This article answers the 30 questions we hear most often from teams building in this space, covering everything from architecture decisions and async vs. real-time infrastructure to GDPR, data sovereignty, and how to compete against Microsoft Copilot.
Part 1: Technical accuracy and quality
1. How accurate is Gladia's transcription for non-English languages, dialects, and accents?
Accuracy for underrepresented languages is the single most common concern we hear — and for good reason. Generic models trained predominantly on English data frequently underperform on Arabic dialects, Swedish, Scottish accents, and African languages.
Gladia operates a model-routing architecture that selects the best-performing ASR (Automatic Speech Recognition) model for a given language, rather than locking you into a single provider. This means we can match the right model to your language profile rather than asking you to work around a model's limitations.
A core Gladia strength is multilingual robustness: strong performance across 100+ languages (of which 42 exclusive to Gladia) with native code-switching support, and accurate language detection even with strong accents — a common failure point in other systems.
The practical implication: test against your audio, not benchmark datasets. Generic WER (Word Error Rate) scores rarely reflect real-world performance on the specific speakers, microphone setups, and domain vocabulary your users will bring.
2. Can I use custom vocabulary or hot words to improve accuracy for technical terms?
Yes. Custom vocabulary — also called hot words or keyword boosting — is one of the highest-leverage accuracy improvements available to you, and it's particularly effective for meeting assistants where the same specialized terms appear repeatedly.
The problem it solves is straightforward: transcription models have never seen your product name, your company's internal project codenames, or the names of your customers' stakeholders. Without explicit guidance, they'll map unfamiliar audio onto the closest phonetically similar word they've been trained on — which often produces embarrassing or confusing errors.
With Gladia's custom vocabulary, you can supply a list of terms that should be transcribed accurately and consistently. This is especially impactful for healthcare (drug names, procedures), legal (case names, statutes), sales (product names, competitor names), and any technical domain with specialized, sensitive terminology.
Practical guidance: build your vocabulary list from real transcripts, not intuition. Look at what your model consistently gets wrong, and start there.
3. How does Gladia handle speaker diarization when the number of speakers is unknown?
Speaker diarization — the ability to separate and label "who said what" — is foundational to any useful meeting transcript. The complication in real-world deployments is that you rarely know the number of speakers in advance. A sales call might have two people. An all-hands might have twenty.
Gladia provides an industry leading diarization (3x more accurate than other vendors) powered by pyannoteAI (Precision-2) for batch (asynchronous) workflows, without requiring you to declare speaker count upfront. The model detects speaker changes dynamically and segments the transcript accordingly, assigning speaker labels (Speaker 1, Speaker 2, etc.) that can later be enriched with actual names through speaker enrollment or post-processing.
Note: diarization is available in batch mode. For real-time use cases, speaker attribution can be handled in a post-processing pass on the finalized transcript for higher accuracy.
4. How do you handle profanity filtering and prevent misdetection of sensitive terms?
This is a more nuanced problem than it sounds. The naive version — "filter bad words" — is easy. The real challenge is avoiding misdetection: cases where a perfectly legitimate phrase gets incorrectly flagged or transcribed as something offensive.
The most common example is "Google Sheets" being transcribed as something considerably less appropriate. This happens because profanity filters and ASR models operate on phonetic similarity, and some common phrases are phonetically close to words on a blocklist.
Gladia gives you control over profanity filtering behavior rather than imposing a one-size-fits-all policy. You can enable filtering for consumer-facing products, disable it for verbatim legal or compliance transcripts, and use custom vocabulary to explicitly protect terms that might otherwise be misidentified.
For healthcare and regulated industries, this extends to PII and sensitive health information: Gladia can detect entities like names, phone numbers, or identification numbers in the transcript. PII redaction — replacing detected entities with labels like [NAME] or [PHONE_NUMBER] — is available as an opt-in feature that must be explicitly enabled; transcripts are not automatically anonymized by default.
Part 2: Real-time capabilities and latency
5. What is Gladia's latency for real-time transcription?
For voice agents or real-time coaching, latency is a user experience problem before it's a technical one. That said, it's worth starting with a practical reality check: most meeting assistant and note-taker use cases are asynchronous by nature. Meeting summaries, action items, and CRM updates are generated post-meeting, and users are typically comfortable waiting seconds or even a few minutes in exchange for higher accuracy and a more reliable final output.
