Voice AI
We build low-latency voice agents for real telephony workflows, including outbound qualification and multilingual support operations.
Where the operational bottleneck appears
- High-volume outbound calling for lead qualification and reminders is expensive to staff, and coverage is hard to scale predictably as call volume changes week to week.
- Teams in non-English or mixed-language markets face lower quality from off-the-shelf voice tools, especially when conversations switch between languages in the same call.
- Many demos do not hold up in production because latency is too high for natural dialogue or because the system cannot reliably place and manage concurrent calls through compliant telephony providers.
We build voice agent systems as production pipelines rather than single-model demos. Telephony integration handles real inbound and outbound calls; speech-to-text is selected and tuned for the target language profile, including code-mixed Hindi and Hinglish where needed; LLM logic manages conversation flow and qualification state; and text-to-speech returns responses that sound natural within strict latency budgets. The architecture is streaming end to end so partial transcripts and responses are processed continuously instead of waiting for full request-response cycles. We include orchestration primitives for retries, scheduling, and concurrent call queueing, plus monitoring dashboards for outcomes and transcript review. This is the pattern we have used for outbound real-estate lead qualification, where operational reliability and language handling determine conversion quality. The result is a system that can run at scale with measurable call outcomes, not an isolated proof-of-concept call bot.
sequenceDiagram
participant C as "Caller/Callee"
participant E as "Telephony Provider (Exotel)"
participant D as "STT (Deepgram)"
participant B as "Backend Orchestrator (FastAPI)"
participant G as "LLM (Gemini)"
participant T as "TTS (Cartesia)"
participant N as "Frontend Dashboard (Next.js)"
B->>E: Trigger call
E->>C: Connect call
C->>E: Audio stream
E->>D: Stream audio
D->>B: Live transcript
B->>G: Transcript + conversation state
G->>B: Response text
B->>T: Generate response audio
T->>E: Stream TTS audio
E->>C: Play response
loop until call ends
C->>E: Continue speaking
end
B->>N: Log outcome + transcriptThe call loop is streaming to keep round-trip latency low enough for natural turn-taking. Conversation state and high-concurrency call orchestration are managed through Redis and Arq so retries and active sessions remain reliable at volume.
Stack selected for this delivery pattern
In practice: We built a self-hosted outbound AI voice calling platform for real-estate lead qualification with Hindi and Hinglish conversation handling. Calls are initiated through a telephony provider, and audio is transcribed in real time so the system can interpret intent and eligibility signals as the lead speaks. Conversation logic follows a defined qualification flow, asks follow-up questions, and produces spoken responses through low-latency text-to-speech. When a lead meets handoff criteria, outcome data is captured and routed to the human sales team with transcript context. Queueing and retry orchestration are handled through Redis and Arq so campaigns can run many concurrent calls without dropping state. This allowed the operations team to scale outbound coverage while keeping call quality in language patterns that generic providers often handle poorly. Instead of manual first-touch calls for every lead, staff focus on warm handoffs that are already qualified against business criteria.
Service-specific answers
Yes — we've built this specifically for Hindi/Hinglish (code-mixed) conversations; the architecture is language-agnostic given the right STT/TTS providers.
The pipeline is architected for low round-trip latency (streaming STT/LLM/TTS) specifically because latency is what makes voice AI feel robotic — we optimize for this, not just for demo quality.
Yes — call outcomes, transcripts and qualification data are written back to your CRM or system of record via API.