Verticalized Voice AI – The Next Application Layer Shift

Every decade or so, a new interface reshapes not just how people interact with technology, but the structure of entire software markets. The graphical user interface in the 1980s made computing accessible to the enterprise. Mobile touchscreens in the 2000s redefined consumer engagement and created new platform giants. Application programming interfaces (APIs) in the 2010s quietly rewired the software economy, enabling interoperability and accelerating SaaS adoption.

We believe we are now on the cusp of the next interface shift: voice. Similar to how large language models (LLMs) have transformed how software is built, we think that voice AI has the potential to redefine the multi-hundred billion dollar application software layer and the thousands of companies that serve it. Most B2B software applications today still depend on manual data entry; sales reps typing notes into their customer relationship manager (CRM), financial advisors updating compliance logs, clinicians documenting encounters in electronic health record (EHR) software. These workflows tend to be slow, error-prone, and fundamentally misaligned with how humans actually communicate: with their voices.

Voice is the most intuitive interface we have. We speak faster than we type, and spoken language carries nuance that is often lost in structured forms or post-hoc summaries. When captured, structured, and integrated into workflows, we think voice data has the potential to not just replace existing systems of record, but expand the application layer itself, powering automation, intelligence, and new categories of software.

A Brief History: Pre-LLM Voice AI

Before LLMs unlocked new possibilities for near real-time reasoning, voice was already proving its commercial value. At Georgian, we saw this shift first-hand through our investment in Chorus, one of the first sales conversation intelligence platforms that was ultimately acquired by ZoomInfo (NASDAQ:GTM) in 2021. Alongside Gong, which became a category leader, these platforms demonstrated how recording and analyzing sales conversations could fundamentally reshape how sales teams operate.

In its early days, Gong might have looked like just another call-recording tool. But over time, it became a trusted source for what was happening behind the scenes on sales calls and important customer touch points. Reps still logged opportunities in Salesforce (NYSE:CRM), but managers turned to Gong transcripts and analytics for the unfiltered reality of sales conversations. Entire coaching programs were built on top of Gong’s dataset.

The insight, which seems obvious in retrospect, is that voice captures reality more accurately than manual entry. CRM fields are subject to human error, bias, or outright neglect. A recorded and transcribed conversation, by contrast, is unambiguous. Gong demonstrated how a voice-first system could coexist with a CRM and, in certain respects, become more trusted than the CRM itself. In 2021, before ChatGPT’s release took the world by storm, Gong reportedly reached $300M in ARR, tripling in growth, foreshadowing, in our view, the broader potential of voice AI’s impact on enterprise technology.

The Horizontal Wave: Voice AI Meets LLMs

The arrival of LLMs pushed voice AI past transcription into understanding. Transcription accuracy improved, processing latency dropped to near real-time, and new capabilities have emerged: summarization, intent extraction, and reasoning over conversations to generate insights and actions.

The step-change in voice AI can be attributed to several technical advances, including:

• Automatic speech recognition (ASR) / speech-to-text (STT): End-to-end neural models trained on large audio datasets reduced error rates by handling noise, accents, and domain jargon. Latency also dropped significantly, with leading systems now claiming well below <500ms latency and enabling real-time dialogue. 
• (LLMs): Once transcription became both fast and dependable, LLMs added a reasoning layer, interpreting conversations to produce structured outputs such as summaries, compliance tags, or recommended next steps.
• Text-to-speech (TTS): Finally, advances in text-to-speech ensured that AI could not only listen but also respond in a natural tone. Modern systems capture context across full sentences, producing speech with human-like rhythm and intonation at real-time speeds.

These advances unlocked a wave of horizontal platforms using voice AI. Venture funding is flowing into these platforms (e.g., CB insights estimates an 8x increase in Voice AI funding from 2023 to 2024) as investors seek to capture the opportunity, and user adoption has followed. 

In customer support, companies like PolyAI (a Georgian portfolio company since 2022), are providing conversational agents that guide conversations from start to finish in a way that feels natural and on-brand to the customer. PolyAI’s agents look to enhance both service quality and support capacity beyond what traditional teams have historically managed, solving common functional pain points felt across a variety of verticals.