For use cases that genuinely require a live transcript — in-meeting display, real-time compliance monitoring, live coaching — Gladia's streaming API delivers transcription at sub-300ms latency. This is a competitive real-time capability, though Gladia's core strength remains asynchronous workflows where full audio context enables better accuracy, diarization, and multilingual consistency.
For applications where latency is a hard constraint (live sales coaching, real-time agent guidance, accessibility captioning), there is always a trade-off with accuracy: faster models finalize words sooner but may produce more corrections as context accumulates. Gladia gives you configuration options to tune this balance for your use case.
What "low latency" actually means in practice: for most meeting note-takers, latency under three seconds is imperceptible to end users. Millisecond precision matters only for applications like flashcard generation triggered on specific keywords, or coaching prompts that need to fire within a conversational turn.
6. Can I get word-level timestamps for real-time streaming?
Yes. Gladia provides word-level timestamps in both asynchronous and streaming modes.
This is particularly valuable for note-takers that need to sync transcript segments with other data streams — for example, tying a specific moment in a transcript to a slide change, a screen recording timestamp, or a coaching event. Word-level timestamps allow you to build these associations with precision measured in hundreds of milliseconds rather than seconds.
One thing to be aware of: in streaming mode, word timestamps are provisional until the surrounding context is finalized. A word transcribed at 00:02:14.2 may shift slightly as the model processes subsequent audio and refines its output. For most UX purposes this is imperceptible; for applications where timestamp precision is critical (legal, compliance, accessibility), you may want to run a post-processing pass on the finalized transcript.
7. Do you support real-time diarization, not just post-processing?
Gladia's premium-quality diarization — powered by pyannoteAI (Precision-2) — is available in batch (async) mode, not in the real-time streaming pipeline. This is actually the right architecture for most meeting assistant use cases: post-meeting diarization operates on the full recording with complete audio context, producing more accurate and stable speaker attribution than any live approach.
For products where the display of a live transcript with speaker labels is important, the recommended pattern is to stream a live transcript for the in-meeting UX, then replace it with a high-accuracy diarized transcript once the meeting ends. The final output — which is what feeds CRM sync, summaries, and action items — benefits from the full diarization model.
If you have a use case that truly requires real-time speaker attribution as audio streams, this is worth discussing with our team; for most meeting note-takers, however, the batch diarization path produces substantially better results.
Part 3: Integration and infrastructure
8. What audio formats does Gladia support? Can I send Opus or other compressed formats?
Gladia supports a broad range of audio formats including WAV, MP3, MP4, OGG, Opus, FLAC, and WebM, among others. You do not need to re-encode audio to a specific format before sending it to the API.
This matters operationally because re-encoding adds latency and complexity to your pipeline. If your telephony or WebRTC stack is already producing Opus — the default codec for most browser-based audio — you can send it directly rather than transcoding to PCM or WAV mid-stream.
For streaming use cases, Gladia accepts raw audio chunks over WebSocket, and you can configure the sample rate and encoding to match your capture setup.
9. How does Gladia integrate with telephony systems (SIP, WebRTC, WebSocket)?
Gladia integrates with voice infrastructure through WebSocket streaming, which is the standard transport for real-time audio in modern telephony and meeting platforms.
For SIP-based telephony, the typical architecture routes media through a media server (such as FreeSWITCH, Asterisk, or a cloud PBX) that forwards audio streams to Gladia via WebSocket. For WebRTC-based products, while audio is captured in the browser, a server-side component is still required to handle the WebSocket connection to Gladia — exposing the API key directly in client-side code would be a security risk. The browser streams audio to your backend, which then forwards it to Gladia
For meeting platform integrations (Google Meet, Zoom, Microsoft Teams), Gladia can process audio captured through a meeting bot or a native recording integration, depending on your architecture.
Practical note: integrating with Google Meet, Zoom, and Teams natively — rather than through a bot that joins as a participant — requires navigating platform APIs that change frequently. Many builders find that a well-built meeting bot is a faster path to market, even if a native SDK integration is the eventual goal.
10. Can Gladia be self-hosted or deployed on-premise for data sovereignty?
Yes. Gladia offers on-premise and private cloud deployment options for teams with strict data residency requirements.
