AI · Generative AI Solutions
Conversational AI & Voice Agents
Conversational AI is the engineering of production chat and voice agents that handle real workflows — support, scheduling, qualification, discovery. These are not chatbots replying with FAQ snippets; they reason, call tools, write to your systems, and hand off cleanly to humans when the workflow demands it. Voice agents ship with telephony, latency budgeting, and barge-in engineered, not bolted on. Senior engineers own the build, India + global delivery.
In short
What is Conversational AI & Voice Agents?
Conversational AI is an engineering engagement that builds production chat and voice agents handling real workflows like support, scheduling, and qualification. Builds typically ship in six to ten weeks. Senior engineers own the work end-to-end, delivered from India with global reach.
What we deliver
Concrete artefacts, not capabilities
- 01
Deployed agent across voice, chat, WhatsApp, or in-product channels
- 02
Tool-calling layer wired into your CRM, scheduling system, and product APIs
- 03
Conversation-level evaluation harness with labelled scenarios in CI
- 04
Human-handoff workflow with transcript and context preserved across the boundary
- 05
Per-intent cost and latency dashboards with channel-level attribution
- 06
Compliance posture aligned to your industry - PCI, HIPAA, or DPDP where relevant
How we work
Engagement phases
Conversation design
We capture the real conversations your users have today - transcripts, recordings, escalation triggers. Edge cases that crash a naive chatbot - silence, interruption, multi-intent turns - are catalogued early. The agent's persona, fallback behaviour, and human-handoff thresholds are decided here, not improvised during the build phase.
Tool integration
The agent talks to your CRM, scheduling system, knowledge base, and product APIs through a tool-calling layer engineered against your auth and tenant boundaries. We instrument each tool call with retries, idempotency where required, and audit logging. The same tools the agent uses are exposed for testing by your internal teams.
Voice or chat hardening
Voice agents land on LiveKit, Twilio, or Vonage with latency budgeting, barge-in handling, and noise tolerance engineered. Chat agents ship with state management, typing indicators, and graceful retry. Both ship with rate limits, prompt-injection mitigations, and audit logging from the first deployment, not after the first production incident.
Evaluation and rollout
The evaluation harness runs labelled conversation scenarios in CI. We roll out behind a feature flag - internal users first, then a slice of production traffic, then full launch. Drift in intent classification, tool-call accuracy, and resolution rate is tracked per cohort. Regressions block deployment automatically until the cause is understood.
Tech stack
What we build on
- OpenAIModels
- AnthropicModels
- LiveKitVoice
- TwilioTelephony
- LangGraphOrchestration
- pgvectorRetrieval
- RedisConversation state
- SentryObservability
- OpenAIModels
- AnthropicModels
- LiveKitVoice
- TwilioTelephony
- LangGraphOrchestration
- pgvectorRetrieval
- RedisConversation state
- SentryObservability
Scope
When this fits and when it doesn't
| This fits when | This doesn't fit when |
|---|---|
| You have a real workflow - booking, support, qualification - that needs handling at conversational scale. | You want an FAQ chatbot - that is a different, much simpler engagement we do not focus on. |
| You want voice or chat with channel-specific engineering, not a generic embeddable widget. | You expect the agent to operate without human handoff for high-risk regulated workflows. |
| Your team can label a few hundred conversations to seed the evaluation harness before launch. | Your compliance posture forbids LLM-generated responses in regulated turns - talk to us first. |
Related work
Shipped engagements
- Live project
AI prompt platform - assistant authoring experience
Built the conversational interface and prompt engineering pipeline that powers grounded assistants for non-technical authors.
View live project - Live project
Operations workflow - multi-intent chat agent
Shipped a tool-calling chat agent that routes approvals and pulls live data from internal systems with audit logging.
View live project
Frequently asked questions
Whichever channel your users already prefer. Voice agents require more upstream engineering - telephony, latency budgeting, barge-in - but reduce friction for phone-first workflows. Chat agents iterate faster and instrument more easily. Most engagements start in one channel and add the other after the evaluation harness is stable in production.
Grounded retrieval, structured tool outputs for anything verifiable, and explicit thresholds for human handoff on low-confidence turns. The agent never invents an order status, account balance, or appointment slot - it calls a tool. Anywhere a tool is unavailable, the agent escalates instead of guessing. The eval harness gates the rest.
Cost depends on model choice, conversation length, and channel. Voice with a top-tier model runs higher than chat with a smaller model. We engineer per-intent model routing - cheaper models for classification, capable models for synthesis - and instrument cost per conversation so finance has live visibility, not surprise invoices.
Yes. Tool integration is half the engagement - the agent talks to your CRM, scheduler, knowledge base, and product APIs through a tool-calling layer engineered against your auth and tenant boundaries. We do not ship agents that live in isolation from the systems your team already runs on.
Tell us what you are building.
We build what large agencies under-deliver and freelancers can't architect, across Web3 protocols, AI agents, and SaaS products. Tell us what you are building. We will tell you how we would approach it, no pitch deck, no fluff, no commitment required.