Voice Technology
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What is telephony? Learn how the PSTN works, what SIP transfers do, and how modern voice AI platforms connect to the phone network in 2025.
Saurabh Jain
CMS article
What Is Telephony and Why the PSTN Still Runs the World's Phone Calls
Pick up any phone, tap a contact, and within milliseconds your voice is converted into electrical or digital signals, routed through a chain of switches, and delivered to someone thousands of miles away. That entire infrastructure has a name: telephony. And the backbone that has carried the world's phone calls for more than a century is the Public Switched Telephone Network, commonly abbreviated as the PSTN.
Understanding what telephony is, and how the PSTN actually works, matters more right now than it did five years ago. Businesses deploying AI voice agents, contact center leaders modernizing their stacks, and operations teams evaluating platforms like Feather AI all need a clear mental model of how calls originate, how they travel, and where modern voice AI slots into the existing infrastructure. Without that foundation, vendor claims about "seamless integration" or "real-time AI calls" can sound interchangeable even when the underlying architectures are completely different.
Telephony Defined
Telephony is the technology and set of protocols that enable voice communication over a distance using electronic transmission systems. The term covers everything from the original analog telephone lines of the 1870s to today's cloud-based VoIP platforms and AI-powered voice agents. At its broadest, telephony is any system designed to transmit speech, or data that represents speech, between two or more endpoints.
The word itself comes from the Greek roots for "far" and "voice," which is a fitting description of what every telephone system has always tried to do: make a distant voice feel present.
Modern telephony breaks down into three main categories:
Circuit-switched telephony (the traditional PSTN model, where a dedicated physical circuit is reserved for the duration of your call)
Packet-switched telephony (VoIP and SIP-based systems, where voice is digitized, chopped into packets, and reassembled at the other end)
Hybrid telephony (what most enterprises actually use today, a mix of legacy PSTN trunks and modern SIP/VoIP infrastructure)
The PSTN: 150 Years of Infrastructure You Rely On Daily
The Public Switched Telephone Network is the aggregate of the world's circuit-switched telephone networks. It includes the copper wire local loops running into homes and businesses, the fiber backbone that connects central offices, the microwave and satellite links bridging continents, and the switching equipment that routes calls from one endpoint to another.
The PSTN was built incrementally over more than 150 years. Early telephone exchanges in the 1880s used human operators who physically patched cables together to connect callers. Electromechanical switching arrived in the early twentieth century, followed by digital switches in the 1980s that converted analog voice signals to digital streams (using a process called Pulse Code Modulation, or PCM) for long-distance transport before converting them back to analog at the receiving end.
By the time the internet became commercially widespread in the 1990s, the PSTN was already a mature, global, highly reliable network. Regulatory bodies in virtually every country had classified it as critical infrastructure, which is why the PSTN's core architecture is governed by international standards bodies like the ITU (International Telecommunication Union) and, in the United States, overseen in part by the FCC.
How a PSTN Call Actually Travels
When you dial a number on a traditional PSTN line, here is what happens at the technical level:
Your handset converts sound waves into analog electrical signals.
Those signals travel over the local loop (the pair of copper wires connecting your premises to the nearest telephone exchange, also called a central office in North America or a local exchange in the UK).
At the central office, your analog signal is converted to a digital stream (64 kbps per channel, using the G.711 codec standard on most PSTN infrastructure).
The digital stream is routed through a hierarchy of switches and trunks to reach the central office nearest the called party.
The called party's phone rings, they answer, and a dedicated circuit is held open between the two endpoints for the entire duration of the call. No other traffic shares that circuit.
When either party hangs up, the circuit is released and the bandwidth becomes available for other calls.
This circuit-switched model is predictable and reliable. Latency is low and consistent because there is no queuing of packets. But it is also expensive and inflexible. Dedicating a full 64 kbps circuit per call, whether the conversation is active or full of silence, wastes capacity. And because physical circuits are finite, the number of simultaneous calls is physically constrained.
