defeat in my cage match with robocallers, I realized I needed to stop punishing myself simply because I was a small business. After all, small businesses make up 99.9% of all businesses in the United States (yes, that’s actually true). A small business shouldn’t have to pay for call services or expensive staff to handle phones! For lack of a better word, it’s just stupid.

What if I could use AI to make a reliable, easy-to-use call assistant that was actually good? What if I could create a receptionist that answered instantly, sounded human, had flawless memory, understood context, booked appointments, screened out junk, handled FAQs, and didn’t require salaries, benefits, HR, compliance, or a California labor lawyer on speed dial?

So, I started building Sloane. Not as a gimmick. Not as another AI toy. But as a necessary correction to a broken system every small business simply tolerates because they think they have no alternative.

I wasn’t a coder. But vibe coding was a thing, so I tried that. That got me a prototype. Not good enough, so I taught myself how to code. Days became weeks. Weeks became months. While testing early prototypes of Sloane, the results were promising. Calls were being answered instantly. Customers were actually thanking “her” for being so helpful. Appointment bookings increased because people no longer landed in my voicemail canyon. Spam calls were quietly filtered out without ever touching my phone. And for the first time in my career, my phone wasn’t an anxiety machine.

10 users became 100 users… which became 500 users… then 1,000 users. Wait, what? Businesses were actually using it! Were they faced with the same problem?

Incoming calls no longer felt like hostile missiles on a radar screen. Space opened up again: mental, emotional, and entrepreneurial. When small fires popped up, the bandwidth was finally there to put them out. My telephonic umbilical was severed. Lunches became peaceful instead of filled with the dreadful sense that something was slipping through the cracks. Suddenly, I had an assistant again. I once was lost but now I am found

Customers Actually Loved It

And here’s the part nobody expected: customers preferred it. I ran surveys. I listened to call transcripts. We watched behavior trends. Across the board, customers engaged. They asked questions. They were thankful when they received information. They treated her as if she was an assistant from my bygone Hollywood era.

We’re trained to believe the “human touch” is always superior. But that’s only true when the human is at their best. Most receptionists aren’t at their best 24/7-no one is. AI doesn’t get tired, hungry, annoyed, stressed, flustered, or overwhelmed. It doesn’t have off days. And it doesn’t accidentally hang up on people.

So the stigma disappeared. Customers didn’t care that Sloane was AI. They cared that their problem was solved. It turns out that a virtual receptionist that provides the same or more value than a human is all the same to a caller.

The Future

AI doesn’t replace people. It replaces the parts of running a business that nobody should be doing.

Screening calls… repeating FAQs… booking appointments… filtering spam… taking messages… asking basic qualification questions… coordinating schedules. Nothing about these tasks requires a human being. They require accuracy, speed, memory, and flawless repetition. Does AI make mistakes? All the time. But so do humans.

Stop settling for the old way of doing things. Stop paying for employees or call services. And stop answering your own phone! This is the way.

Sloane isn’t here to eliminate jobs. She’s here to eliminate friction. I wish I had this five years ago. Phone calls seem like the last frontier of business modernization. Everything else has evolved: payments, marketing, sales, scheduling, customer communication, analytics, payroll, logistics. But the phone? It’s stayed exactly the same until now.

If email had Mailchimp, if support had Zendesk, if websites had Shopify, if scheduling had Calendly… phone calls now have Sloane.

📞 I'm Not a Coder but Used Claude to Build a Free AI Answering Service

Michael Esola — Tue, 04 Nov 2025 05:35:21 +0000

Try Sloane free

I'm not a coder. Zero experience. Claude taught me everything - it was fun. I learned a ton. Never expected to get any customers. But I just crossed 500 users yesterday. Just as email is free, AI phone systems should also be free. Every business deserves to have its own free AI phone system. Businesses can customize their voice through 960 permutations when choosing from 20 different languages, 8 different voice styles and 6 different voice tones.

⚙️ The Tech Stack

I wanted this to be fast, scalable, and genuinely free to use. Here's what powers it:

Frontend (Dual Architecture)

Next.js 14 with SSR for the public marketing site (SEO matters!)
React 18 SPA for the authenticated dashboard (snappy UX)
Material-UI + TailwindCSS + Framer Motion for that modern feel
Deployed on Netlify with edge functions

Backend

Python Flask running on Google App Engine (auto-scaling FTW)
MongoDB Atlas for data persistence
45+ API endpoints organized by domain
JWT auth with refresh tokens + Google OAuth

AI Pipeline

Azure OpenAI GPT-4 for natural language understanding
Google Cloud Speech-to-Text (350ms latency!)
Azure Neural TTS with streaming support
Custom business-specific training for each client

Telephony

Asterisk PBX server on Google Compute Engine
Telnyx SIP trunks for phone number provisioning
Custom AGI scripts in Python for call logic
Sub-250ms response time from speech to AI reply

🧠 How It Actually Works

When a call comes in, here's what happens in under 250 milliseconds:

Call hits Asterisk → AGI script triggers
Business context fetch → Optimized MongoDB lookup with phone number mapping
Speech-to-text → Google Cloud converts audio to text
AI processing → GPT-4 understands intent with business-specific knowledge
Text-to-speech → Azure Neural TTS streams natural response
Conversation flows → Context retained throughout the entire call

The magic is in the optimization. That <50ms AGI context API? Critical. MongoDB projections, connection pooling, lazy loading for non-critical data – every millisecond matters when you're trying to sound human.

