AI Development for Businesses: Assistants, Integrations

What AI development really means

AI development connects a language model to your data, rules, and existing systems. The goal is for an employee to find the answer they need faster and check its source.

The solution searches your materials, prepares an answer, and shows its source. When a person needs to decide, the decision stays with them.

One request journey: question → search of your documents and databases → model drafts an answer → source references → human approval or automated action.

AI knowledge base: answers from your own documents

An AI knowledge base is retrieval plus a model working on your material: contracts, guides, price lists, intranet, database rows. The model does not answer from memory — it searches your documents first and shows the source behind every claim.

What decides whether it works in practice: search has to handle Estonian cases and compound words, not only exact matches; every answer needs an owner, meaning the person accountable when the answer is wrong; and access has to come from your existing permissions, not a second list.

What goes in: approved documents, not the drafts folder.
What stays out: personal data and secrets the asker has no right to see.
How I measure it: a set of test questions with the right answers agreed before work starts.

A typical first version is an internal assistant that answers repeated questions and routes the exception to a person. For the public version see the website AI chatbot; for workflow automation see AI agents for businesses.

What I build

I start with one specific task and build the solution it needs.

RAG assistant

Answers based on your documents, guides, price lists, and intranet. Each claim includes a reference; answers are not guessed.

AI agents

Handle defined workflows: categorise requests, draft text, find data, and escalate exceptions to a person.

Integrations

Connections to CRM, email, databases, APIs, and document storage. The solution lives in your systems, not on a separate island.

Evaluation and monitoring

A metric written down before work begins, a set of test questions, logs, and cost tracking. Otherwise, no one knows whether the solution works.

When AI development is worthwhile—and when it is not

Good candidate

The same question or search recurs every week, and the answer is in your documents.
The data exists, even if it is a messy pile of PDFs and spreadsheets.
The result can be measured: response time, share of requests handled, or number of drafts created.
Human review can limit errors, and the consequences are not irreversible.

Wrong place to start

The data is missing or exists only in people's heads—it needs to be documented first.
A decision affects money or rights in a way that makes mistakes unacceptable even with human review.
The need comes up a few times a year; it is simpler to do it manually.
The goal is to “use AI,” rather than solve a specific bottleneck.

How the work proceeds

Assessment

I review one workflow with you: where time is spent, what data exists, who may see it, and what can be measured. I provide a written plan with a price.
Pilot

I build a working version using your data for a limited group of users. I show progress and measure the agreed result.
Production or handover

After the trial, we agree on the next step. I can maintain the solution or hand the code, settings, and instructions to your team.

Data, permissions, and costs

If the solution uses sensitive information, we agree in advance who has access and how I handle the data.

Role-based access: each user sees only the documents they are permitted to see.
Data is not used for training: your documents are not shared with third parties or used to train models.
Log-free mode: for sensitive processes, request content can be left unstored.
Cost limits: model and search costs are visible and can be capped before the bill comes as a surprise.

Price and schedule

Assessment · €0One conversation and a written pilot proposal with a metric.
Pilot · fixed priceDefined work using your data, with the price stated before starting.
Time · 1–2 weeksA typical pilot for one workflow and a limited group of users.
Maintenance · by agreementMonitoring, updates, and support if you need them.

Frequently asked questions

What is an AI knowledge base?

An AI knowledge base is retrieval plus a model working on your own material: contracts, guides, price lists, intranet, database rows. A question is answered from your documents with the source shown, not from the model's memory.

What is the difference between AI development and adopting an AI tool?

A ready-made chat window answers general questions. With AI development, I connect the model to your materials and systems. The assistant searches your documents, shows its source, and routes the question to a person when the answer needs checking.

How long does it take to build an AI solution?

A pilot is usually ready in 1–2 weeks when the data is available and the use case is defined. A larger integration may take longer. I put the price and schedule in writing before work begins.

Which models and services do you use?

I choose the model based on the task, language, data sensitivity, and cost. I connect it to your systems where needed and check answers before putting it into use.

Who maintains the solution after the pilot?

If the pilot works, we agree whether I maintain the solution or hand the code, settings, and instructions to your team.

AI development: assistants, agents, and integrations