Why the PSTN Is Not Going Away (But Is Quietly Changing)
There is a narrative in technology circles that the PSTN is obsolete, that VoIP and internet telephony have replaced it. The reality is more nuanced. In the United States, the FCC has been managing what it calls PSTN transition, the gradual retirement of copper-based local loops and their replacement with IP-based infrastructure. Major carriers like AT&T and Verizon have been petitioning to discontinue legacy copper service in many markets, and the FCC has granted a growing number of those petitions.
But "retiring the copper loop" is not the same as eliminating the PSTN as a concept. The phone number system (the NANP, North American Numbering Plan, and its international equivalents under ITU E.164 standards), the call routing logic, the regulatory obligations around emergency services (911/112), and the interconnection agreements between carriers all persist. They have simply migrated from analog and TDM (Time Division Multiplexing) infrastructure to IP-based equivalents.
What this means practically: when a customer dials your business's phone number today, that call almost certainly traverses a mix of IP and legacy infrastructure before it reaches your contact center or AI voice agent. The PSTN, in its modernized IP form, remains the connective tissue of global voice communication.
From PSTN to Contact Center: Where Voice Enters Your Business
For businesses deploying any kind of phone-based customer interaction, the PSTN is the entry point. Calls arrive via a trunk, which is a group of telephone lines shared between your carrier and your premises. Historically, those trunks were physical (T1, T3, or ISDN PRI lines). Today, most businesses have migrated to SIP trunks (covered in detail in the next section), which carry calls over the internet using the Session Initiation Protocol.
Once a call crosses from the carrier's network into your environment, it hits your telephony platform: a PBX (Private Branch Exchange), a cloud contact center platform, or increasingly, an AI voice agent infrastructure layer like Feather AI. The telephony platform is responsible for answering the call, routing it to the right destination, and managing what happens during the call.
This is the layer where AI voice agents operate. They do not replace the PSTN. They sit inside the telephony infrastructure, receive calls that have already been routed through the public network, and handle the conversation from there. Understanding this distinction is essential for any operations or technology leader evaluating voice AI platforms. The question is never "does this AI replace the phone network" but rather "how does this AI connect to the phone network, and how reliably does it do so."
Why This Matters for Operations and Revenue Leaders
For a Head of Revenue or VP of Operations at a financial services firm, an insurance company, or a healthcare organization, telephony is not an academic topic. Your customers call you. Millions of those calls travel through PSTN infrastructure every day. The reliability, latency, and compliance properties of that infrastructure directly affect call quality, customer experience, and regulatory exposure.
When a vendor promises that their AI voice agent handles calls "in the cloud," what they are describing is an application that still depends on the PSTN (or its IP-based successor) as the delivery mechanism. The quality of that last mile, the codec negotiation, the latency budget, the failover behavior when a trunk goes down: these are telephony questions, not just AI questions.
That is why any serious evaluation of a voice AI platform has to start with a clear understanding of how that platform connects to the phone network. SIP trunking, codec compatibility, number porting, carrier redundancy: these details determine whether your AI voice agent delivers production-grade call quality or a frustrating experience that erodes customer trust.
How SIP Works, What a SIP Transfer Is, and How Voice AI Connects to the Phone Network
The transition from circuit-switched PSTN infrastructure to modern AI-powered voice operations runs almost entirely through one protocol: SIP, the Session Initiation Protocol. If the PSTN is the highway system that moves voice calls around the world, SIP is the on-ramp that lets modern software-based systems enter and exit that highway.
Understanding SIP, and specifically understanding what a SIP transfer is and how it works, closes the gap between the abstract promise of AI voice agents and the concrete mechanics of how they actually handle your customers' calls.
What Is SIP?