Data sovereignty requirements are non-negotiable for a growing set of customers — particularly in healthcare, government, financial services, and regions with specific local data storage mandates. Saudi Arabia, for example, increasingly requires that data processed by AI services remain within Saudi infrastructure. EU customers under GDPR face similar constraints, especially when sensitive health or HR data is involved.
Gladia's on-premise offering means your audio and transcripts never leave your infrastructure. The model runs within your cloud environment or physical servers, and Gladia provides the deployment support to make this operationally manageable.
Gladia also maintains EU and US infrastructure clusters and holds ISO 27001, SOC 2, and HIPAA compliance for cloud deployments that need strong compliance guarantees without going fully on-premise.
Who needs this: if your enterprise customers ask "where is our audio processed?" and any answer other than "inside your own infrastructure" is a deal-breaker, self-hosting is the path forward. For most early-stage products targeting SMB customers, the cloud API is sufficient — and significantly faster to deploy.
Part 4: Scalability and reliability
11. How many concurrent connections can Gladia handle? How does auto-scaling work?
Gladia's infrastructure is built to handle production scale — processing millions of calls per week with high concurrency, without requiring capacity planning on your side. For standard API customers, concurrent connection limits are set at the plan level and can be raised through a capacity request. For enterprise customers with predictable or seasonal volume spikes, Gladia works proactively on capacity planning.
Auto-scaling in Gladia's cloud infrastructure handles unexpected spikes without manual intervention. If your product has a usage pattern tied to business hours — many meeting assistants see 80% of their volume between 9am and 5pm local time — the infrastructure scales up ahead of peak and down afterward.
For on-premise deployments, scaling behavior depends on your hardware provisioning. Gladia provides guidance on GPU and CPU requirements for target concurrent call volumes.
12. What are your SLAs for uptime and latency?
Specific SLA terms are defined at the contract level for enterprise customers, but Gladia maintains a strong operational track record for uptime with a publicly available status page for transparency.
For teams building mission-critical applications — emergency dispatch, healthcare communication, financial services — SLA requirements should be part of your vendor evaluation from day one, not a negotiation afterthought. Key things to specify: uptime percentage (99.9% vs 99.99% matters enormously at scale), latency guarantees for streaming endpoints, and incident response and communication commitments.
13. Who is your transcription provider, and what happens if they have an outage?
This is a question that comes up frequently in security and compliance reviews, and it deserves a direct answer.
Gladia operates a multi-model architecture that routes transcription requests across several underlying ASR providers and in-house models depending on language, use case, and performance characteristics. This is not a single-provider dependency.
From a resilience standpoint, this multi-model approach provides a degree of natural redundancy: if one underlying provider experiences degraded performance, traffic can be shifted to alternative models. Gladia's infrastructure monitoring continuously evaluates model performance and can reroute automatically.
For compliance documentation purposes, Gladia can provide sub-processor information as part of enterprise data processing agreements (DPAs).
Part 5: Architecture and build decisions
14. Should I build my own transcription engine or use a third-party API?
The build-vs-buy question for transcription has a clearer answer today than it did three years ago: for nearly all meeting assistant builders, buying is faster, cheaper, and produces better results.
Building a production-grade ASR system requires significant ML infrastructure investment — training data acquisition, model training and evaluation, serving infrastructure, continuous fine-tuning as models improve and language drift occurs. The teams that choose this path typically have specific language requirements no commercial provider can meet, or are operating at a scale where per-minute API costs become material relative to infrastructure costs.
For most builders, the real question isn't build vs. buy — it's which API, and what the total cost of ownership looks like over time. Evaluate providers on accuracy for your specific language and domain, latency for your use case, scalability, compliance posture, and flexibility for customization. The cheapest per-minute rate is rarely the right optimization target.
15. When should I use real-time transcription vs. asynchronous transcription?
For meeting assistants and note-takers specifically, async (batch) transcription is the right default for most use cases — and it's worth understanding why.
Batch transcription processes the full recording before producing output, which means the model has complete audio context. This enables better accuracy, more reliable speaker attribution (diarization), and more consistent handling of multilingual or accented audio. A 10-minute meeting typically processes in under a minute, and the output quality difference is meaningful. Most meeting assistants rely on this async pipeline to generate high-accuracy post-meeting notes and summaries — users are comfortable waiting a few seconds or minutes for reliable output.
