AI that works in the
real world — your world
ELOQUENCE's conversational AI systems have been validated across four high-stakes industry environments — from emergency healthcare call centres to privacy-preserving smart home assistants. Each use case was built with real partners, tested on real data, and measured against real performance targets.
Four industries.
One platform.
Each use case is a fully validated deployment scenario — not a demo. The same ELOQUENCE Interactive Playground backs all four, configured with domain-specific data, dialogue flows, and evaluation pipelines.
AI-Supervised Emergency Call Centres
LLM-powered triage for healthcare call centres. The agent collects structured clinical information and delivers a context summary to medical staff before handoff — reducing cognitive load and improving response quality.
- Assesses urgency levels in real time using dialogue context and clinical ontologies
- Collects structured information from callers while they wait for staff availability
- Generates concise handoff summaries for medical professionals
- Operates with human oversight — all critical decisions remain with clinical staff
- Validated on a corpus of 150 simulated dialogues covering realistic newborn care scenarios
Self-Adapting Customer Service Virtual Agents
RAG-powered virtual agents that generalise across industries — finance, insurance, utilities — without fine-tuning. Retrieves verified answers from enterprise documents, tracks multi-turn dialogue state, and responds with empathy and factual grounding.
- Trained on diverse industry datasets including finance, insurance and utilities
- Deploys to new enterprise contexts without fine-tuning or retraining
- Retrieval-augmented: answers grounded in company documents, not model memory
- Dialogue state tracking for coherent multi-turn conversations
- API call capabilities for live data lookup during customer interactions
Privacy-Preserving Home Voice Assistants
Federated learning for smart home voice assistants. Models improve collaboratively across devices — only parameter updates leave the device, never raw user data. Privacy by design, built into Telefónica's AURA platform.
- Federated learning: model weights shared, never raw user data
- Hierarchical FL architecture supporting heterogeneous device environments
- Keyword spotting, automatic speech recognition and speaker diarisation included
- Differential privacy layers for additional protection
- Fully compatible with GDPR and EU AI Act requirements
Socially-Aware Career Counselling AI
Career guidance AI that systematically detects and mitigates cultural, gender, ethnic, and religious bias — using synthetic persona datasets and multilingual evaluation across 24+ EU languages to establish benchmarks for equitable AI.
- Evaluates responses across cultural, gender, religious and ethnic contexts
- Uses synthetic persona datasets to surface biases not visible in standard evaluation
- Prompt engineering and bias stimulation strategies for rigorous stress-testing
- Multilingual evaluation covering 24+ EU official languages
- Emotion analysis module for detecting contextually inappropriate responses
Shared capabilities across
all deployments
Every ELOQUENCE use case is built on the same core technology stack — validated across all four scenarios and available for custom deployment.
From your challenge
to a running system
ELOQUENCE isn't a product you install — it's a research-backed, production-ready framework that we configure to your specific domain, data, and compliance requirements.
Industries where
language matters at scale
Let's talk about
your needs
Tell us about your use case. We'll prepare a customised demo using your domain, your language, and your data — so the first conversation is already relevant.