AI agents that actually do the work.
From focused voice receptionists that book appointments to stateful multi-agent systems connected to your CRM, databases, and internal workflows.
What I can build.
- ✦AI Phone Receptionists (24/7 inbound call answering & scheduling)
- ✦Conversational Lead Qualification & Intent Scoring
- ✦Appointment Booking & Calendar Synchronization
- ✦Customer Support & Order Lookup Copilots
- ✦Automated Outbound Follow-up Agents
- ✦Internal Operations Assistants
- ✦Action-Taking Agents with Custom Tool Integrations
What you get.
- Working voice or chat software configured to your business domain
- Clean integration with your calendar, CRM, and databases
- Human escalation gates so staff review ambiguous cases
- Guardrails preventing hallucinations, price errors, or off-topic responses
- Automated evaluation suites testing response accuracy on every change
From fast build to complete system.
You don't always need a massive enterprise build. We scope the software to match your exact timeline and stage.
Focused Working MVP
A useful voice receptionist or chat booking bot live in 1–2 weeks using optimized platforms (Retell, Twilio, Deepgram).
Production Infrastructure
Multi-agent directed state graph (LangGraph) + persistent PostgreSQL checkpoints + Model Context Protocol (MCP) tool registry + fallback LLM gateways + admin dashboard + telemetry.
Keep your current tools. I'll build around them.
Zero missed calls, instantaneous qualification, and automated CRM updates.
Relevant working systems.
Real implementations that demonstrate this capability in production.
RealEstate AI Sales OS
Stateful multi-agent workflow engine that orchestrates the real estate sales lifecycle through a directed graph with checkpoint-based persistence, MCP tool abstraction, and human-in-the-loop interrupts.
A production-grade multi-agent system demonstrating stateful workflow orchestration with checkpoint persistence, MCP tool abstraction for integration decoupling, LLM reliability engineering (structured outputs, model fallback, guardrails), and automated evaluation—reducing per-workflow LLM calls by 40% through agent consolidation and deterministic routing.
Inbound Lead Engine
Production-grade async lead processing with AI intent inference and SLA enforcement.
Full-stack containerized environment. Zero ingestion wait-time. Automated state escalation for hard failures.
Under the hood.
Architectural details and engineering decisions behind this system.
Durable State Machines (LangGraph)
State persistence via append-only PostgreSQL snapshots enables pause/resume across process restarts and human review cycles.
Deterministic routing predicates eliminate redundant LLM calls on deterministic paths, cutting token costs by 40%.
Human-in-the-loop interrupts trigger before critical side effects when qualification confidence falls below threshold.
Model Context Protocol (MCP) Tool Registry
Decoupled JSON-RPC tool interface separates agent reasoning prompts from database client libraries.
Swap CRMs or internal databases with zero modifications to agent prompts or orchestration logic.
Idempotency keys on all tool invocations prevent duplicate writes on network drops.
Have a use case for ai agents?
Tell me what you're trying to solve. I'll outline the architecture and build a working system quickly.