SIP (Session Initiation Protocol) is an application-layer signaling protocol defined in RFC 3261. It is used to initiate, maintain, and terminate real-time communication sessions, primarily voice and video calls, over IP networks. SIP handles the "control plane" of a call: it says "I want to call this number," "the called party is ringing," "the call is now connected," and "the call has ended."
SIP does not carry the actual voice audio. That job belongs to RTP (Real-time Transport Protocol), which streams the media between endpoints once SIP has established the session. Think of SIP as the reservation system at a restaurant and RTP as the food delivery itself.
Key SIP concepts every telephony buyer should know:
SIP trunk: A virtual bundle of telephone lines delivered over an internet connection, replacing physical T1/PRI lines. A SIP trunk connects your business's telephony platform to a carrier's network, and from there to the PSTN. SIP trunks are sold in channels (each channel handles one simultaneous call).
SIP endpoint: Any device or software application that can send and receive SIP messages. This includes IP desk phones, softphone apps, contact center platforms, and AI voice agent infrastructure.
SIP proxy / SIP server: The intermediary infrastructure that routes SIP messages between endpoints, similar to the role a DNS server plays for web traffic.
Codec: The algorithm used to compress and decompress voice audio for transmission. Common codecs include G.711 (uncompressed, highest quality, standard on PSTN), G.729 (compressed, lower bandwidth), and OPUS (the codec increasingly preferred for AI voice applications because of its adaptability and low latency).
What Is a SIP Transfer?
A SIP transfer is the process of moving an active call from one SIP endpoint to another. In a contact center context, this is how a call gets handed from an AI voice agent to a human agent, or from one department to another, without forcing the caller to hang up and dial a new number.
There are two primary types of SIP transfer:
Blind transfer (also called unattended transfer): The originating endpoint transfers the call to a new destination without first checking whether that destination is available. The caller may hear ringing or hold music. If the destination does not answer, the call typically fails. Blind transfers are simple and low-latency, but they offer no quality assurance.
Warm transfer (also called attended or consultative transfer): The originating endpoint first establishes a connection with the transfer destination, confirms the destination is ready and has relevant context about the call, and then bridges the caller through. The caller may hear hold music briefly while the handoff is coordinated. Warm transfers are more complex to implement in SIP terms (they require a third-party call control flow), but they are far superior from a customer experience standpoint.
This distinction matters enormously in AI voice agent deployments. A platform that can only execute blind SIP transfers puts callers at risk of dropped handoffs and forces human agents to re-ask questions the AI already answered. A platform that executes warm transfers with full context attached creates a seamless experience where the human agent already knows who they are talking to and why.
SIP Transfers in the AI Voice Agent Context
When an AI voice agent handles a call, the SIP architecture typically looks like this:
The caller dials a phone number.
The PSTN routes the call to a SIP trunk associated with the AI platform.
The AI platform's SIP endpoint answers the call and begins the conversation.
The AI qualifies the caller, answers questions, or completes a workflow.
At a decision point (for example, the caller qualifies as a hot lead or needs a specialist), the AI initiates a SIP transfer to a human agent or another system.
The quality of that transfer step is one of the clearest differentiators between AI voice platforms. A warm transfer with context attached requires the platform to not just execute the SIP mechanics correctly, but to also pass a structured summary of the conversation to the receiving agent in real time. This is a software-level capability layered on top of the SIP infrastructure.
VoIP vs. PSTN vs. SIP Trunking: Clearing Up the Terminology
These three terms are frequently conflated, and the confusion causes real problems when evaluating telephony platforms.
VoIP (Voice over Internet Protocol) is the broad category of any voice communication that travels over IP networks rather than dedicated circuit-switched lines. VoIP encompasses SIP, WebRTC, proprietary protocols like Cisco SCCP, and more. All SIP calls are VoIP, but not all VoIP calls use SIP.
PSTN is the global telephone network infrastructure described in the previous section. It is the destination that most calls ultimately reach, even if they start their journey over VoIP.
