AI agents & chatbots
Hire an AI chatbot and voice agent developer
I build retrieval-augmented assistants and real-time voice agents that answer from your data instead of hallucinating — grounded retrieval, evaluated prompts, and latency low enough for a live conversation.
- RAG assistants on LangChain with Pinecone vector search
- Sub-second voice agents built on the LiveKit SDK
- Snowflake and warehouse-backed historical context
- Streaming responses, tool calling and guardrails
What you get
RAG knowledge assistants
Ingestion pipelines, chunking strategy, embeddings and retrieval tuning so answers cite your documents rather than inventing them.
Real-time voice agents
LiveKit-based agents with speech-to-text, LLM reasoning and text-to-speech stitched into a single low-latency loop, plus transcripts and call analytics.
Support and sales automation
Agents that triage tickets, qualify leads or run practice sales conversations, with handoff rules to a human when confidence drops.
Evaluation and cost control
Answer-quality evals, prompt versioning, caching and model routing so quality is measured and spend stays predictable.
Tools I use for this
Work I've shipped in this space
Enterprise sales practice agent
Voice-calling AI built on the LiveKit SDK that lets sales teams rehearse live conversations against a realistic buyer persona.
LangChain + Pinecone assistant
Enterprise chatbot at Peaklyft grounded in company documents, with Snowflake supplying historical business context.
Agent chatroom write-up
Published engineering breakdown of building an AI-powered agent chatroom with LiveKit and React.
How we'd work together
Data audit
We look at what content the agent must know and how clean it is — that decides retrieval quality more than the model does.
Prototype
A working agent on a slice of your data within days, so you judge answers instead of architecture diagrams.
Evaluate and tune
A question set with graded answers drives chunking, prompt and retrieval changes.
Deploy and monitor
Production deployment with logging, cost tracking and a feedback loop for bad answers.
Questions people ask first
Can the chatbot answer from our internal documents?+
Yes — that's the standard build. Your documents are chunked, embedded into a vector store and retrieved per query so answers stay grounded and citable.
How fast can a voice agent respond?+
With LiveKit and streaming models, a well-tuned pipeline lands under roughly a second of perceived latency, which is what makes a call feel natural.
Which model providers do you use?+
Whatever fits the accuracy, latency and budget target — OpenAI, Gemini and open models all work. The pipeline is built so the model can be swapped.
Can it integrate with our CRM or support desk?+
Yes. Tool calling lets the agent read and write to CRMs, ticketing systems and internal APIs, with permissions scoped per action.
Have a project in mind? Send the brief.
A short description of the outcome you need is enough. You'll get an honest answer on feasibility, timeline and cost — usually within a day.