AI agents that do real work, not demos
Custom voice agents, chatbots, and RAG assistants wired into your CRM and workflows via n8n, qualifying leads, booking calls, and answering questions 24/7. Built in 2-4 weeks.
Top Rated · 100% Job Success · 5.0 ★ · 22 Upwork contracts
Quick answer
Yes, I build custom AI agents (Vapi.ai voice agents, lead-qualification chatbots, and RAG assistants) wired into your CRM and workflows via n8n, typically in 2-4 weeks. Pricing is $60/hour (5-hour minimum) or a fixed price from $2,500, with honest scoping on whether an agent even beats a plain workflow.
Most “AI agents” are 90% marketing
The other 10% is function. A chatbot that can't touch your calendar, CRM, or knowledge base is a toy. The agents I build are wired into your actual stack: they read your data, take real actions in your tools, and hand off to a human the moment it matters. If AI is the wrong tool for your problem, I'll say so on the first call.
Agents with jobs, not gimmicks
Voice agents
Vapi.ai voice agents that qualify and book callers, covered in depth on the dedicated AI voice agents page (linked below).
Lead-qualification chatbots
Chat agents that ask the right questions, score the lead, and route hot ones straight to booking, before your competitor replies.
RAG knowledge assistants
Agents grounded in your docs and data. Answers come from your knowledge base, with sources, instead of guessing.
AI content pipelines
Brief → AI generation → SEO optimization → publishing → distribution, orchestrated end to end in n8n.
Workflow-embedded AI
OpenAI and Claude steps inside n8n workflows to summarize, classify, extract, or decide, where fixed rules alone fall short.
Guardrails & human handoff
Clear escalation paths, logging, and fallbacks, so the agent helps your team instead of embarrassing it.
Follow one chat through the agent
This is the lead-qualification agent I get asked to build most. I have mapped it end to end, from the first inbound message to a booked call, so the moving parts are out in the open rather than hidden behind a vague promise. Watch one visitor go from “just looking” to a confirmed slot in your CRM.
- 1
An inbound message arrives
A visitor types into the chat widget and a Webhook node fires into n8n, opening a session that tracks the conversation and the contact behind it.
- 2
The agent qualifies
Driven by an OpenAI or Claude step, the agent asks the two or three questions that actually decide fit (budget, timeline, use case) and parses free-text replies into clean, structured fields.
- 3
RAG grounds every answer
When the visitor asks about pricing, scope, or how you work, a retrieval step pulls the matching passages from a vector index of your docs, so the reply comes from your knowledge base with sources, not a guess.
- 4
Score and classify the lead
An OpenAI or Claude node inside n8n rates the collected answers against your criteria and returns a score plus a label (qualified, nurture, or unclear), along with a confidence value.
- 5
Book, write back, or escalate
Qualified leads get offered a live calendar slot and the contact, score, and transcript are written into your CRM. Low-confidence cases route straight to a human with the full context attached.
On a documented lead-qualification and booking build, this cut the time to qualify and book an inbound lead from 2-3 hours to under 10 seconds. See the lead-qualification case.
Where an agent earns its place
If the job involves messy language, your own documents, or a decision a fixed rule cannot make cleanly, an agent fits. These are the jobs I hand to an agent most often:
- Lead-qualification chatbots that ask, score, and book hot leads into your calendar
- RAG assistants that answer product, pricing, and policy questions from your own docs with sources
- Support triage that reads an incoming ticket, tags it, drafts a reply, and escalates the hard ones
- Inbound email and form handling that classifies intent and routes it to the right pipeline stage
- Knowledge assistants for your team, grounded in internal wikis, SOPs, and past tickets
- OpenAI or Claude steps inside n8n that summarize calls, extract fields, or classify records
- Content pipelines that turn a brief into a draft, then optimize and queue it for review
- First-reply agents that answer common questions instantly and hand off the moment a human is needed
From idea to working agent in 2-4 weeks
Scope the job
We define the one job the agent must do well and the metric that proves it. Fixed scope within 48 hours.
Build & test
I build the agent and test it against real conversations and real data, then tighten the prompts and guardrails.
Handover, you own it
Prompts, workflows, and integrations live in your accounts, fully documented, with a walkthrough call. No lock-in.
AI-first, engineering-grounded
- Claude Code, OpenAI API, and Vapi.ai in production work, not weekend experiments.
- 100% Job Success and 5.0 rating across 22 Upwork contracts.
- Agents plugged into your CRM, calendar, and workflows via n8n, not chat widgets bolted on the side.
- Honest scoping: if a plain workflow beats an agent for your case, that's what I'll recommend.
Common questions
How much does a custom AI agent cost?
Work runs $60/hour with a 5-hour minimum, or a fixed price from $2,500. A lead-qualification chatbot sits at the lower end. A voice agent wired into calendars and CRM takes more. You get an exact quote within 48 hours of a free discovery call.
Do you build AI voice agents too?
Yes. Voice agents have their own considerations (telephony, latency, interruptions), so they get a dedicated page. This page covers chatbots, RAG assistants, and workflow-embedded AI; for voice specifically, see the AI voice agents service linked below.
Will the agent say something wrong to my customers?
That risk is exactly what the guardrails are for: agents are grounded in your documents (RAG), limited to defined actions, logged, and given clear escalation paths. Nothing goes live before it's tested on real conversations.
Do I need an AI agent, or a plain automation?
If the job follows fixed rules, a plain n8n workflow is cheaper and more reliable, and I'll tell you that on the first call. Agents earn their keep where inputs are messy: conversations, documents, qualification, support.
Which models and tools do you build with?
OpenAI and Claude for language, Vapi.ai for voice, and n8n as the backbone that connects the agent to your CRM, calendar, and data. RAG grounding runs on your own knowledge base.
Who owns the agent after handover?
You do. Everything ships in your accounts, documented well enough for any future developer to pick up. There's no dependency on me to keep it running, though a retainer is available if you want ongoing tuning.
What does it cost to run the agent once it is live?
Beyond the build, your only running cost is the model usage, typically OpenAI or Claude API tokens billed by volume, plus hosting for n8n and the vector index. For a lead-qualification chatbot that is usually a small monthly figure, and I size it for you before we start so there are no surprises.
How much of my content do you need for the RAG assistant?
Whatever grounds the answers you care about: help docs, a pricing page, past support replies, PDFs, or a knowledge base export. I clean and index it into a vector store, and when your content changes we re-sync so the agent never quotes something out of date.
Where can the chatbot live once it is built?
On your website as a chat widget, and it can also run inside channels like WhatsApp, Instagram, or a web form, since n8n connects to each over its API. The same qualifying logic and RAG grounding run underneath, so behavior stays consistent wherever a visitor reaches you.
Put an agent on it
Tell me the conversations or decisions eating your team's time. On a quick call I'll tell you honestly whether an agent can own them, and what it takes to build one that does.