SIP trunking is specifically the service of connecting a business's on-premise or cloud telephony system to the PSTN using SIP over the internet, instead of physical telephone lines. SIP trunking providers include carriers like Twilio, Bandwidth, Vonage (now part of Ericsson), and Lumen.
For an AI voice platform, the typical connectivity chain is: caller on PSTN > SIP trunk provider > AI platform's SIP endpoint > AI conversation logic > SIP transfer back to human agent or CRM workflow.
How AI Voice Platforms Differ in Their Telephony Architecture
Not all AI voice platforms connect to the phone network the same way. The architectural choices made here have significant implications for call quality, compliance, and operational reliability.
Developer-first platforms like Vapi give engineering teams direct access to the underlying telephony stack. You can bring your own SIP trunk, configure your own codec preferences, and route calls however your infrastructure requires. This is powerful and flexible, but it requires significant engineering investment to get right. The telephony configuration is your responsibility.
Mid-market platforms like Retell AI sit between the developer toolkit and the fully managed service. They abstract some of the telephony complexity but still expect buyers to make meaningful technical decisions about call routing and infrastructure.
High-volume outbound platforms like Bland AI are optimized for dialing at scale. Their telephony architecture is tuned for throughput on outbound campaigns. Features like warm transfer and inbound scheduling workflows have historically been gated behind enterprise tiers.
Business-ready platforms like Feather AI are built for operations teams that need a working, compliant calling operation without assembling the telephony stack themselves. The SIP connectivity, codec configuration, carrier redundancy, and call quality monitoring are handled at the platform level. The buyer's responsibility is configuring the agent's behavior and workflows, not managing SIP trunks.
Codecs, Latency, and Call Quality in AI Voice Deployments
Any discussion of telephony for AI voice agents has to address latency. Conversational AI requires very low end-to-end latency to feel natural. If the gap between a caller finishing a sentence and the AI responding is longer than about 600 to 800 milliseconds, the interaction starts to feel broken.
The codec choice directly affects latency. G.711, the standard PSTN codec, introduces minimal encoding delay but requires more bandwidth. G.729 compresses audio more aggressively, reducing bandwidth at the cost of slightly higher encoding latency and marginally lower voice quality. OPUS, widely used in WebRTC and modern AI voice stacks, is adaptive and particularly well-suited to the variable network conditions of internet-delivered calls.
Beyond the codec, latency in an AI voice call accumulates across several stages: the SIP signaling round-trip, the media streaming path, the speech-to-text (STT) processing, the LLM inference for response generation, and the text-to-speech (TTS) rendering. A platform optimized for production-grade voice AI needs to minimize latency at every stage, not just the telephony layer.
This is why evaluating a voice AI platform purely on its AI capabilities, without examining its telephony architecture, leads to poor purchasing decisions. The best language model in the world will feel clunky if the underlying SIP configuration adds 300 milliseconds of unnecessary signaling delay.
Where Telephony and Voice AI Go Wrong: Honest Limitations and Common Mistakes
The combination of modern telephony infrastructure and AI voice agents is genuinely powerful. But it comes with real limitations, specific failure modes, and areas where honest operators need to set accurate expectations before they go live. This section covers the mistakes businesses commonly make when deploying AI on the phone network, and the cases where the technology, including platforms like Feather AI, is not the right answer.
Mistake 1: Assuming SIP Connectivity Is a Commodity
One of the most common mistakes in voice AI deployments is treating SIP trunking as a solved, undifferentiated commodity layer. It is not. SIP trunk quality varies significantly by carrier, by region, and by the specific routing configuration used.
Common SIP-layer problems that surface in production AI voice deployments:
Codec mismatch: If the AI platform negotiates OPUS but the receiving PSTN gateway only supports G.711, there will be a transcoding step that adds latency and can degrade audio quality.
DTMF handling: Touch-tone input (pressing 1 for sales, 2 for support) can be transmitted in-band (as audio tones) or out-of-band (as RFC 2833 events). Mismatched DTMF configurations cause keypress detection to fail silently.