🎨 The Onboarding Experience

One of my favorite parts is the 3-minute onboarding. Seriously, three minutes from signup to taking calls:

Step 0: Connect Your Business

Paste your website URL → AI scrapes and extracts everything
Or use Google Business Profile integration
Or manually fill in details (but why would you?)

Step 1: Customize Your AI

Preview your AI greeting with live audio generation
960 voice permutations (20 languages × 8 styles × 6 tones)
Real-time audio player to hear how you'll sound

Step 2: Create Account

Email/password or Google OAuth
Email verification with pretty dialog
Automatic phone number provisioning via Telnyx

Done. You're taking calls.

📊 The Dashboard

I built a consolidated dashboard because I hate clicking through tabs:

Live call feed with real-time status updates
Full transcripts with searchable text
Call recordings with inline audio players
Analytics showing call volume, duration, outcomes
Business settings for hours, services, transfers
AI training interface for teaching your bot

WebSocket integration keeps everything updating in real-time without refreshing.

🔧 The Architecture Decisions

Why dual frontends?
SEO matters for acquisition, but a rich dashboard needs client-side routing. Next.js SSR for marketing, React SPA for the app. Cross-domain auth via localStorage tokens keeps users logged in across both.

Why MongoDB?
Fast lookups with phone number indexing, flexible schema for diverse business types, and MongoDB Atlas auto-scales better than I ever could manually.

Why persistent connection pooling?
This was huge. Initially, I was closing connections on teardown (standard practice). But with high call volume, I was getting "Cannot use MongoClient after close" errors. The fix? Never close the connection. Global client, 100 connection pool. Problem solved.

Why Flask over FastAPI?
Hot take: Flask's maturity won out. I needed battle-tested deployment on App Engine, not bleeding-edge async (calls are inherently synchronous anyway).

🪄 The Result

When someone calls a business using Sloane, they hear:

"Hi, thank you for calling [Business Name]! This is Sloane, how can I help you today?"

Natural. Professional. Available 24/7.

The AI can:

Answer questions about services, hours, location
Book appointments and send calendar confirmations
Take detailed messages with follow-up emails
Transfer calls to humans based on keywords
Text customers booking links, forms, or info
Screen spam and obvious sales calls

And businesses get:

A FREE professional phone number
Full call transcripts and recordings
Real-time notifications via email/SMS
Analytics and lead capture
All for $0 on the Starter plan

🚀 What I Learned

Latency is everything in conversational AI. That 250ms response time took weeks to optimize. Every database query, every API call, every speech processing step needed scrutiny.

The freemium model actually works. 90% gross margins + viral growth = sustainable. Businesses upgrade when they need more minutes or advanced features.

Dual frontends are worth the complexity. Next.js gives me SEO juice I could never get with a SPA. The React dashboard gives users the snappy experience they expect. Having both is powerful.

MongoDB projections are criminally underused. Don't fetch full documents when you only need 3 fields. That AGI context endpoint went from 200ms to 40ms just by being specific about what data to return.

Build for scale from day one. Auto-scaling on App Engine, connection pooling, optimized indexes, CDN caching – these weren't premature optimizations. They were table stakes.

💰 The Business Model

Freemium tiers:

Starter: FREE (25 min/month)
Essentials: $19/mo (100 min)
Pro: $49/mo (250 min)
Growth: $99/mo (500 min)

Compare to competitors charging $49-99/mo for just the AI, $29-97/mo for the phone number, and $24-49/mo for recording. We give it all away free.

The bet? Get businesses hooked on the value, let them grow, and they'll upgrade naturally. So far, it's working.

🔮 What's Next

Currently scaling to support 1000+ concurrent calls. Roadmap includes:

Multi-language support (Spanish, French)
SMS/WhatsApp integration
Advanced workflow automation
CRM integrations (Salesforce, HubSpot)
White-label offerings for agencies

The goal is to make professional AI phone systems accessible to every business, regardless of size or budget.

🎯 The Takeaway

Building Sloane taught me that solving real problems with AI doesn't require massive ML teams or millions in funding. It requires:

Deep understanding of the problem space
Obsessive attention to latency and UX
Smart use of existing tools (GPT-4, Azure TTS, etc.)
Business model that aligns with user value
Willingness to iterate based on real usage

The most rewarding part? Seeing small businesses actually use it. Dentist offices, law firms, home service companies – they're taking real calls, booking real appointments, and never missing another customer.

If you're thinking about building something with AI, don't wait for the perfect idea or the perfect stack. Pick a real problem, ship fast, and iterate. The best time to build was yesterday. The second best time is now.

Try Sloane free
How to post feedback: support@hi-sloane.com