AI agents and chatbots wired into the product you already have
The hard part of an AI feature is rarely the model. It is connecting it to your real data and systems, so the chatbot answers from your documentation instead of guessing, or the agent can actually look up an order, update a record or file a ticket, safely.
That is integration work, and integration work is what I have done for 10 years: payments, GPS fleets, device management, video platforms, browser automation. I build AI agents and chatbots the same way, as part of your existing Laravel or Node.js product rather than as a demo that lives on its own.
What I build
Support and knowledge chatbots
Assistants that answer from your own documents, help articles and product data using retrieval-augmented generation (RAG), and hand off to a human when they should.
Workflow agents
Agents that use tools: they call your APIs to look up records, draft replies, classify incoming requests, update your CRM or kick off a process, with you deciding what they are allowed to touch.
AI features inside your app
Summaries, smart search, extraction from documents and emails, and drafting tools built into the screens your users already work in.
Automation pipelines
Background jobs that combine LLMs with scraping, data clean-up and integrations to take repetitive work off your team, in the same way I have automated card operations and order management for clients.
How I keep AI features dependable
Grounded in your data
The model answers from content and records you control, retrieved at the moment of the question, so answers stay current and can point to their source.
Narrow permissions
An agent gets the smallest set of actions it needs, through the same permission checks your app already enforces. Anything risky, like refunds or deletions, waits for human approval.
Measured, not guessed
Before launch we agree on a set of real example questions and tasks, and test against them every time the prompts, model or data change. Usage, cost and failure cases are logged so you can see how it is doing.
Proof
Integration-heavy systems that show how I connect software to real operations:
How pricing works
AI work usually starts with a small, fixed-price first milestone: one well-defined use case, connected to your real data, in front of real users. That shows quickly whether the feature earns its keep before you invest further.
From there, further milestones or a monthly retainer cover new use cases and ongoing tuning. Model usage is billed by the AI provider straight to your account, so you always see the real running cost. Either way, nothing starts until the scope and price are in writing and you have approved them.
Questions
Which AI models do you use?
I work with the major model providers through their APIs and pick per use case, based on quality, speed and cost. Your keys and billing stay in your account, and the code is written so the model can be swapped later without a rewrite.
Can a chatbot answer questions about our own product and data?
Yes. That is what retrieval-augmented generation is for: your documents and records are indexed, the relevant pieces are fetched for each question, and the model answers from them. It can be limited to only answer from that material.
Is it safe to let an AI agent take actions in our system?
It can be, if it is built carefully. Agents get only the actions they need, go through your existing permission checks, and anything with real consequences can require human approval first. Every action is logged.
Our product is built in Laravel or Node.js. Do we need to change stack?
No. AI features are added as part of your existing application, through the provider APIs, so they share your authentication, data and deployment.
Have an AI use case in mind?
Book a free 30-minute call. Within 48 hours you'll have a written plan with scope, timeline and a fixed price.