telli Secures $15M Seed to Automate Customer-Facing Operations
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| Stage | Job | Fails when |
|---|---|---|
| Speech-to-text (STT) | Converts sound into words | Background noise, heavy accents, cross-talk |
| Natural Language Understanding (NLU) | Understands the words being spoken | Ambiguous phrases, missing context, complex sentences, informal language, idioms |
| Large Language Model (LLM) | Determines intent and best response | Poor prompting, limited training data, lack of access to tools |
| Retrieval-Augmented Generation (RAG) | Pulls company-specific context and information | Outdated or unorganised information base, poor indexing or keyword selection |
| Application Programming Interface (APIs) | Actually does the work (booking appointments, updates, checks, sending emails) | Broken integrations, network timeouts, poorly structured or incomplete API information |
| Text-to-speech (TTS) | Turns response back into speech | Robotic speech patterns, inaccurate pronunciation, latency, or delays |
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| Factor | Why it matters |
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| Prosody | Natural rise and fall in intonation, not flat delivery |
| Pronunciation | Correct handling of names, numbers, abbreviations |
| Pacing | Neither rushed nor unnaturally slow |
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