Use asynchronous transcription when:
- Your core use cases are post-meeting — summaries, action items, CRM updates
- Accuracy and diarization quality matter more than in-meeting latency
- You're processing recorded calls, not live audio
- Cost is a consideration (async is typically cheaper per minute than streaming)
Use real-time streaming when:
- Your product surfaces live guidance, coaching, or alerts during a call
- Users expect to see a transcript appear as they speak
- You're building agent-assist tools that need to respond within a conversational turn
- You're doing live compliance monitoring for regulated industries
Many mature meeting assistants use both: real-time streaming for in-meeting display, and an async post-processing pass for the final high-accuracy transcript that feeds CRM sync and summary generation.
16. What is the minimum viable product for a meeting assistant?
The simplest viable meeting assistant has three components: audio capture, transcription, and output delivery.
Audio capture: a bot that joins meetings as a participant and captures the audio stream, or a native integration with a telephony platform. Google Meet and Zoom bot integrations are the most common starting point for general-purpose assistants; telephony integrations (SIP, WebRTC) are the path for customer-facing call use cases.
Transcription: Gladia's API handles this. Wire your audio to the API and receive a structured transcript with speaker labels and timestamps.
Output delivery: at minimum, email the transcript to the meeting organizer. More sophisticated delivery — CRM auto-fill, in-app display, Slack notifications, action item extraction — can come in later iterations.
The teams that ship fastest resist the temptation to over-engineer the MVP. Speaker identification by name, sentiment analysis, and multi-platform support are valuable features; they're not features you need before your first ten customers.
Part 6: Compliance and privacy
17. What are the GDPR requirements for recording and transcribing meetings?
GDPR compliance for meeting transcription has several distinct requirements that builders frequently collapse into a single checkbox:
Lawful basis for processing: you need a valid legal basis to record and transcribe. Consent is the most common for consumer products; legitimate interest may apply in certain B2B contexts. The lawful basis must be documented and communicated to participants before the meeting.
Data minimization: don't retain audio or transcripts longer than necessary for the stated purpose. Define and communicate retention periods.
Data residency: for EU customers, processing data on EU servers is strongly preferred and in some cases legally required. Gladia offers EU-region processing to satisfy this requirement.
Sub-processor transparency: if you're using Gladia (or any third-party API) to process personal data, Gladia must be listed as a sub-processor in your privacy policy and data processing agreements.
Right to erasure: you must be able to delete a user's transcripts and related data on request. Build this into your data model from day one; retrofitting it is painful.
Data usage: on paid plans (Pro, Scaling, Enterprise), your audio and transcript data is not used to train Gladia's models by default — this is included without extra cost or a separate opt-out process.
Practical first step: get a DPA (Data Processing Agreement) signed with Gladia before you process production data from EU users.
18. How do I handle PII and sensitive health information in transcripts?
Transcripts from meetings in healthcare, legal, financial services, and HR regularly contain sensitive personal information — names, diagnoses, account numbers, case details — that should not be stored verbatim in your database or CRM.
The two primary approaches are:
PII redaction at the transcript level: identify and mask sensitive entities (names, addresses, identifiers) before the transcript is stored. Gladia supports entity recognition by default and optional PII redaction — replacing detected entities with labels like [NAME] or [PHONE_NUMBER] — when explicitly enabled in your API request.
Access control at the storage level: store transcripts with role-based access controls, so only authorized users can view full transcripts. This is complementary to, not a substitute for, redaction.
For healthcare specifically, HIPAA compliance adds requirements around business associate agreements (BAAs), audit logging, and encryption at rest and in transit. These are contractual and operational requirements in addition to product-level controls.
19. Do I need to self-host transcription for data sovereignty?
It depends on your customers and the regions you operate in. The short answer is: if your customers are asking the question, you probably do.
Where self-hosting is effectively required:
- Saudi Arabia: regulations increasingly mandate that data be processed on local infrastructure
- Germany: many enterprise customers have policies requiring data to stay within German borders
- Healthcare (global): HIPAA and equivalent frameworks in other countries create strong pressure for on-premise or private cloud deployments
- Government/defense: classified or sensitive government data almost universally requires on-premise processing
Where cloud processing is typically acceptable:
- General B2B SaaS with standard DPAs and EU region processing
- Consumer applications with clear consent flows
- SMB-focused products where enterprise compliance requirements aren't a factor
Gladia's on-premise deployment option is designed specifically for teams that need to offer data sovereignty guarantees to their customers without building their own transcription infrastructure.