SIP header stripping: Some carriers strip custom SIP headers used to pass call context between systems. If your warm transfer relies on custom headers to send conversation summaries to the receiving agent's screen, a carrier that strips those headers breaks the feature.
NAT traversal issues: Network Address Translation creates problems for SIP media paths, particularly in hybrid cloud and on-premises deployments. Without proper STUN/TURN configuration, one-way audio is a common result.
Businesses that assume their existing SIP trunk will "just work" with a new AI voice platform often spend weeks in troubleshooting cycles that could have been avoided with upfront carrier compatibility testing.
Mistake 2: Underestimating the Complexity of Warm Transfer at Scale
Warm transfer sounds simple in a demo. In production at scale, it is one of the more technically complex features to get right consistently. The issues that surface:
Transfer target unavailability: If no human agent is available when the AI tries to transfer, the call needs a graceful fallback. Without one, callers are dropped or sent to a voicemail that no one monitors.
Context delivery latency: The conversation summary needs to arrive at the agent's screen before they say "hello." If the context arrives three seconds after the call connects, the agent has already started the conversation blind.
Agent rejection of transferred calls: Some contact center platforms do not accept SIP-transferred calls from external systems without specific configuration. A transfer that works perfectly in QA testing can fail in production because the ACD (Automatic Call Distributor) is not configured to accept third-party SIP REFER messages.
This is not a theoretical concern. It is a practical implementation challenge that separates platforms with genuinely battle-tested warm transfer capabilities from those that have it in the feature list but not in the architecture.
Mistake 3: Ignoring E.164 Number Formatting and Caller ID Compliance
The PSTN routes calls using E.164 formatted phone numbers (international standard: country code + area code + subscriber number, no spaces, no dashes). AI voice platforms that do not handle E.164 formatting correctly will have calls that fail silently, particularly for international numbers.
Beyond routing, caller ID (CNAM and CLID) is a compliance and trust issue. In the United States, the STIR/SHAKEN framework requires carriers to cryptographically attest the legitimacy of caller ID on calls traversing the PSTN. AI voice platforms that place outbound calls without proper STIR/SHAKEN attestation will see their calls labeled "Spam Likely" or "Scam Risk" by carrier-level call screening. For any legitimate business, this is a conversion-killing problem.
Mistake 4: Conflating PSTN Connectivity With Compliance
Being able to place and receive calls over the PSTN does not make your AI voice operation compliant with applicable regulations. These are separate concerns that are frequently conflated in vendor marketing.
Telephony compliance (TCPA, TSR, DNC registry scrubbing, STIR/SHAKEN attestation) governs how you use the phone network, particularly for outbound calling.
Data compliance (HIPAA for healthcare, GLBA for financial services, GDPR for any EU resident data) governs what you do with the information exchanged during the call, including call recordings, transcripts, and data passed to CRM systems.
A platform that offers compliant SIP connectivity but stores call transcripts in non-HIPAA-compliant infrastructure is still a compliance risk for healthcare organizations. Both layers need to be evaluated independently.
Feather AI bundles HIPAA, GDPR, and SOC 2 compliance into its standard offering, not as an enterprise upsell. But even with those certifications, buyers are responsible for ensuring their specific use case, consent language, recording disclosures, and data retention policies align with applicable regulations. No platform certification substitutes for a compliance review of your actual deployment.
Where Voice AI on the PSTN Genuinely Falls Short
Beyond implementation mistakes, there are scenarios where AI voice agents are simply not the right tool, regardless of the platform:
Highly complex, emotionally sensitive calls: Calls involving bereavement, serious medical diagnosis, active mental health crises, or contentious legal disputes require human judgment, emotional intelligence, and accountability that AI cannot reliably provide. Routing these calls to an AI agent (even briefly) before transferring to a human creates friction and can erode trust at a critical moment.
