AI Simultaneous Interpretation Software: 2026 Playbook

Why Fora Soft wrote this playbook

We’ve built real-time video products since 2005 — 625+ projects, and most of them touch live communication somewhere: video conferencing, broadcast, tele-education, telemedicine, hybrid events. Over the past three years we’ve added live translation to a growing share of them. Captions in 30 languages for a global webinar series. Voice dubbing for a HIPAA-scoped cross-border telehealth app. Real-time interpretation for a corporate town hall that used to hire six interpreters and now hires none.

This is the working document we use to scope those projects. It covers what “AI simultaneous interpretation” actually means in 2026, what the pipeline looks like, which vendors and models are worth using, where the milliseconds and the dollars go, and when not to use AI at all. If you’re weighing Zoom AI Companion against a custom build, choosing between Wordly, KUDO, and Interprefy, or just trying to understand why your human interpreters cost $13k a day. Start here. Two of our shipped systems sit behind this: TransLinguist, a video interpretation platform, and Volo, a real-time translation system.

For the wider vendor picture, our team also keeps the AI translation companies breakdown and the real-time speech translation architecture guide current. If you want us in the room, that’s our language interpretation engineering team.

One honest note on cost and speed: Agent Engineering and modern tooling have compressed our timelines about 40% in 18 months. A 16-week integration is now 10–12. We still do the hard parts — pipeline tuning, glossary work, compliance — we just ship them faster and price them lower than we could two years ago.

What “AI simultaneous interpretation” actually means in 2026

The term gets used loosely, and the buying decisions differ by mode, so it’s worth being precise. There are five things people mean.

Translated captions (live captions): speaker audio → source transcript → translated text overlay. Under 1 second is achievable. Zoom, Teams, Google Meet, and Webex all ship this. No voice replacement, cheap, and dominant in enterprise.

Simultaneous interpretation (voice-to-voice): speaker audio → translated audio in another language, delivered live. This is what a human interpreter in a booth does. AI pipelines now run it 2–4 seconds behind the speaker. Wordly, KUDO AI, Interprefy’s AI mode, and purpose-built deployments do this.

Consecutive interpretation: the speaker pauses, the interpreter translates, the speaker resumes. Common for small meetings, depositions, and medical consultations. It’s less latency-critical, so AI handles it comfortably; there’s no race against a live speaker. If you’re searching “consecutive vs simultaneous interpretation,” the split is simply whether the translation overlaps the speaker (simultaneous) or follows them (consecutive).

Whisper interpretation (chuchotage): a quiet translation to one or two listeners. On mobile this is Google Meet speech translation and Apple’s real-time translation in AirPods, where the listener just holds a phone or wears earbuds.

Pre-recorded dubbing and subtitling: not simultaneous, done in post-production. Out of scope here, though it shares most of the stack with live dubbing.

The market: two curves compounding

AI simultaneous interpretation sits where two growing markets meet. Business Research Insights puts the AI simultaneous interpreting segment at roughly $0.5B in 2023 heading to $2.3B by 2032, about 19% CAGR. Slator’s 2026 Market Report tracks the broader AI-addressable language-solutions market at ~$30.9B in 2025. Interpretation is the fast-growing slice inside a much larger video and language stack.

The demand driver that matters most in 2026 isn’t unit-price savings. It’s scope. Human interpretation was rationed: you hired interpreters for the annual kickoff, not the weekly town hall. AI makes interpretation cheap enough to switch on for every meeting with international participants. That’s a 10–100× jump in how often it gets used, not just a discount on the old use.

The interpretation pipeline: six stages, end to end

Almost every deployment — Zoom AI Companion or a custom WebRTC build — runs the same six-stage cascade. The interesting decisions are what runs where, and how tightly the stages couple.

Figure 1. The cascade, stage by stage, with the 2026 latency budget for each step.

1. Audio capture. Microphone input from the speaker’s client over WebRTC, SIP, or raw RTP. 16 kHz minimum, 48 kHz preferred for TTS output. Noise suppression (RNNoise, Krisp, or platform-native) runs here.

2. Voice activity detection and turn segmentation. VAD separates speech from silence; turn detection predicts when a thought is finished. Deepgram’s Flux folds both into the ASR and emits end-of-turn events at about 260 ms; Silero VAD and WebRTC VAD stay the open-source defaults. A 300–500 ms silence threshold is typical.

