The system works.
Now we make it smarter and more useful.

What can be built?

• An AI agent inside the system
• A chat that answers from your documents
• Search and analysis with RAG
• New screens and workflows
• A small system for management or automation

What is RAG?

Connecting AI to information that belongs to your business — documents, data or a knowledge base — so answers are based on your content, not just general knowledge.

What else is included?

A review of the system, checking permissions and data, building the integration, handling issues, testing and preparing for day-to-day use based on the current state.

What affects the price?

The quality of the existing code and data, the number of information sources, the complexity of the agent and the integrations required. The final price is set after a short review.

Review of the existing system

I go through the code, the data structure and the permissions to understand what already exists and what will work against us. This review is deliberately short — the goal is to know what we are building on, not to produce a report.

Defining what AI actually solves

Not everything needs a model. We mark the places where AI saves real time — triaging requests, searching documents, drafting replies — and the places where a plain rule does the job better and cheaper.

Integration and development

We build the integration inside the existing system: indexing your data, the entry points in the interface, permissions and logging. Without rewriting what already works.

Testing and rollout

We test on real cases from your own system, not on samples. We tune the answers, define when the system hands off to a human, and roll out gradually.

Do we need to rewrite the system to add AI?

In most cases, no. If there is access to the data and the code, an AI layer can sit on top of the existing system. A rewrite only enters the conversation when the current structure blocks what you want to add — and in that case I say so upfront.

How much data do we need for RAG?

Less than people assume. Even 50 to 100 relevant documents are enough for good answers, as long as they are organized and current. Data quality affects the result far more than volume.

What if the existing code is messy?

That is the normal case, not the exception. I work with systems built in Lovable, Base44 or by previous developers. If the state of the code affects the timeline or the price, you will know after the initial review and before we start.

How long does it take?

Adding AI to an existing system usually takes 2 to 6 weeks, depending on the number of data sources and integration points in the interface. An agent that performs actions inside the system sits at the high end.