Building voices that
actually listen
Domain has spent a decade working out why most voice assistants feel robotic — and doing something about it. We design conversational AI that handles real language, not just scripted commands.
From Strathroy to nationwide deployments
Process, people, and outcomes
Discovery and conversation mapping
We sit with your team to document the actual conversations your customers have — not the ones a requirements document assumes they have. This shapes the intent architecture before any code is written.
Prototype with real language samples
Early prototypes use transcripts and logs from your existing support channels. Testing against real phrasing catches failure points that synthetic test sets consistently miss.
Integration with existing infrastructure
The assistant connects to your CRM, booking system, or internal database — whichever is relevant. Clean handoffs to human agents are designed in from the start, not retrofitted.
Post-launch monitoring and refinement
Conversation logs are reviewed at regular intervals. Mishandled intents, unexpected phrasings, and drop-off points get addressed in scheduled update cycles — not emergency patches.
Fewer escalations. Faster resolution.
A well-designed assistant does not just deflect tickets — it resolves them. Clients typically see their human support queues concentrate on genuinely complex cases within the first few months. That shift takes time and iteration; we do not promise it happens on day one.
Callum Ostrowski
Designs intent taxonomies and conversation flows. Has worked on IVR and chat systems for healthcare, logistics, and retail clients.
Veronika Šimánková
Connects language models to production systems and handles training data curation. Particular focus on Canadian English regional phrasing and bilingual edge cases.
Tarquin Adewale
Responsible for dialogue pacing, error recovery patterns, and the small moments that make an automated conversation feel less mechanical. Background in voice interface research.
Mireille Fontecha
Coordinates project timelines, stakeholder communication, and post-launch review cycles. Ensures the work delivered matches what was scoped — and flags it early when it might not.