In productivity, tools like Granola, Otter, and Zoom’s AI companion (NASDAQ:ZM) capture and summarize meetings. For knowledge workers, voice capture and AI-generated summaries are starting to be seen as table-stakes. The colleague with searchable transcripts and AI-curated action items will likely outpace the one still relying on memory and manual notes.

But horizontal platforms also have limits. In regulated, workflow-heavy verticals, generic solutions are often insufficient. They can summarize a conversation, but aren’t built to guarantee compliance. They can transcribe, but may miss the acronyms, shorthand, and regulatory language that define professional dialogue. And even when generic tools get the words right, they rarely integrate into the core systems professionals actually use, leaving workflows fragmented.

If past cycles in the application layer teach us anything, it’s that verticalization emerges where horizontal tools fall short. In CRM, Salesforce became the horizontal standard, but vertical players emerged where workflow depth and compliance mattered. Veeva built a $40B+ business by tailoring CRM to the unique needs of life sciences. In wealth management, domain-specific CRMs like Redtail (acquired by Orion in 2022) gained adoption because they were embedded into financial advisors’ workflows and addressed requirements around maintaining audit trails.

We believe the same lesson may apply here: horizontal solutions may reach mass distribution, but specialized players can still win multi-billion dollar verticals with trust and workflow fit. In our view, Voice AI has the potential to follow that same trajectory.

From Scribes to Agents: The Long Game

Much of Voice AI today is about capture and transcription, but we predict the following evolution for platforms as they mature:

1. Recorder of truth: Voice becomes the primary real-time record and enabler of professional interactions.
2. Workflow automation: Voice systems can auto-update systems of record (CRMs, ERPs, TMS), file compliance logs, and trigger downstream tasks eroding some of the value that legacy systems provide. 
3. Agentic systems: With proprietary datasets and contextual memory, voice AI platforms enable agents that act on behalf of professionals by surfacing insights, orchestrating workflows, and making recommendations or executing tasks autonomously.

Why Verticalization Matters

In our view, many of the most defensible voice AI companies over the next decade will be vertical specialists that go deep into specific domains. What sets them apart is not just better transcription, but the ability to embed directly into the systems professionals use day-to-day. These tight integrations have the potential to turn voice AI from a capture tool into the workflow engine itself, reducing legacy software to a passive database while the voice layer orchestrates how work gets done.

Beyond integrations, we believe three additional factors may compound defensibility:

• Vertical tuning beats raw accuracy. Mapping utterances to the terminology, acronyms, and compliance requirements of a given profession makes a system usable and trusted, often more valuable than squeezing out marginal improvements in word error rate.
• Proprietary data loops create moats. Domain-specific audio and transcripts generate feedback cycles that general-purpose models may not be able to replicate. Challenges such as noisy environments can be accounted for with vertical specific datasets that reflect the true environment users work in.
• Compliance and trust are often critical. In professionally-licensed domains, accuracy and regulatory adherence are not optional. Platforms that embed compliance from day one provide the trust layer professionals depend on to protect both their license and reputation.

Case Study 1: Healthcare 

Healthcare is a technically difficult environment to implement voice AI. Conversations are laden with jargon. Encounters are chaotic – interrupted, nonlinear, filled with acronyms. Documentation is tied not just to clinical outcomes but to reimbursement, compliance, and liability. It’s also a market we believe stands to benefit from voice AI, as clinicians today spend hours on manual note-taking and administrative tasks that take time away from patient care.

That’s why Georgian invested in Ambience Healthcare. Ambience’s core product is an ambient scribe: listening to clinician-patient conversations, generating structured notes, and auto-charting directly into the EHR.

In our view, Ambience’s product illustrates the playbook for vertical voice AI:

• Domain specific precision. Differentiating between “diabetes mellitus type 1” and “type 2” may seem trivial – but it’s essential for both treatment and billing.
• Process alignment. Documentation must map cleanly to insurance codes for reimbursement.
• Frictionless adoption. Physicians may be hesitant to change their behavior for new tools. Success comes when the technology disappears into the background and feels like a natural extension of existing workflows.
• Regulatory compliance. Regulatory compliance is non-negotiable. Requirements like HIPAA, audit trails and data residency are baseline expectations in healthcare. Without them, adoption stalls before a pilot can even begin.