3. Streaming ASR. Speech to text, incrementally. Target under 500 ms from audio in to first token. Deepgram Nova-3, Google Chirp 3, AssemblyAI Universal-Streaming, Azure Speech, and self-hosted Whisper v3 are the 2026 options.

4. Machine translation. Source text to target text. In 2026 the top LLMs (GPT-5, Claude, Gemini 2.5 Pro) have overtaken dedicated engines on COMET for most high-resource pairs. Gemini 2.5 Pro won the WMT25 human evaluation on 14 of 16 language pairs. DeepL and Google Translate still win on raw latency (~50 ms) and predictability. Meta NLLB-200 is the open-source fallback past 100 languages.

5. Text-to-speech. Translated text to audio. ElevenLabs Flash v2.5 (~75 ms first byte, 32 languages) leads on low latency; Google Chirp 3 HD, Amazon Polly Neural, and Azure Neural TTS round out the cloud. For output in the speaker’s own voice, use ElevenLabs voice cloning or Meta SeamlessExpressive.

6. Delivery. Caption overlay (WebVTT, or EBU-TT-D for broadcast) or audio replacement. WebRTC SFUs (LiveKit, mediasoup, Janus, Amazon Chime SDK) fan out one stream per language; each listener picks their language client-side. This is the layer we most often own end to end on video-conference builds.

Cascade vs direct speech-to-speech

The theoretically cleaner approach is end-to-end: audio in, audio out, no text in between. Meta SeamlessM4T v2 (with its streaming variant) does this for ~100 input and ~36 output languages, and Meta released the weights openly, but under a non-commercial CC-BY-NC license, which rules it out of most paid products. OpenAI’s GPT-Realtime models and Google Meet’s speech translation are the commercially viable native-S2S options in 2026.

In practice, 85–90% of production deployments still run the cascade. The comparison below is why.

Figure 2. Seven axes where the two architectures differ, and why the cascade still ships most 2026 systems.

We pick direct S2S where prosody and voice preservation dominate (media dubbing, high-end executive comms, creative work), and only with a commercially licensed model. We pick the cascade for everything else, including nearly every enterprise deployment.

The 2026 model stack: who ships what

The vendor list is long; the short answer is short. Deepgram or AssemblyAI for ASR, an LLM (Claude, GPT-5, Gemini) or DeepL for MT, ElevenLabs or a GPT-Realtime model for TTS, and Meta Seamless for the rare commercially-cleared direct-S2S case. Everything else is a detail.

Streaming ASR

• Deepgram Nova-3 — 45+ languages, diarization, auto language detection, under 300 ms first token, ~$0.0077/min (~$0.46/hr). Its newer Flux model adds model-native end-of-turn detection and went multilingual GA in April 2026. The default for mid-to-large events.
• AssemblyAI Universal-Streaming — ~150–300 ms P50, purpose-built for voice agents.
• Google Chirp 3 — multilingual streaming ASR, GA across 24+ languages and 100+ in preview, strong on non-English.
• Azure Speech — enterprise-grade, custom models, regional deployment; pairs naturally with Teams.
• Whisper v3 (self-hosted) — via faster-whisper or whisper.cpp; free if you own the compute, reliable across 99+ languages.

Machine translation

• LLMs (Claude, GPT-5, Gemini 2.5 Pro) — now ahead of dedicated MT on COMET for most top-30 pairs, especially with glossary prompting. Latency 200–500 ms. Gemini 2.5 Pro topped WMT25 human eval on 14/16 pairs.
• DeepL — fastest MT for high-resource pairs (~50 ms), predictable, strong European coverage.
• Google Translate — broadest coverage (130+ pairs), 50–80 ms.
• Meta NLLB-200 — 200 languages, open-source, self-hostable; quality falls off outside the top 100.
• Llama, Mistral — open-weight LLMs for self-hosted MT when data cannot leave your infrastructure.

Streaming TTS

• ElevenLabs Flash v2.5 — 32 languages, ~75 ms first byte, ~$0.05/1k characters. The low-latency multilingual leader.
• OpenAI GPT-Realtime-2 and GPT-Realtime-Translate — native speech-to-speech and live translation (70+ input to 13 output languages at ~$0.034/min), shipped May 2026. Prompt-driven, easy to shape into a custom interpretation agent.
• Google Chirp 3 HD, Amazon Polly Neural, Azure Neural TTS — solid cloud options, good prosody, wide voice catalogs.
• Hume EVI 3 — emotional prosody (sighs, emphasis) across 11 languages.