Part 7: Advanced features and customization
20. Can I fine-tune Gladia's models with my own data?
Fine-tuning isn't something Gladia offers — and for most use cases, it's not something you actually need.
Fine-tuning a model requires hundreds of hours of labeled audio, significant iteration, and ongoing evaluation. It's a resource-intensive process that makes sense only in a narrow set of scenarios: languages or dialects where general-purpose models are genuinely under-trained, or highly specialized professional domains where even that investment may not close the gap.
Gladia's approach to personalization is through features built on top of best-in-class models rather than modifications to the model itself. Custom vocabulary lets you teach the system your domain's specific terminology — product names, acronyms, technical jargon, internal shorthand — so it stops stumbling on the words that matter most to your users. Combined with other configuration options, this covers the overwhelming majority of accuracy gaps teams actually encounter in production.
21. Can Gladia detect emotions or sentiment at the word or phrase level?
Gladia's audio intelligence layer includes sentiment analysis that operates at the segment level during transcription. It's important to understand what this means technically: sentiment is derived from the transcript text, not from vocal tone or prosodic signals. The analysis interprets the words and phrasing of what was said, not how it sounded.
For coaching and QA use cases, this enables flagging specific moments in a conversation — expressions of frustration, satisfaction, or urgency — post-call or in a batch processing workflow. It works best as a signal to surface for human review, not as a hard trigger for automated actions.
One practical note for multilingual use cases: text-based sentiment can vary in reliability across languages depending on the quality of the underlying transcription and the language's representation in the sentiment model.
Part 8: Market strategy and technical trade-offs
22. How do I compete with Microsoft Copilot, Gemini, and other big tech meeting tools?
The honest answer: don't compete on breadth. Compete on depth, customization, and the use cases that big platforms deliberately leave underserved.
Microsoft Copilot and Google's meeting intelligence are optimized for the median enterprise user in a generic meeting context. They're sold as platform features, not as standalone products. That means:
- They don't offer deep customization for specific verticals (healthcare, legal, staffing)
- They're tied to specific platforms (Teams, Meet) — poor or absent support for telephony, third-party conferencing, or custom infrastructure
- They don't allow custom models, fine-tuning, or integration with proprietary CRMs
- Enterprises pay per-seat, per-user — which becomes expensive for high-volume use cases
Your differentiation lives in vertical depth, workflow integration, and the parts of the meeting intelligence stack that Copilot can't or won't touch. A meeting assistant purpose-built for recruiting that integrates directly into an ATS, understands recruiting vocabulary, and auto-populates candidate profiles is not competing with Copilot — it's solving a problem Copilot doesn't address.
23. How do I evaluate and benchmark speech-to-text providers?
The speech-to-text market lacks standardized evaluation frameworks, which creates a real problem for buyers: every provider quotes a different benchmark on a different dataset, making direct comparison nearly impossible.
That's a problem we decided to address directly. Gladia has published an open benchmark evaluating Solaria-1 against 8 leading providers across 7 datasets and 74+ hours of audio, with a fully open-sourced methodology so every result can be independently reproduced — or re-run against your own audio.
Most providers quietly avoid benchmarking on conversational data because clean audiobook and parliamentary speech scores look far better. Switchboard reflects what your users actually sound like. On that dataset, Gladia ranks first with a WER of 35.8% — on average 30% better than other providers.
On speaker diarization, Gladia's results are even more pronounced. Measured on the DIHARD III benchmark suite across diverse real-world audio domains, Gladia's diarization error rate is roughly 3x better than other commercial vendors — meaning significantly fewer misattributed speaker turns in your final transcript, and more reliable downstream outputs like CRM entries, summaries, and action items.
The only benchmark that ultimately matters is yours. Build an evaluation set from your own audio — representative samples of the languages, accents, audio quality, and domain vocabulary your users will bring. The Gladia open benchmark gives you the framework to do exactly that: transparent methodology, open-source tooling, and results you can verify rather than just trust.
24. How do I balance accuracy vs. latency in real-time transcription?
This is one of the most consequential architectural decisions in a meeting assistant, and the right answer varies significantly by use case.
For most meeting note-takers, accuracy should win. The output your users care about — summaries, action items, CRM updates — depends entirely on the quality of the underlying transcript. An error in the transcript compounds into an error in every downstream system. Users generally accept a delay of seconds or even a few minutes for post-meeting notes if the output is reliable and well-structured.