The result is not just transcription, but workflow automation. Ambience, which is now used by leading health systems across the United States (e.g., Cleveland Clinic, UCSF Health, Houston Methodist, and Memorial Hermann), serves as an example of how voice AI, when verticalized, can evolve from a helpful scribe into mission-critical infrastructure.

Case Study 2: Financial Advisors

Wealth management is another vertical where we view voice AI as a natural fit to transform workflows. In the U.S. alone, there are over 320,000 financial advisors at managing ~$144 trillion in assets. Their day-to-day is shaped by two realities: constant client interaction and heavy regulatory oversight. Every conversation must be documented, every recommendation logged, every audit trail maintained.

Yet the tools advisors rely on (e.g., CRMs like Salesforce or Redtail) were designed as static databases, not real-time systems of record. As a result, advisors spend hours filling out documentation and client meeting notes rather than generating new business or providing client service.

Companies such as Jump, Zocks, and Zeplyn are addressing this gap by turning voice into the interaction layer for advisors. What began as transcription is now evolving into CRM-ready summaries, compliance intent tagging, firm-level trend analysis, and coaching insights across organizations.

And, as in healthcare, the implications of voice AI adoption extend well beyond transcription. By capturing the verbatim record of every advisor-client interaction, verticalized voice AI platforms can orchestrate workflows that legacy CRMs were not designed to support. In our view, these software platforms could, over time, expand naturally into adjacent regulated markets such as insurance, estate planning, and tax, with similar accuracy and compliance requirements.

Case Study 3: Logistics

In the logistics industry, each shipment passes through a chain of drivers, dispatchers, warehouses, and customers, requiring accurate documentation at every handoff. Today, much of this coordination still occurs over phone, email and text, with details logged manually after the fact. The result is predictable: incomplete records, limited real-time visibility for supervisors, and costly disputes when deliveries are delayed or conditions are misreported.

Emerging companies such as HappyRobot, Augment, and Vooma are applying voice AI directly to frontline workflows by capturing and transcribing conversations between drivers and dispatch in real time and automatically tagging key details such as delivery times, load conditions, exceptions, and instructions. Record-keeping shifts from ad hoc notes to structured, searchable data, reducing ambiguity and improving transparency across the supply chain.

Integrations are key, as we believe voice records gain real value when tied directly into transportation management systems (TMS), fleet platforms, and customer dashboards, creating a single operational view. Just as important are audit trails, which ensure accountability, support safety enforcement, resolve disputes, and meet compliance requirements.

And these platforms are already moving beyond capture. Augment’s agents, for example, enumerate dozens of recurring call types (e.g., appointment scheduling, ETA changes, document chase) with guardrails and approvals, then acts: updating systems, escalating exceptions, and notifying stakeholders automatically. Over time, this shifts information-trading from slow, asynchronous exchanges to a more synchronous, automated flow – cutting waste from missed docks, idle labor, and suboptimal fleet utilization.

In our view, for logistics providers, voice AI marks a shift from reactive oversight to proactive, automated operations.

Why Now

We think that there are several factors that point toward Voice AI reaching an inflection point:

• The technology has matured. Transcription accuracy and LLMs have advanced to the point where real workflows can be automated in real time. What once produced brittle outputs is now robust enough for regulated, professional environments. 
• Adoption is accelerating. Enterprises are already deploying horizontal voice AI tools at scale, normalizing AI-driven transcription and summaries as part of everyday work. The amount of funding in the space has amplified awareness and experimentation, even if the ultimate winners are still being determined.
• The demand is urgent. In industries like healthcare, financial services, and logistics, professionals are overwhelmed by administrative work leading to burnout, and regulators are simultaneously demanding greater transparency and auditability. Voice AI sits at the intersection of those two forces: relieving the burden of documentation while creating a more complete and verifiable record of interactions.

Taken together, these shifts explain why voice AI is moving from experimental to inevitable. The open question is not whether voice will matter, but which companies will establish the trust and workflow depth required to carry it into the most critical professional domains.

We think Voice AI, when done correctly, is more than just another productivity feature layered onto existing software. It is a wedge into the core of professional workflows.

The opportunity isn’t incremental efficiency. It’s the chance to redefine the core software stack of entire professions.

We believe the prize ahead is clear: voice is the new interface, and the next generation of vertical leaders will be built on it.