Direct speech-to-speech

• Meta SeamlessM4T v2 — ~100 input / ~36 output languages, preserves prosody and speaker voice, open weights — but CC-BY-NC (research only), so not for commercial products as-is.
• OpenAI GPT-Realtime-2 — commercially licensed native S2S with GPT-5-class reasoning; the practical choice for a custom real-time interpreter in 2026.
• Google Translatotron 3 — research preview, limited deployment.

Platforms: what’s actually shipping

Conferencing suites with built-in AI interpretation

Most enterprise buyers don’t need to build. The big four all have real offerings in 2026, but read the voice-translation column carefully, because “translation” usually means captions, not dubbed voice.

If you’re already on one of these and the built-in captions cover your languages, you’re done. Don’t build. A dedicated platform earns its keep on quality, terminology control, voice cloning, large-event reliability, and on-prem or EU-residency compliance.

Dedicated AI interpretation platforms

Latency budget: where the milliseconds go

Human simultaneous interpreters run at a 2–4 second ear-voice span (EVS). That’s not reaction time, but the content they need to hear before they can translate meaningfully. Interpreters tolerate about 3 seconds of system latency before quality drops; past 4, delivery breaks. A tuned AI cascade now lands inside that window.

Figure 3. Stage-by-stage P50 latency — ASR and the ASR-to-MT chunk wait dominate the budget.

Two optimizations matter most. First, co-locate ASR and MT in the same cloud region (Deepgram + an LLM in us-east1); cross-region hops cost 40–150 ms each. Second, start TTS speculatively on the first clause instead of waiting for the full sentence. ElevenLabs Flash takes incremental input, and overlapping TTS with the tail of MT shaves 200–400 ms off perceived delay.

Cost model: AI vs human, per hour

This is where the buying case usually closes. Professional simultaneous interpretation needs two interpreters per language. They rotate every 20–30 minutes to keep fatigue errors down (the AIIC “rule of two”). For a full-day event in three languages, that’s six interpreters plus booths and equipment.

Figure 4. The buying case in one chart, plus the DIY per-speaker-hour API math.

The DIY numbers assume Deepgram Nova-3 (~$0.46/hr), an LLM for MT (~$0.40/hr at typical speaking rates), and ElevenLabs Flash (~$0.80/hr per output language) — about $1.66 per speaker-hour for one language. ASR scales with speaker-hours; MT and TTS scale with speaker-hours × output languages.

What the DIY column hides is the operational cost: engineering, infrastructure, SFU operations, caption UX, glossary management, on-call. Realistically a DIY interpretation layer costs a small team on the order of $180k to build and ~$20k/year to run. That only pays off above roughly 500–1,000 hours a year or when the product needs something SaaS can’t give you: custom voices, proprietary glossaries, EU residency, or broadcast-grade reliability.

Compliance: GDPR, EU AI Act, HIPAA, ADA, broadcast

Interpretation sits on top of voice data, one of the most regulated data categories there is. Most projects hit at least two of these, and each one is an architecture decision, not a checkbox.

• GDPR (EU) — voice is personal data and voiceprints are Article 9 biometric data. You need a Data Processing Agreement with every ASR/MT/TTS vendor, a lawful basis, explicit consent for voice cloning, a DPIA, EU data residency, and delete tooling.
• EU AI Act Article 50 — in force 2 August 2026. Systems that generate synthetic audio must disclose it: an AI-dubbed voice needs an opening disclosure and machine-readable marking of the output. Fines run up to €15M or 3% of global turnover.
• HIPAA (US healthcare) — interpretation transcripts often carry PHI, so any vendor that touches them is a business associate and must sign a BAA, with encryption, retention limits, and audit logging. Most commercial ASR SDKs are not HIPAA-eligible by default.
• ACA Section 1557 (US healthcare) — the updated rule requires qualified interpreters and, for the first time in federal policy, that critical machine-translated communications with limited-English-proficiency patients be reviewed by a qualified human. Design a human-review path for high-stakes medical content.
• ADA Title III / Section 508 (US) — public meetings and webinars need real-time captions at a 95%+ accuracy target; Section 508 extends this to government systems.
• Court and diplomatic work — AI is not accepted in most US and EU courts. Depositions, immigration hearings, and asylum interviews still require certified humans; AI can prep and transcribe but not substitute.
• BIPA (Illinois) — any voiceprint processing on Illinois residents needs written consent and a published retention and destruction schedule. Active class-action venue.

