Voice AI Development
Voice AI agents and speech systems for calls, transcription and voice interfaces with natural, low-latency conversation.
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Division
Service area
Generative AI & LLM Applications
Engagement
Project · Team · Managed
Overview
Voice AI combines speech recognition, language models and speech synthesis into systems that listen, understand and respond in real time. We build voice agents for inbound and outbound calls, real-time transcription and call analytics, and voice interfaces for applications — engineered for low latency, interruption handling, telephony integration and accurate handoff to human agents when needed.
Common use cases
- Inbound call agentAnswers common calls, authenticates callers and completes simple requests.
- Appointment and reminder callsOutbound confirmations and rescheduling.
- Call transcription & analyticsSummaries, sentiment and compliance checks for every call.
- Voice-enabled appsHands-free interfaces for field, driving or accessibility scenarios.
Quick answers
Voice AI Development at a glance
The essentials in brief. Every project is scoped individually — ask us for specifics.
- What is voice AI development?
- Voice AI agents and speech systems for calls, transcription and voice interfaces with natural, low-latency conversation.
- Who is it for?
- Typically product companies adding AI features, enterprises automating knowledge work, and teams whose AI pilot needs to become a dependable production system.
- What does Shivacha provide?
- Speech recognition
- Real-time orchestration
- Natural speech synthesis
- Telephony integration
- Handoff & escalation
- Compliance features
- Which technologies are used?
- Speech AI, Large Language Models, Python, WebSockets, OpenAI Models, Retrieval-Augmented Generation — chosen to fit your stack and constraints.
- How does the process work?
- Define the job → Build the evaluation set → Prototype retrieval and prompts → Harden for production → Roll out and learn.
- What affects the cost?
- Number and quality of data sources
- Accuracy targets and evaluation effort
- Integrations with business systems
- Model choice, hosting and per-request cost
- Human-in-the-loop and audit requirements
- Data residency and privacy constraints
- How long does it take?
- A scoped proof of value usually takes 4–8 weeks; a production system with evaluation, integrations and guardrails typically takes 3–6 months.
- How do I get started?
- Share a short brief in the form below, book a 30-minute call or message us on WhatsApp. A senior engineer replies within one business day; NDA on request.
Capabilities
What we deliver
Speech recognition
Accurate transcription tuned for accents, domains and noise.
Real-time orchestration
Streaming pipeline for low-latency turn-taking.
Natural speech synthesis
Voice selection and pronunciation control.
Telephony integration
SIP and cloud telephony connectivity.
Handoff & escalation
Warm transfer to agents with context.
Compliance features
Consent prompts, redaction and recording policies.
Architecture
Engineered right from day one
The layers we typically design for generative AI & LLM applications, adapted to your stack and partners.
- Data boundariesDocument-level permissions are enforced at retrieval time so users only see answers from content they may access.
- Grounding & citationsAnswers link to their sources so users can verify, and out-of-scope questions are declined.
- Model portabilityAn abstraction layer lets you swap or mix model providers without rewriting the application.
- Unit economicsSemantic caching, prompt compression and model routing keep per-request cost predictable.
Delivery
How an engagement runs
- 1
Define the job
Which questions or tasks, for which users, with which data — and what a correct answer looks like.
- 2
Build the evaluation set
Representative questions with expected answers, used to score every iteration.
- 3
Prototype retrieval and prompts
A working slice against real documents, measured against the evaluation set.
- 4
Harden for production
Access control, guardrails, fallbacks, caching, streaming and observability.
- 5
Roll out and learn
Staged rollout, feedback capture and scheduled re-evaluation as content and models change.
Security
Security built into delivery
Controls we apply by default on this kind of work — not a separate phase at the end.
Data boundaries
Permission-aware retrieval so users only see answers from documents they may access.
Guardrails
Input and output checks, tool allow-lists and human approval for consequential actions.
No training on your data
Provider settings and contracts chosen so your data is not used to train third-party models.
Audit trail
Prompts, sources, tool calls and approvals logged for review.
Technology
Tools we use for this
Products
Start from a platform
Shivacha Voice AI
Natural, low-latency voice agents for inbound and outbound calls.
Learn moreShivacha Copilot Kit
Embed a context-aware AI copilot into your product or internal tools.
Learn moreShivacha AI Support
AI support that resolves, not just responds — grounded in your knowledge and systems.
Learn moreRelated services
Often combined with
Generative AI Development
Generative AI applications for text, documents, code and media — grounded in your data and engineered for production reliability.
Learn moreLLM Development
LLM application and platform engineering: model selection, fine-tuning, serving, evaluation and cost-optimised inference.
Learn moreRAG Development
Retrieval-augmented generation systems that ground LLM answers in your documents with permissions, citations and measurable accuracy.
Learn moreDedicated team
AI Engineering Team
AI engineers who build production LLM applications, RAG systems and AI features.
Work & insights
Related thinking
Permission-aware enterprise knowledge assistant
How we design a RAG assistant that answers from thousands of internal documents while respecting every user's access rights.
Learn moreMulti-tenant SaaS foundation built for enterprise readiness
The foundations we put in place when building a SaaS product that will need to sell to enterprises.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn moreFAQ
Frequently asked questions
How natural do voice agents sound?
Modern speech synthesis is highly natural. The bigger factor is latency and turn-taking, which we optimise with streaming architectures.
Can voice agents handle multiple languages?
Yes, with language detection and multilingual models; quality varies by language, so we test with your target languages and accents.
Should we fine-tune or use retrieval?
For most knowledge-grounded use cases, retrieval-augmented generation is the right starting point: it is cheaper, keeps knowledge current and supports citations. Fine-tuning helps with style, format, domain vocabulary or narrow classification tasks, and is often combined with retrieval.
How do you measure LLM quality?
With task-specific evaluation sets scored for correctness, groundedness, completeness and format, using a mix of automated checks, model-graded evaluation and human review for high-stakes outputs.
Next step
Build Your AI Product.
Tell us about your voice AI development requirements — goals, timeline and constraints. We will reply with questions, an approach and next steps.
- Senior engineer reads every enquiry
- Reply within one business day
- NDA on request
Your details are used only to reply to this enquiry.