voice & chat support bots

Customer support that answers instantly, on the phone and in chat.

Customer-facing support agents that handle real conversations by voice and by text — answering questions, resolving routine issues, and escalating to your team with full context when a human is genuinely needed. I solely built Rubricate, an entire agentic AI voice-interview platform running adaptive real-time voice conversations at scale — not a component of one, the whole thing.

01the problem

Sound familiar?

If any of these describe where you are right now, this is the right page.

Support tickets pile up overnight and customers wait until morning for simple answers.

Your phone line goes to voicemail outside working hours, and those callers don't call back.

Agents spend most of their day on the same handful of routine questions.

Scaling support means hiring, and hiring is slower and costlier than the volume justifies.

02deliverables

What you get

Concrete outputs, not a vague statement of work.

  • A voice agent that answers your phone line, understands callers, and responds naturally
  • A chat agent on your site and messaging channels sharing the same knowledge base
  • Real-time speech recognition and natural speech output with low conversational latency
  • Escalation to a human with the full conversation transcript and context attached
  • Integration with your CRM or helpdesk so conversations are logged where your team works
  • Call transcripts, summaries and sentiment surfaced in a dashboard
  • 24/7 coverage with defined boundaries on what the agent will and won't handle

// built with

Voice AIPythonAgentic AISpeech-to-TextText-to-SpeechRAGTwilioWebSockets
03process

How this project would run

Five stages, so you always know where things stand and what happens next.

01

Discover

We map your real support volume — which conversations are routine, which always need a person, and what escalation should look like.

02

Design

Conversation flows, tone and escalation rules designed and agreed before build, including how the agent introduces itself.

03

Build

Built against your knowledge base and tested on recorded real conversations, tuning latency and accuracy until it feels natural.

04

Ship

Launched on a subset of traffic — after-hours calls or one channel first — then expanded as it proves out.

05

Support

Weekly transcript reviews in the early period, tuning flows and knowledge where conversations went sideways.

04proof

Related work

Projects where I've done exactly this kind of build.

Rubricate

rubricate.net

live

An agentic AI voice-interview and evaluation platform — the entire platform, built solo — that vets domain experts at scale with adaptive voice interviews backed by a full automated evaluation pipeline. The expert network behind Dataclap.

  • Agentic AI voice interviews that adapt to each candidate in real time
  • End-to-end automated evaluation pipeline with scoring rubrics
  • Automation workflows for sourcing, screening, and expert onboarding
Agentic AIVoice AILLM EvalsPythonAutomation

Agentic RAG Chatbot

A local agent-driven RAG system that combines LLM capabilities with tool integrations to deliver accurate, contextual AI responses without relying on external APIs.

  • 5-step reasoning pipeline for transparent, auditable reasoning
  • Local AI system with tool integrations for contextual responses
  • No dependency on external APIs
PythonLangChainRAGLLM
05faq

Voice & Chat Support Bots — questions

// next step

Need voice & chat support bots?

Book a 15-minute call and tell me what you're trying to build. You'll get a straight answer on whether I can help, what it would take, and roughly what it would cost.