Professional AI Voice Assistant Development - Custom Skills Services

A cascaded pipeline runs three sequential API calls: Speech-to-Text (Whisper), LLM inference (GPT-4o), and Text-to-Speech (ElevenLabs). Each call adds 400-600ms of latency, resulting in 1,200-1,800ms total before the user hears a response. OpenAI Realtime API processes audio natively in a single model call, achieving sub-300ms end-to-end. Beyond latency, native multimodal processing retains paralinguistic cues (tone, emotion, hesitation) that are permanently lost when audio is transcribed to text.

Generic ASR models (Whisper, Google STT) are trained on clean studio audio. Real-world environments — warehouses at 85dB, hospital wards with PA systems, automotive cabins with road noise — are acoustically very different. We address this by recording 30-60 minutes of audio in your actual deployment environment and fine-tuning the ASR acoustic model on that data, consistently achieving 15-20% WER improvement over the generic model baseline.

A custom wake word (e.g., 'Hey [BrandName]') creates a branded, ownable voice identity. Generic wake words ('Hey Siri', 'OK Google') train users to associate the experience with Apple or Google, not your brand. Custom wake words run fully on-device (Sensory or Picovoice) at <1mW — no audio is ever sent to the cloud until the wake word triggers, meeting GDPR data minimization requirements. Training a high-quality custom wake word takes 2-3 weeks, including false-positive rate optimization.

We deploy with OpenAI's HIPAA-eligible Realtime API configuration (BAA available from OpenAI). All audio processing uses zero-retention mode: audio is processed in real-time and not stored by OpenAI. For ambient clinical documentation, we implement speaker diarization to separate clinician commands from patient conversation and ensure patient speech is never transmitted. EHR integration via SMART-on-FHIR uses OAuth 2.0 with role-based scopes.

Yes. We implement hybrid edge-cloud architectures: wake word detection and simple command processing run fully on-device (no connectivity required). For more complex queries, we cache frequent responses locally and queue cloud requests for when connectivity resumes. For fully offline requirements (automotive tunnels, remote industrial sites), we deploy quantized on-device LLMs (Llama 3 8B INT4 on NVIDIA Jetson or Snapdragon) for complete offline conversational capability.

In our blind listening tests across 10 client deployments, 7 of 10 listeners cannot distinguish the ElevenLabs clone from the original voice actor. The clone requires a minimum 30-minute clean recording sample from your chosen voice actor. We validate the clone against 50+ test utterances covering your domain vocabulary (technical terms, product names, brand language) and iterate on prosody fine-tuning until the voice matches brand standards.

A focused single-domain voice assistant (e.g., healthcare documentation, voice-directed warehouse, in-car controls) with environment ASR fine-tuning, one custom voice, and 3-5 backend integrations typically takes 10-14 weeks to production readiness. A full multi-domain assistant with custom wake word, ElevenLabs voice, edge deployment, and enterprise integrations (CRM, ERP, EHR) typically takes 16-22 weeks depending on integration complexity and compliance requirements.

We instrument every deployment with: Word Error Rate (WER) per user segment and environment, intent classification accuracy, conversation completion rate (user achieved their goal?), barge-in rate (proxy for response-too-slow), and end-to-end latency at p50/p95/p99. Weekly automated WER reports flag degradation before users notice. We provide bimonthly ASR model refreshes with new environment audio to maintain accuracy as conditions evolve.

Yes. The voice layer supports full function calling: mid-conversation, it can query CRM records, create support tickets, update ERP inventory, schedule appointments, or confirm orders — all without breaking conversational flow. Tool calls are wrapped with idempotency keys (preventing duplicate API calls if the user repeats a command), retry logic with exponential backoff, and verbal confirmation prompts for irreversible actions (e.g., 'I'll cancel order #4892. Shall I confirm?').

Voice AI in healthcare and financial services is classified as 'high-risk' under EU AI Act Annex III. Requirements include: conformity assessment before deployment, registration in the EU AI Act database, transparency logging (every decision logged and explainable), human oversight mechanism (ability to override or shut down), and regular accuracy/bias testing documentation. We perform the risk classification, implement the required technical measures, and prepare the conformity assessment documentation for your legal team.