Very low call volumes: If your business receives fewer than a few hundred calls per month, the ROI math for a production-grade AI voice platform does not close. Manual handling or a basic IVR is more cost-effective at that scale.
Highly specialized technical domains with no existing knowledge base: AI voice agents are grounded in the knowledge base you provide. If your calls require real-time access to proprietary technical systems, live pricing engines with millisecond SLAs, or expertise that has never been documented in any form, the agent will underperform without significant knowledge engineering investment upfront.
Callers with severe speech impediments or non-standard acoustic environments: Automatic speech recognition (ASR) accuracy drops significantly for callers with strong accents the model has not been trained on, severe speech impediments, or calls made from environments with high background noise. While the gap is narrowing with newer ASR models, it has not been eliminated.
Honest Assessment: Where Named Competitors Have Specific Edges
Feather AI is the right platform for operations teams at regulated businesses who want a working, compliant calling operation without building the stack themselves. But it is not the right tool for every situation:
Vapi is genuinely superior for engineering teams that want to assemble a fully custom voice stack with granular control over every layer of the telephony and AI architecture. If your team has the engineering resources and the specific requirements justify a custom build, Vapi gives you more low-level control than Feather AI.
Bland AI is optimized for high-volume outbound dialing campaigns where raw throughput is the primary metric. If your use case is pure outbound volume at massive scale and you do not need warm transfer, appointment scheduling, or SMS in the initial phase, Bland AI's infrastructure is built for that workload.
Knowing where competitor platforms have genuine edges is not a reason to dismiss them. It is a reason to be clear about what you actually need before you evaluate.
How Feather AI Connects to the Phone Network and Who It Is Built For
The telephony infrastructure described in the previous sections, the PSTN, SIP trunking, codec negotiation, warm transfer mechanics, and compliance layering, represents the "plumbing" of any voice AI deployment. For most operations and revenue leaders at regulated businesses, the goal is not to become an expert in SIP RFC specifications. The goal is to have a compliant, reliable calling operation running with qualified leads being reached, customers being served, and human agents getting only the calls worth their time.
That is the specific gap Feather AI is built to close.
How Feather AI Fits Into the Telephony Stack
Feather AI is positioned as a business-ready voice AI platform. It is not a developer toolkit, and it is not a DIY SIP configuration exercise. The telephony connectivity, carrier relationships, codec configuration, and call quality monitoring are handled at the platform level. Buyers configure agent behavior and business workflows, not SIP trunks.
Here are the specific Feather AI capabilities that are most directly relevant to the telephony concepts covered in this post:
1. Warm Transfer With Full Context Attached
This is the single most important telephony-level differentiator for businesses that care about the caller experience. Feather AI executes warm transfers where the receiving human agent gets a structured summary of the conversation before they say a word. The AI has already qualified the caller, identified the intent, and surfaced the relevant account information. The human agent enters the conversation with context, not a cold introduction.
This is not just a feature. It is an architectural commitment. Feather AI's warm transfer implementation is built to work reliably at production call volumes, with graceful fallback behavior when transfer targets are unavailable.
This capability was central to Feather AI's deployment for Nada, a real estate investment platform that was receiving 40 or more qualified inbound leads per day but could not call them back fast enough for the sales team to keep up. Feather deployed an agent named "Jessica" that handled instant outreach, qualification, and warm transfer of hot leads to human sales reps. The result: more than 5,000 calls in the first 30 days, with a 19.5% warm transfer rate. According to Sundance Brennan, Head of Revenue at Nada, the operation was live in under two weeks.
"Jessica" handled instant outreach, qualification, and warm transfer of hot leads. 5,000+ calls in the first 30 days. 19.5% warm transfer rate. Live in under two weeks.
2. Real-Time Observability and Call Quality Monitoring
Call quality in AI voice deployments is affected by every layer of the telephony stack: the SIP trunk, the codec negotiation, the network path, and the AI processing pipeline. A platform that gives you production-grade voice AI but no visibility into call quality leaves you guessing when something goes wrong.