When latency matters more than accuracy:
- Live coaching and agent assist (a prompt that fires three seconds late missed the moment)
- Real-time transcript display (users find lag more jarring than occasional corrections)
- Keyword detection triggers
- Accessibility captioning (where latency above 1-2 seconds is disqualifying)
Gladia's API gives you configuration options to tune the latency-accuracy balance. Shorter audio segments produce faster output with less context; longer segments produce more accurate final output with higher latency. For most meeting note-takers, displaying a streaming transcript in real-time with an async post-processing accuracy pass is the optimal architecture — you get the UX of live transcription with the accuracy of a finalized model.
25. How do I handle multi-language conversations and code-switching?
Code-switching — where speakers move between languages within a single conversation — is one of the harder problems in applied ASR. It's common in multilingual markets (South Africa, Singapore, many MENA countries, European businesses with multinational teams) and almost universally underperforming in off-the-shelf models.
Gladia's language detection operates at the segment level, which means the system can identify language shifts within a conversation rather than committing to a single language at the start of the session. For well-resourced language pairs (English ↔ French, English ↔ Spanish), this works reliably. For lower-resource pairs, performance degrades.
Practical guidance: if code-switching is a core requirement for your target market, make it an explicit evaluation criterion — not an assumption. Test specifically on mixed-language samples representative of your users. For languages where no model handles code-switching adequately, consider segment-level language detection combined with per-segment model selection as an architectural pattern.
26. How should meeting notes be structured and delivered to maximize user value?
The format and delivery channel for meeting notes are as important as the quality of the underlying transcript. A perfectly accurate transcript delivered in the wrong format at the wrong time creates no value.
Format: users consistently prefer structured summaries over raw transcripts. The hierarchy that works: TL;DR (2-3 sentences) → key discussion points → action items with owners → next steps. Raw transcripts belong in a collapsible section for reference, not as the primary output.
Gladia's API provides the transcription and audio intelligence foundation — translation, entity detection, and text-based sentiment analysis are all bundled in the base price. For teams building meeting notes or quick recaps, summarization is available as a helpful convenience layer on top of transcription. For more advanced or customized summarization workflows, most builders pair Gladia's structured transcript output with their own LLM — you can send Gladia's output to any LLM or use integrated models.
Delivery timing: for async meetings, delivering within 5 minutes of call end outperforms both instant delivery (before the user has mentally transitioned) and delayed delivery (when context has faded). For sales calls, CRM auto-population should happen before the rep's next meeting.
Channel: the right delivery channel depends on your users' workflow. Email works universally but has low engagement. In-app notifications work well if your product has a native client. CRM delivery (Salesforce, HubSpot, Pipedrive) has the highest workflow integration value for sales-facing products. Slack delivery works for internal team meetings.
27. What integrations do users expect from a meeting assistant?
User expectations have evolved significantly as the category has matured. The table stakes integrations in 2026:
Calendar integration: the assistant should know about upcoming meetings, join automatically, and link transcripts to the corresponding calendar event without user intervention.
CRM sync: for any customer-facing meeting use case, automatic CRM enrichment — contact details, meeting summary, follow-up tasks — is expected, not a premium feature.
Video platform coverage: users expect their assistant to work across Google Meet, Zoom, and Microsoft Teams without requiring separate tools.
Communication channel delivery: Slack and email delivery of summaries are expected by most teams.
Emerging expectations: integration with project management tools (Linear, Asana, Jira) for action item creation; integration with knowledge bases (Notion, Confluence) for meeting documentation; and AI-powered search across meeting history.
28. How do I turn unstructured meeting transcripts into actionable workflows?
The first generation of meeting assistants answered "what was said." The products winning in this space now answer "what should happen next."
The architectural shift is from transcript storage to context engine. Instead of depositing a transcript in a database, a context engine makes the meeting content available to downstream AI agents that can:
- Draft follow-up emails with specific references to commitments made in the meeting
- Create and assign tasks in project management tools based on action items detected in the transcript
- Update CRM fields with information surfaced during the call (budget, timeline, stakeholders, objections)
- Trigger workflows based on specific phrases, sentiments, or events detected in real-time
Building this layer on top of Gladia's API typically involves: structured extraction (using LLMs to identify entities, commitments, and action items from the transcript), workflow integration (pushing structured data to downstream tools via their APIs), and confidence scoring (surfacing low-confidence extractions for human review rather than automating blindly).
