Four things separate Interpreterly from every other AI interpreter on the market: proprietary noise cancelling, sub‑300ms turn‑taking, industry‑tuned speech language models, and near 99% accuracy on the fields that actually matter — emails, phone numbers, and IDs.
Most AI interpreters collapse on real phone calls: clinic chatter, service‑bay air tools, infant cries, speakerphone echo, cell handoffs. Interpreterly runs proprietary, bidirectional noise suppression tuned for telephony bandwidth (8 kHz narrow‑band and HD voice). Both the English speaker and the LEP caller come through clean, so the model interprets what was actually said — not a guess.
Generic LLM interpreters wait 1–3 seconds before responding. Callers talk over each other, hang up, or assume the line dropped. Interpreterly's turn‑taking engine is optimized end‑to‑end under 300 milliseconds — fast enough to feel like a live human interpreter. People stop apologising for the pause and start having a normal conversation.
We don't ship one generic interpreter. We deploy a Speech Language Model (SLM) tuned to your industry's vocabulary — and then tuned again per language pair. Healthcare gets ICD‑10, medications, and intake phrasing. Auto gets VIN/RO numbers and service‑drive language. Legal gets immigration and personal‑injury terms. Each language is fine‑tuned separately so Spanish‑for‑clinics doesn't sound like Spanish‑for‑dealerships.
Ask a generic interpreter to capture an email address or a phone number over the phone and you'll get gibberish — "gmail" becomes "e‑mail," digits drop, dots disappear. Interpreterly uses a dedicated capture layer for emails, phone numbers, dates, addresses, policy numbers, account numbers, and confirmation codes. We hit near 99% accuracy on these high‑stakes fields — the single biggest failure point in every competitor we've benchmarked.
| Capability | Interpreterly | Everyone else |
|---|---|---|
| Bidirectional noise cancelling tuned for telephony | Off-the-shelf or none | |
| Turn-taking latency | <300 ms | 1–3 seconds |
| Speech model tuned per industry | Yes — per vertical | Generic LLM |
| Speech model tuned per language pair | Yes — every pair | Same model for all languages |
| Email / phone / ID capture accuracy | ~99% | 60–80% |
| Works on cell, landline, speakerphone, VoIP | Degrades on cell / speaker | |
| Deploy without app, SDK, or SIP changes | Just dial a number | Integration project |
Trained on a top BrainCX receptionist — warm, fast, never robotic.
Emails, phones, policy and account numbers, dates, and IDs — captured and read back for confirmation.
Section 1557, Title VI, 42 CFR Part 2, and Fair Housing aware — every transcript audit‑ready.