Integration patterns: where AI interpretation plugs in

Four patterns dominate. Pick based on how much of the media stack you already own.

Pattern A — SaaS overlay on Zoom / Teams / Meet. Wordly, KUDO, or Interprefy join the meeting as a participant, take the speaker audio, and publish captions or a dubbed channel through the platform’s interpretation slots. Zero integration work. Buy, turn on, done. Fits 60–70% of enterprise buyers.

Pattern B — conferencing platform on a WebRTC SFU (LiveKit, mediasoup, Janus). You add an interpretation server as an SFU participant. It subscribes to the speaker track, runs the cascade, and publishes one output track per language; the client picks its language via track subscription. Clean, scalable, and how we build most custom video-conference platforms.

Pattern C — broadcast / streaming with caption-first delivery. HLS or DASH output with a subtitle track per language (WebVTT or EBU-TT-D). The translation layer produces captions only; voice dubbing happens in post for recorded content. Standard for live news, sports, and public-audience conferences.

Pattern D — mobile whisper apps. Single-user, client-side or API-backed. Apple Translate with AirPods, Google Meet speech translation, and purpose-built apps. The mobile side is covered in our mobile voice playbook; the delivery side in the AI streaming platform guide.

Mini case: an interpretation layer on a hybrid-event platform

An enterprise-events client ran 40+ multi-language webinars a year on a custom LiveKit platform. Before 2026 they hired 4–6 human interpreters per event and paid about $60k/year for the service on top of $180k/year for the platform team. AI was clearly cheaper, but they’d tried plain Zoom captions and found the quality too weak for pharma and medical audiences full of terminology.

We built a cascade layer inside their existing LiveKit cluster:

• ASR: Deepgram Nova-3 with a ~2,500-term medical/pharma keyword boost list.
• MT: an LLM with per-event glossary prompting, DeepL as the latency-critical fallback.
• TTS: ElevenLabs Flash v2.5 across five target languages (Spanish, French, German, Japanese, Portuguese).
• Captions: LiveKit data channels, WebVTT rendered client-side.
• Compliance: EU data residency end to end, GDPR DPAs, and an EU AI Act Article 50 disclosure at event start.

After three months: P50 latency 1.4 s, P95 2.3 s. Caption accuracy against human transcription ran ~96% on the source and ~92% on translation targets. Cost per event dropped from ~$1,500 (human interpreters) to ~$80 (AI). The pharma customer accepted AI-only captions after a 30-day pilot and kept humans on standby only for their highest-stakes regulatory briefings. Year-one savings landed near $55k; integration cost $140k one-time plus ~$18k/year to run. Want a similar assessment for your platform?

Quality metrics: what to measure

Don’t trust vendor marketing numbers. Measure in production. These are the metrics that predict whether users can actually follow along.

• WER (word error rate) — ASR quality. Under 5% is excellent on English; 5–15% is workable on other top-30 languages; over 20% means the wrong model or bad audio. Expect WER to roughly double on noisy or heavily accented input.
• COMET / MQM — MT quality. COMET is the modern neural metric and tracks human judgment better than BLEU; aim for COMET above 0.80 on high-resource pairs, with human sampling for the rest.
• MOS (mean opinion score) — TTS naturalness. ElevenLabs Flash and Hume EVI 3 both score above 4.1 in user studies; target above 4.0.
• End-to-end latency — P50 and P95. Captions under 1 s / 1.5 s; voice dubbing under 2 s / 3 s.
• Terminology hit rate — the share of domain terms the translation gets right. Glossary-dependent; aim above 95%.
• User satisfaction — a post-event survey on “I could follow the content in my language.” Target above 4.0 of 5.

Accents, dialects, and low-resource languages

AI interpretation degrades gracefully on high-resource languages and falls off a cliff on low-resource ones. Plan around the tiers.

Top 30 (English, Mandarin, Spanish, French, Arabic, Hindi, Japanese, Korean, Portuguese, German, Italian, and the like): excellent ASR and MT across all vendors — WER under 10%, MOS above 4.0. You can ship AI interpretation and be understood.

Ranks 30–100 (Swahili, Tamil, Telugu, Ukrainian, Urdu, Farsi, and ~60 others): workable but error-prone. BLEU 0.30–0.40, WER 15–25%. Disclose the limits and keep a human fallback for critical sessions.