Gladia structures audio into LLM-ready data and supports both bring-your-own LLM and integrated models — so the transcript output is designed to feed directly into this kind of downstream pipeline.
29. What is the market opportunity for meeting assistants in specific verticals?
The horizontal meeting assistant market is consolidating around a few dominant platforms. The growth opportunity is in vertical-specific products that solve problems generic tools don't address.
Healthcare: clinical conversation intelligence — documenting patient interactions, generating structured notes for EHR systems, flagging clinical commitments. Regulatory complexity (HIPAA, local equivalents) is high, but so is willingness to pay. Accuracy requirements are extremely high. Nabla is the standout example here: purpose-built for clinical environments, it generates structured medical notes directly from patient conversations, targeting a problem that generic tools like Otter.ai or Fireflies aren't designed to solve.
Staffing and recruiting: automatic ATS population from candidate interviews, structured candidate evaluation, compliance documentation. High call volume creates strong ROI for automation. Tools like Fireflies have made early inroads here — its CRM and ATS integrations make it a natural fit — but the opportunity for a purpose-built recruiting intelligence layer remains largely uncaptured.
Customer support and contact centers: agent assist, quality assurance automation, compliance monitoring, coaching at scale. Telephony integration (SIP, WebRTC) is the primary technical requirement. Gong is the most mature example, having evolved from sales call recording into a full revenue intelligence platform — a proof point for how deep vertical focus can unlock enterprise-level willingness to pay.
Accessibility and assistive technology: real-time captioning for users with hearing impairments, communication aids for users with speech differences. Different accuracy requirements and latency constraints than traditional business use cases; deep social impact. This remains one of the least competed-for segments in the market — an open opportunity for builders willing to optimize for a different definition of performance.
Financial services: call recording compliance, structured note-taking for advisors, regulatory documentation. High compliance requirements, high value per interaction. Read AI has made moves in this direction with its compliance-oriented feature set, and the segment broadly remains underserved by tools that weren't designed with financial regulation in mind from day one.
30. How do I handle real-time meeting assistance for accessibility use cases?
Accessibility-focused meeting assistants have distinct requirements from standard business note-takers, and the differences are worth understanding before you design your product.
Latency is not negotiable. For users relying on real-time captions to follow a conversation, latency above one to two seconds is disqualifying. The product must be engineered with latency as the primary constraint from day one.
Accuracy for non-standard speech patterns. Users with speech differences, accents, or non-native speech patterns often receive the worst results from off-the-shelf ASR models — which is exactly the opposite of what an accessibility product should deliver. Custom vocabulary, fine-tuning, and model selection need to prioritize these edge cases. Gladia's strength in real-world audio robustness — noisy conditions, non-native accents, imperfect recordings — is particularly relevant here.
Display and format. Accessibility-focused transcript display requires attention to reading ease: clean word wrapping, high contrast, appropriate font sizing, and minimal visual noise. The raw streaming output from an API needs more UX work than a summary delivered by email.
Regulatory landscape. Products in the accessibility space often touch ADA (US), WCAG standards, and regional equivalents. Compliance requirements shape both product design and procurement conversations.
Gladia's real-time streaming API (~300ms latency), combined with careful model selection for the relevant language and speaker profile, provides the transcription infrastructure. The differentiation is in how you wrap it.
Getting started with Gladia
The questions in this article represent the real decision points builders work through before shipping meeting intelligence products — and the answers only matter if your infrastructure can keep up.
Gladia's API is designed for exactly that: broad language support with native code-switching, custom vocabulary, enterprise-grade compliance, low-latency streaming, and a multi-model architecture that routes to the best-performing ASR for your use case. Pricing is per hour of audio processed, with diarization, translation, sentiment analysis, and entity detection all bundled at no extra cost — Starter starts at $0.61/hr async and $0.75/hr real-time (with 10 free hours per month), Growth brings rates as low as $0.20/hr through upfront commitment, and Enterprise adds zero data retention, SLAs, and dedicated support.
Whether you're shipping your first meeting bot or scaling to millions of calls a week, the foundation is the same accurate transcription, flexible infrastructure, and a pipeline that grows with you — not around you.
.png)
.png)
