The Challenge
Echofern Enterprise AI is the client behind this project. A Melbourne enterprise AI business wanted customers and employees to interact with applications through voice and natural language. The platform was intended to support hands-free workflows, accessibility and routine request automation.
Background noise, different accents and response latency affected the experience. Keeping context across a conversation was another requirement: follow-up requests needed to remain connected to earlier interactions.
What ApexCore Digital built
ApexCore Digital combined speech-to-text, NLP, language models and text-to-speech within a voice assistance platform. Noise filtering, domain-specific speech models and streaming processing address recognition and response requirements.
Context-aware conversation memory supports follow-up interactions. The platform also includes multilingual support, voice authentication and API action execution, connecting conversational requests with enterprise applications. Low-latency inference forms part of the solution approach.
How the workflow supports the business
A voice interaction moves through several stages: speech is transcribed, the request is interpreted, an application action or response is prepared, and the answer is delivered as speech. Conversation memory supports follow-up requests that depend on prior context. Multilingual support and domain-specific speech models address the range of inputs identified in the brief. Streaming processing and low-latency inference focus on responsiveness across this sequence. Measuring speech recognition separately from intent and response accuracy helps distinguish where the experience succeeds and where further refinement may be needed.
Reported Results
- 95% speech recognition accuracy.
- 92% intent recognition.
- 80% voice query automation.
- 91% response accuracy.
- 90% user satisfaction.
Technology: React / mobile interfaces, Python, FastAPI, Transformers, GPT / enterprise LLM, Whisper / cloud speech API, AWS Polly / Azure Speech, PostgreSQL, AWS.
