# Objective

Make the imported legacy content not the final migration result, but the raw material for systematic AI editing.

## Basic Scenario

The AI agent receives:

- original record data from the legacy DB;
- the newly created Concept;
- general project context and rules.

After that, the agent updates the Concept content: reworks the structure, formatting, and presentation while preserving confirmed original facts.

## What to Do

- Define a unified interface for AI processing of Concepts.
- Pass the original legacy data to the agent as factual context, and the new Concept as the editable result.
- At the first stage, work primarily with `content`, without complicating the DB schema with new fields for specific editorial structures.
- Allow the agent to structure the material, add formatting, and internal linking.
- Prohibit inventing unverified facts and commercial results.
- Add result validation before saving: markup correctness, validity of internal links, and other deterministic checks.
- Make the processing repeatable and suitable for mass execution across the imported database.

## Result

After migration, old content is systematically transformed into a modern knowledge base without manual rewriting of every page and without expanding the schema for every type of material.

## Definition of Done

- A single AI pipeline is applicable to different types of Concepts.
- Original facts are preserved.
- The result passes programmatic validation before saving.
- The imported database can be processed in bulk.
- No separate AI code is required for each legacy table.

Following the initial migration of legacy data, automatically reprocess Concepts: structure and format content, preserve facts, improve presentation and internal links.

Develop an AI process for updating imported Concepts

## The task becomes one of the main next steps

The technical redesign of the website has already been completed, so further gains are expected primarily from systematic work with the content. Following the import, old materials should be treated as a source factual base rather than the final version of the pages.

An AI-driven pipeline is needed for the mass processing of Concepts: improving structure, formatting, clarity, internal linking, and the overall usefulness of the materials. This is especially important now, when the primary goal of the project is to restore search visibility and test whether the updated website is capable of receiving organic traffic again.

Worklog for task "Develop an AI process for updating imported Concepts"

The task becomes one of the main next steps

Task: Develop an AI process for updating imported Concepts