Conversational AI for Customer Service — Designed, Engineered, and Operated at Scale
We design, engineer, and operate conversational AI for customer service across inbound support, outbound campaigns, and back-office voice workflows. Your team gets multilingual voice agents on your cloud with production-grade MLOps/LLMOps, agentic orchestration, real-time voice pipelines, and MCP integrations — from first MVP to large-scale rollout in weeks, not quarters.
Production-ready conversational AI for contact centers, built on your cloud, governed by your standards, integrated with your systems of record.
- Real-time voice stack with VAD, ASR, TTS, diarization, and interruption handling
- Agentic orchestration with specialist agents, memory, and safe human escalation
- MCP integrations into CRM, EHR, billing, HR, and ticketing systems
- Security-first setup with GDPR/HIPAA-ready controls and data residency options
- 6–8 weeks from data access to first live agent in production
Why Do Most Conversational AI Projects Fail Between Demo and Production?
Most organizations have already tested a voicebot, IVR add-on, or cold call AI script, yet still cannot move it into governed production. The issue is rarely the demo. It is the absence of platform standards, deployment discipline, observability, integration governance, and an operating model that scales conversational AI beyond a single pilot.
Architecture & Technical Building Blocks
Event-driven services on AWS, GCP, or Azure that autoscale per call volume.
Low latency, failover, and data residency compliance across regions.
VAD → ASR → LLM orchestration → TTS with sub-second p95 turn latency.
Memory and caching layers that hold context across turns and across calls.
Routing to the optimal model, voice, and language per call turn.
Metrics, logs, traces, call recordings, and quality dashboards.
Hooks for call tracking, predictive analytics, and RPA post-call work.
From Discovery to Run in 4 Steps
We align on business goals, SLIs/SLOs, security constraints, current telephony setup, integration scope, and target architecture. Output: signed-off architecture diagram, SLO sheet, integration map, risk register. (1–2 weeks)
We build infrastructure, the real-time voice stack, MCP integrations, orchestrator flows, evaluation harnesses, and CI/CD pipelines. Output: deployed non-prod environment, test suite, observability dashboards, prompt and model registry. (3–5 weeks)
We launch one contained but production-grade use case, such as inbound FAQ, appointment booking, or outbound renewals, and validate quality, latency, and business impact. Output: live agent handling real calls, measured containment and CSAT, go/no-go report for scale. (6–8 weeks from kickoff)
We provide SLA-based support, tuning, language and use-case rollout, and enablement so your teams gradually own more of the platform. Output: quarterly roadmap, optimization backlog, handover playbooks, trained internal owners. (ongoing)
Benefits of Production-Ready Conversational AI for Customer Service
30–50% reduction in telephony and contact center infrastructure cost
60–80% automation of routine inbound calls on selected queues
2–4x higher contact rate on outbound and cold call AI campaigns versus human-only dialing
40–60% lower cost per resolved call after full MVP stabilization
Who This Technical Service Is For
Conversational AI Design & Voice Platform Engineering
We cover the full path from dialog design and outbound agents to in-call intelligence, multilingual voice, and the MLOps and MCP integration that keep it all in production.
Frequently Asked Questions
Conversational AI for customer service is a voice agent that understands natural language, maintains context across turns, and executes tasks in real systems, unlike IVR, which relies on rigid menus and DTMF tones. It handles free-form speech, multiple intents per call, and safe handover to humans.
6–8 weeks from data access to a first live agent in production. Discovery and architecture take 1–2 weeks, implementation 3–5 weeks, and MVP go-live happens on a contained, production-grade use case before we scale to additional queues, languages, or outbound campaigns.
Yes. Our outbound and cold-calling agents enforce consent capture, regional calling windows, do-not-call list checks, pacing, and full call recording with audit trails. Compliance rules are configured per market and enforced at the orchestrator layer, not inside prompts.
Yes. Multilingual conversational AI is native to the platform. We support 20+ languages with automatic language detection, mid-call switching, and locale-specific TTS voices, which is critical for contact centers serving multiple markets from shared teams.
Through MCP-based, governed integrations with IAM/RBAC, audit logs, and schema contracts. This covers read and write operations, RPA for post-call work, call tracking, and bi-directional sync so every conversation updates the system of record in real time.
It scores leads, predicts churn and call outcomes, and selects the right script, time, and channel per contact. Combined with in-call intelligence, it closes the loop: every call outcome feeds back into models that improve targeting and dialog quality.
Custom engineering built on reusable platform foundations. You own the cloud account, the code, the prompts, and the data. We bring the accelerators: voice stack, orchestrator, observability, evaluation harness, and MCP connectors, so you do not rebuild commodity components.
Yes, with guardrails. We implement HIPAA-aligned controls, strict scope boundaries, and mandatory human-in-the-loop for any clinical decision. Early use cases such as symptom intake, triage routing, and pre-visit questionnaires are production-ready when paired with clinical oversight.
Ready to Move Your Conversational AI From Pilot to Production?
Book a 30-minute, no-obligation technical discovery call. We review your current telephony setup, integration landscape, and target use cases, then share a concrete architecture and 6–8 week MVP plan — whether your priority is inbound containment, outbound call campaigns, or a unified call center voice AI platform.
Discovery call
A 30-minute technical call to review your telephony setup, integration landscape, and target use cases.
Architecture & MVP plan
We share a concrete architecture and a 6–8 week plan for your first production use case.
Build & go-live
We launch a contained, production-grade agent and validate quality, latency, and business impact before scaling.