Below the top 100: unreliable. No commercial TTS voices, and idiom, toponyms, and cultural context fail often. Use humans.

Accent handling: heavy non-native accents push ASR WER up 15–30% versus a native baseline, and the gap widens under noise. Code-switching (mid-sentence language change) is handled by multilingual models like Deepgram Nova-3 and Chirp 3 but still costs accuracy. Regional dialects — Scottish English, Brazilian vs European Portuguese, Arabic variants — track training-data coverage.

5 pitfalls that kill AI interpretation projects

1. Benchmarking in the cloud, deploying to home Wi-Fi. Cloud-to-cloud tests show 800 ms; real users on home broadband with jitter see 2.5 s P95. Measure where your users actually are.

2. Ignoring glossaries. Pharma, legal, financial, and technical domains have hundreds of proper nouns and acronyms generic MT butchers. Per-event glossaries are the single biggest lever on perceived quality, so budget 10–20% of integration time for them.

3. Under-specifying compliance early. GDPR residency, HIPAA BAAs, and Article 50 disclosure are architecture-level. Retrofitting them costs 3–5× more than building them in. Scope compliance in week 1, not week 10.

4. Skipping the human fallback. For board meetings, investor briefings, and regulatory hearings, keep a human interpreter on standby. KUDO and Interprefy hybrid tiers exist for exactly this. A $2k/day interpreter is cheap insurance against a $2M incident.

5. Choosing direct S2S too early. Meta SeamlessM4T v2 is impressive and non-commercially licensed; native S2S generally has less operational tooling than the mature cascade. Unless voice preservation is a core differentiator, ship the cascade and revisit direct S2S in 12–18 months.

When NOT to use AI interpretation

Be honest about the limits. It’s what earns trust. These are the cases where a human interpreter is still the right answer.

• Court proceedings, depositions, immigration and asylum hearings. AI is not accepted in most US and EU jurisdictions; certified humans are required.
• High-risk medical: informed consent, diagnosis, psychiatric evaluation, medication instruction. Liability and patient safety require certified humans.
• Diplomatic or classified government work. Data residency and security requirements typically exclude commercial cloud vendors.
• Creative content: literature, poetry, film, theater. Metaphor, allusion, and wordplay are where MT is weakest, and will be for years.
• Low-resource languages below the top 100. Quality is unreliable and there are no professional TTS voices.
• High-stakes board, investor, or M&A negotiations. Not a technology limit — a risk-tolerance one. When a single mistranslation can move $100M, you pay for humans.

A decision framework — pick your stack in five questions

Figure 5. Two questions route most teams from buy to build; high-stakes sessions stay human.

1. How many hours per year? Under 100: stay with humans or a built-in platform (Zoom AI Companion). 100–1,000: SaaS interpretation (Wordly, KUDO AI). Over 1,000 or a differentiated product: custom build on a WebRTC SFU.

2. How many language pairs? One to five top-30 pairs: any vendor works. Ten or more, or low-resource: you need NLLB-200 or enterprise MT with custom terminology.

3. Captions or voice dubbing? Captions-only is a simpler pipeline, under 1 s latency, and cheaper. Voice dubbing adds TTS, more latency, and voice-cloning consent.

4. What’s your compliance surface? GDPR and Article 50 only: any major vendor with a DPA. HIPAA: BAA-signed enterprise vendors and a human-review path. Court or regulatory: stay with humans.

5. Who owns the media stack? On Zoom/Teams/Meet: Pattern A (SaaS overlay). Your own WebRTC SFU: Pattern B (integrated cascade). Broadcast or streaming: Pattern C (caption-first). If the answer points to a build, that’s exactly what our interpretation team scopes.

Integration playbook: the 10–14-week path

For a SaaS overlay (Pattern A), compress to 4–6 weeks — skip the SFU integration, keep compliance and the pilot. For a broadcast build (Pattern C), add 4–6 weeks for CVAA compliance and CDN caption delivery. For a timeline against your specific stack and volume, book a 30-minute scoping call and we’ll walk the phases week by week.

Where AI interpretation is heading in 2026–2027

Direct speech-to-speech goes mainstream. As commercially licensed native-S2S models mature (OpenAI’s GPT-Realtime line, Google Meet), expect 30–40% of new deployments to use direct S2S by end of 2027, led by media and creative work.

