AI voice agents that hold a real conversation
We build and deploy production voice agents on your phone system or app: real time speech in, a spoken answer back in under a second, and a clean handover to a human when it matters. What it costs, how long it takes, and what you own at the end.
Who this is for
- - A business whose phone line, support desk or booking flow is drowning in repetitive calls and needs an agent that answers, acts and escalates, not an IVR menu.
- - A SaaS or product team that wants voice inside its own app, on its own data, without handing the conversation to a third party platform.
- - An agency or reseller that needs a white label voice agent it can deploy for its own clients.
- - A regulated or multilingual business (healthcare, finance, Indian language markets) where the off the shelf platforms fall over on accents, interruptions and compliance.
What you get
- - A working voice agent on your telephony (SIP, Twilio, your PBX) or embedded in your web and mobile app.
- - Grounded answers from your own knowledge: documents, product data, policies, CRM. It says "I do not know" rather than inventing.
- - Actions, not just answers: booking, lookup, ticket creation, payment status, through your existing APIs.
- - Interruption handling, turn taking and a human handover path, tested against real call recordings before go live.
- - The code, prompts, evaluation set and infrastructure config, in your accounts. See what you should own after an AI build.
What an AI voice agent costs
Two numbers matter: the build, and the per minute run cost. The build is driven by how many intents the agent must handle, how many systems it must act on, and how strict the latency and compliance bar is. The run cost is speech recognition plus the model plus speech synthesis per minute of call, and it can differ by 10x depending on the vendors chosen and whether anything runs on your own hardware. We scope both in the first two weeks and put the numbers in writing. How we price AI work in general: what AI development actually costs and when a fixed price is honest.
How long it takes
Discovery, two weeks: we listen to real calls, map the intents that matter and the systems the agent must touch, and agree the success criteria. Pilot, four to six weeks: one call type, live, on a slice of real traffic, measured against those criteria. Then production hardening and scale out. Each stage is a separate agreement with no minimum commitment. The full shape is on how we work.
Where it runs
On your telephony provider or PBX, in your cloud account (AWS, Google Cloud, Azure), or on premises where data residency requires it. Speech models can run on managed APIs or be self hosted; the trade off is cost and latency against control, and we make it explicit rather than defaulting to whichever vendor is easiest for us.
Where voice agents fail, and what we do about it
Most voice agent demos are impressive and most voice agent deployments disappoint. The gap is four specific problems that only show up on real calls:
- Turn taking. The agent talks over the caller, or waits too long and the caller repeats themselves. Getting this right is an engineering problem, not a model setting. We wrote up how turn taking actually works.
- Accents and languages. Speech recognition that is excellent on American English degrades sharply on Indian English, code switching and regional languages. We test against your callers, not a benchmark. See voice agents for Indian languages and accents.
- Latency. Above roughly half a second of silence the caller assumes the line dropped. The budget breakdown below is how we hit it.
- Handover. An agent that cannot hand a call to a human cleanly, with context, will be switched off within a month. We design the escape hatch first.
How we build a voice agent, end to end
Voice AI transforms sound into understanding, then reasoning into natural speech - creating seamless conversational experiences.
The Voice AI Pipeline
End-to-end flow from microphone to speaker. Click each stage or watch it auto-advance.
AUDIO INPUT
Microphone capture, noise suppression, VAD (voice activity detection)
SPEECH-TO-TEXT
NLU
REASONING
TEXT-TO-SPEECH
AUDIO OUTPUT
How Machines Understand Speech
From raw audio waves to text - the ASR pipeline transforms physical sound into digital language through multiple processing stages.
ASR Processing Pipeline
WAVEFORM
Raw audio signal - amplitude over time
SPECTROGRAM
Frequency decomposition using FFT - visual representation of sound
FEATURES
Mel-frequency cepstral coefficients (MFCCs) - compact acoustic features
ACOUSTIC MODEL
Neural network (Transformer/Conformer) maps features to phonemes
LANGUAGE MODEL
Contextual decoding - chooses most probable word sequence
TRANSCRIPT
Final text output with punctuation and formatting
Spectrogram Visualization
ACOUSTIC FEATURESNoise Handling
Spectral subtraction and neural denoising remove background noise while preserving speech characteristics.
Accent Adaptation
Fine-tuned acoustic models adapt to regional accents, speaking styles, and speech patterns in real-time.
Multi-Speaker
Speaker diarization separates overlapping speakers, enabling meeting transcription and conference calls.
Natural Language Understanding
After transcription, the NLU layer extracts meaning - what does the user want, and how do they feel about it?
Intent Classification
Determines what the user wants to accomplish. Maps utterances to predefined action categories.
Entity Extraction
Identifies key pieces of information: names, dates, numbers, locations, product IDs.
Sentiment Analysis
Evaluates emotional tone - positive, negative, neutral, frustrated. Adjusts response style.
Context Tracking
Maintains conversation state across turns. Resolves pronouns, references, and implicit meaning.
Response Generation
The AI crafts responses using conversation context, user history, and knowledge - then synthesizes them into natural speech.
Context Layers for Response
LLM Reasoning
Foundation model processes full conversation context, knowledge, and constraints to generate an appropriate response.
Session Context
Current conversation history, user intents, extracted entities, and active tasks provide immediate context.
User History
Past interactions, preferences, and profile data personalize responses and anticipate needs.
Knowledge Base
RAG-retrieved documents, FAQs, product data, and policies ground responses in verified facts.
Voice Synthesis
Modern neural TTS goes far beyond robotic speech.
Neural TTS
Deep learning models produce natural prosody, rhythm, and emphasis - far beyond robotic concatenative systems.
Emotion Control
Adjustable emotional tone: empathetic for complaints, enthusiastic for promotions, calm for technical support.
Voice Cloning
Custom voice profiles from minimal audio samples. Brand-consistent voice identity across all touchpoints.
Multilingual
Seamless language switching within a single conversation. Code-mixing support for bilingual users.
Real-Time Architecture
Voice AI demands sub-500ms total latency. Every millisecond is budgeted across the pipeline.
Latency Budget Breakdown
Streaming Transcription
Words appear as spoken - no waiting for utterance completion. Partial results enable early processing.
Barge-In Detection
User can interrupt the AI mid-response. System detects new speech, stops TTS, and processes the interruption.
WebSocket Architecture
Bidirectional real-time audio streaming. Low-overhead binary frames for audio, JSON for control signals.
Edge Preprocessing
Noise suppression, VAD, and initial feature extraction happen on-device to reduce network latency.
WebSocket Real-Time Flow
Client Device
Browser / Mobile App
Voice AI Server
ASR + NLU + LLM + TTS
What is an AI voice agent?
An AI voice agent is software that holds a spoken conversation with a person over the phone or inside an app: it listens, understands what the caller wants, takes an action or finds an answer in the business's own systems, and replies in natural speech, in under a second, so the exchange feels like talking to a capable person rather than pressing options in a menu. Some vendors call the underlying capability voice intelligence; the agent is the deployed, working version of it.
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