Feather AI includes real-time observability and call quality monitoring as part of the standard offering. Operations teams can see what is happening on calls as they happen, not just in post-call reports. This matters for regulated industries where a single compliance event on a call can carry material consequences.
3. Pre-Production Testing Against Simulated Caller Personas
One of the failure modes described earlier in this post is the gap between QA testing and production reality. Feather AI addresses this with pre-production testing against simulated caller personas, so businesses can validate agent behavior, transfer logic, and edge-case handling before the agent touches a real customer call. This is particularly valuable for financial services, healthcare, and insurance deployments where call scripts and disclosure requirements have zero tolerance for errors.
The Full Capability Picture
Beyond these three telephony-specific capabilities, Feather AI's platform includes:
Persistent memory across calls: The agent remembers previous interactions with the same caller, enabling continuity across multi-touch outreach sequences.
20+ languages natively: For businesses serving multilingual customer bases over the PSTN, native language support removes the need for separate localization infrastructure.
Voicemail and hold-music detection: Outbound calls automatically detect voicemail and leave a pre-recorded message rather than burning agent capacity on a non-answer.
Knowledge-base-grounded answers: Agent responses are grounded in your specific product and compliance documentation, not generic LLM outputs.
Direct appointment booking: Inbound calls can result in booked appointments without a human agent involved.
Multi-step workflow automation: Complex call flows, where the agent routes, qualifies, triggers downstream actions, and hands off, are supported natively.
Native CRM integration with Salesforce and HubSpot: Call data, transcripts, and lead status updates flow directly into the CRM without manual entry.
Agent SDK: For teams that want to go beyond the no-code dashboard, the SDK enables custom integrations and workflow extensions.
Who Feather AI Is NOT the Right Fit For
Being specific about fit matters more than a broad pitch. Feather AI is not the right platform in these situations:
Solo developers or engineering teams who want to build a fully custom voice stack: If you want granular control over every layer of the telephony and AI architecture, Vapi is purpose-built for that audience. Feather AI's value is in the managed, business-ready layer, not in low-level customization.
Very low call volumes or non-regulated small businesses: If you are handling fewer than a few hundred calls per month and do not operate in a compliance-sensitive industry, the operational investment in a platform like Feather AI will exceed the return.
Buyers wanting instant self-serve signup: Feather AI is deployed through a structured onboarding process, not an instant signup form. If you need a tool live today with no human conversation, this is not the right fit.
Getting Specific About Your Telephony Architecture
If you are evaluating voice AI platforms and this post has surfaced questions about how a specific platform connects to your existing telephony infrastructure, those are exactly the right questions to bring into a discovery conversation. Ask about SIP trunk compatibility, codec negotiation, STIR/SHAKEN attestation for outbound calls, warm transfer architecture, and how call quality is monitored in production.
The platforms that can answer those questions specifically and concretely are the ones that have actually built production-grade telephony integration, not just a demo that works over WebRTC in a browser.
Feather AI's compliance certifications (HIPAA, GDPR, and SOC 2) are bundled into the standard offering, which means the regulated-industry use cases that touch the most sensitive PSTN calling scenarios are supported without gating behind an enterprise pricing tier.
Closing Thoughts
Telephony is not a legacy concept being swept away by AI. The PSTN, in its IP-modernized form, is still the delivery mechanism for nearly every customer phone call. SIP is the protocol layer that connects modern software systems to that network. And voice AI platforms are the application layer that sits on top of both, handling the conversation and the downstream actions.
Understanding how those three layers interact gives operations and revenue leaders a clearer lens for evaluating vendor claims, asking the right technical questions, and making deployment decisions that hold up in production at real call volumes.
If you are ready to see how a business-ready voice AI platform connects to your specific telephony infrastructure, the next step is a direct conversation:
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