Speaker voice preservation becomes table-stakes. By 2027 most AI interpretation will output in the speaker’s own voice; ElevenLabs voice design, SeamlessExpressive, and PlayHT already enable it, with consent workflows as the gating factor.

Sub-second end-to-end latency becomes normal. Co-location, speculative TTS, and wait-k tuning keep tightening the cascade; native S2S drops the floor further. P50 under 1 s for voice dubbing is the 2027 target.

Regulatory consolidation. Article 50 is the template; expect US state-level and Asian equivalents by 2027, and disclosure UX to become a regulated design pattern.

FAQ

What is the best simultaneous interpretation software in 2026?

There isn’t one winner. It depends on volume and control. For turnkey events, Wordly (AI-only) and KUDO (AI plus human fallback) lead; for enterprise all-hands, the built-in Zoom, Teams, and Google Meet features are often enough. For a product that needs custom voices, glossaries, or data residency, a custom cascade on Deepgram + an LLM + ElevenLabs beats every off-the-shelf tool. Match the tool to your hours per year, languages, and compliance surface.

Can ChatGPT or AI replace human interpreters?

For mid-stakes content in top-30 languages with glossary tuning, AI now matches or beats junior human interpreters — and OpenAI’s GPT-Realtime-Translate does live speech translation across 70+ input languages. For legal, diplomatic, and high-risk medical work, and for rare languages, no: certified humans are still required, both legally and for quality. The realistic 2026 answer is AI for scale, humans for stakes.

How accurate is AI simultaneous interpretation?

On top-30 languages with a domain glossary, expect ASR word error rate under 10% and translation accuracy around 90–95% against human reference. Accuracy drops on heavy accents, noisy audio (WER roughly doubles), and low-resource languages. Measure in production against human transcripts. Vendor benchmarks overstate real-world numbers by 2–3×.

What latency should you target?

Captions: P50 under 1 s, P95 under 1.5 s. Voice dubbing: P50 under 2 s, P95 under 3 s. Humans tolerate up to ~4 s of ear-voice span; above that, fluency breaks and people complain.

How much does a custom build really cost?

Roughly $140–220k one-time integration over 10–14 weeks, $15–25k/year in ops and on-call, plus API costs (~$1.66 per speaker-hour per language). Break-even versus Wordly or KUDO lands around 500–1,000 hours a year; below that, SaaS wins.

Zoom captions or a dedicated platform: which do you need?

If your event mix is under ~100 hours a year in top-30 languages, Zoom AI Companion or Teams Premium is fine. Above that, or with specialized terminology, Wordly, KUDO, or a custom cascade produces noticeably better results and gives you glossary control.

Can AI interpret in my speaker’s own voice?

Yes. ElevenLabs voice design, Meta SeamlessExpressive, and PlayHT all support voice cloning. You need a consent workflow, a few minutes of source audio, and a one-time enrollment. Laws like Tennessee’s ELVIS Act and EU AI Act Article 50 require documented consent and disclosure, so plan a signed release.

Do I need to disclose AI interpretation to users?

Under EU AI Act Article 50, in force 2 August 2026, yes. Best practice is an opening announcement (“this session uses AI interpretation”), a visible label on captions, and machine-readable marking of synthetic audio. US states are converging on similar rules. Build disclosure into the UX from day one.

What about on-premises or air-gapped deployments?

Possible, with more work. Self-host Whisper v3 for ASR, Llama or NLLB-200 for MT, and Piper or Coqui for TTS. Quality runs 15–30% below the cloud leaders, but it’s necessary for classified government, some healthcare, and strict EU-residency deployments.

What to read next

Ready to scope your interpretation stack?

AI simultaneous interpretation in 2026 is a buying decision for most organizations and a build decision for a few. The cascade wins on modularity, glossary control, and operational maturity; direct speech-to-speech wins on prosody, and is finally commercially viable through OpenAI and Google. The economics are decisive. AI replaces $5,400–13,200/day human budgets with $800–2,500 SaaS or ~$30–60 DIY spend — and a tuned cascade now lands inside a human interpreter’s latency window. Get glossaries and compliance right early, keep humans for legal, diplomatic, and high-risk medical, and you have a system that scales interpretation to every meeting instead of the once-a-year kickoff.

If you want a second opinion on buy vs build, or an engineering team that’s shipped this into conferencing, telehealth, and broadcast, we’re a call away — and we’ll tell you honestly which path fits your stack.