Software · Automation · AI
for Modern Enterprises
Software · Automation · AI
for Modern Enterprises
Echo is a reference architecture for conversational voice systems — L1 support, scheduling, reception, and outbound calling. Natural turn-taking, context awareness, and direct integration with the back-office systems that handle the actual work.
Most voice bots are scripted IVRs in a fancier coat. Echo treats voice as a conversational interface to your existing systems — the agent listens, understands intent, looks things up in your CRM or ERP, takes action, and confirms. The caller talks; the back-office state changes.
The architecture is opinionated about boundaries: voice handles intent capture and confirmation, structured systems handle records of truth. We don't ask the LLM to remember inventory levels or appointment slots. We ask it to converse, route, and reach the right system at the right moment.
Natural-language understanding with context awareness, emotion detection, and graceful turn-taking. Handles interruptions, clarifications, and topic shifts the way humans do.
One agent, many channels — phone, web call, mobile app, messaging — with shared conversation state and consistent persona.
Smart escalation based on intent, sentiment, and case complexity. The agent hands off to humans with full context, not a transcript dump.
Direct integration with CRM, ERP, ticketing, and scheduling. The agent doesn't just answer — it acts: books, reschedules, refunds, escalates, logs.
Real-time translation and localisation for global operations — including code-switching within a single call.
Call volume, resolution rates, AHT, sentiment trends, escalation reasons, and CSAT — surfaced in dashboards your ops team will actually open.
Always-on support that scales instantly to handle peak demand. No queue, no wait music, no shift handoffs.
Conversations feed back into intent models and prompt refinements. The agent gets sharper with use — supervised by your QA team.
The voice loop is engineered for latency: streaming speech-to-text starts transcribing as the caller speaks, the LLM begins generating before the caller finishes, and text-to-speech streams audio back token-by-token. End-to-end response under 800ms in production deployments.
Every action the agent can take is a typed tool: lookup customer, book appointment, create ticket, escalate to human, send confirmation. We don't give the model raw API access — we give it intent-shaped functions the model can compose safely.
When the agent escalates, the human picks up with the full conversation summary, the customer's verified identity, the relevant system records, and the agent's confidence on the issue. No "let me transfer you to my colleague who needs you to repeat everything."
Tell us about your call profile, your systems of record, and the workflows you want voice to actually complete. We'll come back with a phased pilot plan.